DESC:SYSTEM AND METHOD FOR DIGITAL SIGNING OF CONTRACT DOCUMENTS

FIELD OF THE PRESENT DISCLOSURE
[0001] The present disclosure relates generally to the field of electronic and digital signature technologies, document management, and contract generation. More specifically, the present disclosure pertains to a platform that facilitates secure, user-friendly, and efficient electronic and digital signatures for contract documents.

BACKGROUND
[0002] Traditional paper-based document signing remains time-consuming, particularly when multiple parties are involved. Additionally, the inefficiencies associated with paper-based signing can lead to errors, lost or misplaced documents, and delays in the signing process. Moreover, geographical constraints require all parties to be physically present, which can be challenging for international agreements. The environmental impact of traditional paper-based document signing is also a concern due to the use of paper resources. The digital revolution has significantly transformed the way businesses and individuals handle document signing and management. Electronic signatures and digital signature platforms have become increasingly popular due to their ability to facilitate efficient, secure, and environmentally friendly transactions. With the world shifting towards a paperless system, the importance of platforms that offer swift, secure, and user-friendly solutions for electronic document signing cannot be overstated. In the current technological landscape, digital signature systems are essential for facilitating the electronic signing of documents across various industries. These systems enable users to digitally sign and manage documents, such as contracts, from remote locations, which is particularly useful in today’s increasingly digital and globalized business environment. Typical systems provide functionalities such as uploading documents, selecting templates, and using basic digital signing features.
[0003] Despite the advancements in electronic and digital signature technologies, several limitations still persist in the current state of the art. Known electronic signature and digital signature platforms offer electronic and digital signatures to eliminate the need for physical presence, paper, and ink, making the process more efficient, secure, and environmentally friendly. Furthermore, they provide document management and storage features that enable users to upload, store, and manage their documents on the platform, simplifying organization and tracking of document signing processes. However, existing electronic and digital signature platforms still have some limitations that need to be addressed. These include the inefficiencies in handling complex document workflows, especially when documents require signatures from multiple parties who may also need to sign in a specified order. Additionally, the identification and authentication of signature pages within a document can be error-prone and cumbersome, particularly when dealing with a large volume of pages or documents. Also, secure access to document links for signatures through social media applications is generally not available directly, limiting their ease of use and accessibility.
[0004] Therefore, in light of the foregoing discussion, there exists a need for an improved digital signature solution that leverages advanced AI and machine learning technologies to address the limitations of current systems. Such solution should be capable of intelligently handling and automating complex document workflows, including the accurate classification of pages for signatures and the precise detection of signature locations. Such solution should also provide security features for document access, ensuring a secure and efficient document signing experience. The present disclosure aims to fulfil this need by providing a system and a method for digital signing of contract documents that enhance the functionality and efficiency of digital signature process.

SUMMARY
[0005] In an aspect of the present disclosure, a system for digital signing of contract documents is provided. The system comprises one or more processors, and a memory storing instructions that, when executed by the one or more processors, cause the system to: provide a user interface configured to receive input documents for signing by uploading user documents, selecting pre-defined templates, or generating custom documents based on user keywords utilizing an artificial intelligence (AI) document editor; assign recipients to the input documents and define roles for the recipients including one or more of signers, witnesses, consenting parties, approvers, and reviewers; define a signature workflow specifying a sequence for the recipients to sign, the signature workflow being one of a sequential order or a parallel order; employ a two-stage computer vision algorithm to: classify pages of the input documents into signature pages and non-signature pages using a machine learning image classification model, and detect signature locations on the classified signature pages using a machine learning object detection model; and automatically affix signatures, seals, stamps, or photos provided by the assigned recipients at the detected signature locations based on the defined signature workflow.
[0006] In one or more embodiments, the user interface is further configured to allow uploading of the input documents from one or more of: a local storage device, a cloud storage service, a document management system, or a network drive.
[0007] In one or more embodiments, the AI document editor comprises a large language model trained on a corpus of contract documents to generate the custom documents based on the user keywords and provide text completion suggestions.
[0008] In one or more embodiments, the user interface further provides options to manually specify locations on the input documents for affixing the signatures, seals, stamps, or photos.
[0009] In one or more embodiments, the machine learning image classification model is a convolutional neural network pretrained on a dataset of signature and non-signature page images.
[0010] In one or more embodiments, the machine learning object detection model utilizes a region-based convolutional neural network approach to localize the signature locations within the classified signature pages.
[0011] In one or more embodiments, the memory further stores instructions that, when executed, cause the system to integrate with one or more cloud storage platforms to directly receive the input documents.
[0012] In one or more embodiments, the memory further stores instructions that, when executed, cause the system to: securely distribute encrypted document links to the assigned recipients through one or more communication channels for accessing the input documents with affixed signatures; and authenticate recipient access by one of: one-time passwords or biometric authentication mechanisms.
[0013] In one or more embodiments, the memory further stores instructions that, when executed, cause the system to attach digital stamp papers to the input documents at predefined locations based on the document type.
[0014] In one or more embodiments, the user interface provides a centralized dashboard to manage and track progress of the signature workflow across multiple input documents.
[0015] In another aspect of the present disclosure, a computer-implemented method for digital signing of contract documents is provided. The method comprises providing, via a user interface, an option to receive input documents for signing by uploading user documents, selecting pre-defined templates, or generating custom documents based on user keywords utilizing an artificial intelligence (AI) document editor. The method further comprises assigning recipients to the input documents and defining roles for the recipients including one or more of signers, witnesses, consenting parties, approvers, and reviewers. The method further comprises defining a signature workflow specifying a sequence for the recipients to sign, the signature workflow being one of a sequential order or a parallel order. The method further comprises employing a two-stage computer vision algorithm to: classify pages of the input documents into signature pages and non-signature pages using a machine learning image classification model, and detect signature locations on the classified signature pages using a machine learning object detection model. The method further comprises automatically affixing signatures, seals, stamps, or photos provided by the assigned recipients at the detected signature locations based on the defined signature workflow.
[0016] In one or more embodiments, the method further comprises allowing uploading of the input documents from one or more of: a local storage device, a cloud storage service, a document management system, or a network drive via the user interface.
[0017] In one or more embodiments, the AI document editor comprises a large language model trained on a corpus of contract documents to generate the custom documents based on the user keywords and provide text completion suggestions.
[0018] In one or more embodiments, the method further comprises providing, via the user interface, options to manually specify locations on the input documents for affixing the signatures, seals, stamps, or photos.
[0019] In one or more embodiments, the machine learning image classification model is a convolutional neural network pretrained on a dataset of signature and non-signature page images.
[0020] In one or more embodiments, the machine learning object detection model utilizes a region-based convolutional neural network approach to localize the signature locations within the classified signature pages.
[0021] In one or more embodiments, the method further comprises integrating with one or more cloud storage platforms to directly receive the input documents.
[0022] In one or more embodiments, the method further comprises securely distributing encrypted document links to the assigned recipients through one or more communication channels for accessing the input documents with affixed signatures; and authenticating recipient access by one of: one-time passwords or biometric authentication mechanisms.
[0023] In one or more embodiments, the method further comprises attaching digital stamp papers to the input documents at predefined locations based on the document type.
[0024] In one or more embodiments, the method further comprises providing, via the user interface, a centralized dashboard to manage and track progress of the signature workflow across multiple input documents.
[0025] In yet another aspect of the present disclosure, a computer program product, having machine-readable instructions stored therein, is provided that when executed by the one or more processing units, causes the one or more processing units to perform aforementioned method steps.
[0026] Still, other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details may be modified in various obvious respects, all without departing from the scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

BRIEF DESCRIPTION OF THE FIGURES
[0027] For a more complete understanding of example embodiments of the present disclosure, reference is now made to the following descriptions taken in connection with the accompanying drawings in which:
[0028] FIG. 1 illustrates a system that may reside on and may be executed by a computer, which may be connected to a network, in accordance with one or more exemplary embodiments of the present disclosure;
[0029] FIG. 2 illustrates a diagrammatic view of a server, in accordance with one or more exemplary embodiments of the present disclosure;
[0030] FIG. 3 illustrates a diagrammatic view of a user device, in accordance with one or more exemplary embodiments of the present disclosure;
[0031] FIG. 4A illustrates a flowchart of a process workflow for implementation of proposed platform for document signing, in accordance with one or more exemplary embodiments of the present disclosure;
[0032] FIG. 4B illustrates a process diagram for implementation of computer vision algorithm for automatic placement of signatures, in accordance with one or more exemplary embodiments of the present disclosure;
[0033] FIG. 5 illustrates a representative user interface for managing a contract document, in accordance with one or more exemplary embodiments of the present disclosure;
[0034] FIG. 6 illustrates an exemplary user interface for users to automatically set up signatures on the document, in accordance with one or more exemplary embodiments of the present disclosure;
[0035] FIG. 7 illustrates an exemplary user interface for users to automatically affix signatures, seals, or stamp pads on the document, in accordance with one or more exemplary embodiments of the present disclosure;
[0036] FIG. 8 illustrates an exemplary user interface for users to manually set up signatures and/or affix signatures, seals, or stamp pads on the document, in accordance with one or more exemplary embodiments of the present disclosure;
[0037] FIGS. 9-12 illustrate process diagrams for implementation of computer vision-based algorithms for signature identification, in accordance with one or more exemplary embodiments of the present disclosure;
[0038] FIG. 13 illustrates a flowchart of a process flow for signature identification using the computer-vision based algorithm, in accordance with one or more exemplary embodiments of the present disclosure;
[0039] FIGS. 14A-14C illustrate exemplary interfaces for implementation of an AI document editor, in accordance with one or more exemplary embodiments of the present disclosure;
[0040] FIG. 15 illustrates a flowchart of a process flow for generation of a document using the AI document editor, in accordance with one or more exemplary embodiments of the present disclosure; and
[0041] FIG. 16 illustrates a flowchart of a computer-implemented method for digital signing of contract documents, in accordance with one or more exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION
[0042] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure is not limited to these specific details.
[0043] Reference in this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. The appearance of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Further, the terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items. Moreover, various features are described which may be exhibited by some embodiments and not by others. Similarly, various requirements are described which may be requirements for some embodiments but not for other embodiments.
[0044] Furthermore, in the following detailed description of the present disclosure, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be understood that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to unnecessarily obscure aspects of the present disclosure.
[0045] Embodiments described herein may be discussed in the general context of computer-executable instructions residing on some form of computer-readable storage medium, such as program modules, executed by one or more computers or other devices. By way of example, and not limitation, computer-readable storage media may comprise non-transitory computer-readable storage media and communication media; non-transitory computer-readable media include all computer-readable media except for a transitory, propagating signal. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or distributed as desired in various embodiments.
[0046] Some portions of the detailed description that follows are presented and discussed in terms of a process or method. Although steps and sequencing thereof are disclosed in figures herein describing the operations of this method, such steps and sequencing are exemplary. Embodiments are well suited to performing various other steps or variations of the steps recited in the flowchart of the figure herein, and in a sequence other than that depicted and described herein. Some portions of the detailed descriptions that follow are presented in terms of procedures, logic blocks, processing, and other symbolic representations of operations on data bits within a computer memory. These descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. In the present application, a procedure, logic block, process, or the like, is conceived to be a self-consistent sequence of steps or instructions leading to a desired result. The steps are those utilizing physical manipulations of physical quantities. Usually, although not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated in a computer system. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as transactions, bits, values, elements, symbols, characters, samples, pixels, or the like.
[0047] In some implementations, any suitable computer usable or computer readable medium (or media) may be utilized. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer-usable, or computer-readable, storage medium (including a storage device associated with a computing device) may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable medium may include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fibre, a portable compact disc read-only memory (CD-ROM), an optical storage device, a digital versatile disk (DVD), a static random access memory (SRAM), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, a media such as those supporting the internet or an intranet, or a magnetic storage device. Note that the computer-usable or computer-readable medium could even be a suitable medium upon which the program is stored, scanned, compiled, interpreted, or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory. In the context of the present disclosure, a computer-usable or computer-readable, storage medium may be any tangible medium that can contain or store a program for use by or in connection with the instruction execution system, apparatus, or device.
[0048] In some implementations, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. In some implementations, such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. In some implementations, the computer readable program code may be transmitted using any appropriate medium, including but not limited to the internet, wireline, optical fibre cable, RF, etc. In some implementations, a computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
[0049] In some implementations, computer program code for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like. Java and all Java-based trademarks and logos are trademarks or registered trademarks of Oracle and/or its affiliates. However, the computer program code for carrying out operations of the present disclosure may also be written in conventional procedural programming languages, such as the “C” programming language, PASCAL, or similar programming languages, as well as in scripting languages such as JavaScript, PERL, or Python. In present implementations, the used language for training may be one of Python, Tensorflow, Bazel, C, C++. Further, decoder in user device (as will be discussed) may use C, C++ or any processor specific ISA. Furthermore, assembly code inside C/C++ may be utilized for specific operation. Also, ASR (automatic speech recognition) and G2P decoder along with entire user system can be run in embedded Linux (any distribution), Android, iOS, Windows, or the like, without any limitations. The program code may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user’s computer through a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the internet using an Internet Service Provider). In some implementations, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGAs) or other hardware accelerators, micro-controller units (MCUs), or programmable logic arrays (PLAs) may execute the computer readable program instructions/code by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
[0050] In some implementations, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus (systems), methods and computer program products according to various implementations of the present disclosure. Each block in the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, may represent a module, segment, or portion of code, which comprises one or more executable computer program instructions for implementing the specified logical function(s)/act(s). These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program instructions, which may execute via the processor of the computer or other programmable data processing apparatus, create the ability to implement one or more of the functions/acts specified in the flowchart and/or block diagram block or blocks or combinations thereof. It should be noted that, in some implementations, the functions noted in the block(s) may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
[0051] In some implementations, these computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks or combinations thereof.
[0052] In some implementations, the computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed (not necessarily in a particular order) on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions/acts (not necessarily in a particular order) specified in the flowchart and/or block diagram block or blocks or combinations thereof.
[0053] Referring to example implementation of FIG. 1, there is shown a system 100 that may reside on and may be executed by a computer (e.g., computer 12), which may be connected to a network (e.g., network 14) (e.g., the internet or a local area network). Examples of computer 12 may include, but are not limited to, a personal computer(s), a laptop computer(s), mobile computing device(s), a server computer, a series of server computers, a mainframe computer(s), or a computing cloud(s). In some implementations, each of the aforementioned may be generally described as a computing device. In certain implementations, a computing device may be a physical or virtual device. In many implementations, a computing device may be any device capable of performing operations, such as a dedicated processor, a portion of a processor, a virtual processor, a portion of a virtual processor, portion of a virtual device, or a virtual device. In some implementations, a processor may be a physical processor or a virtual processor. In some implementations, a virtual processor may correspond to one or more parts of one or more physical processors. In some implementations, the instructions/logic may be distributed and executed across one or more processors, virtual or physical, to execute the instructions/logic. Computer 12 may execute an operating system, for example, but not limited to, Microsoft Windows; Mac OS X; Red Hat Linux, or a custom operating system. (Microsoft and Windows are registered trademarks of Microsoft Corporation in the United States, other countries or both; Mac and OS X are registered trademarks of Apple Inc. in the United States, other countries or both; Red Hat is a registered trademark of Red Hat Corporation in the United States, other countries or both; and Linux is a registered trademark of Linus Torvalds in the United States, other countries or both).
[0054] In some implementations, the instruction sets and subroutines of system 100, which may be stored on storage device, such as storage device 16, coupled to computer 12, may be executed by one or more processors (not shown) and one or more memory architectures included within computer 12. In some implementations, storage device 16 may include but is not limited to: a hard disk drive; a flash drive, a tape drive; an optical drive; a RAID array (or other array); a random-access memory (RAM); and a read-only memory (ROM).
[0055] In some implementations, network 14 may be connected to one or more secondary networks (e.g., network 18), examples of which may include but are not limited to: a local area network; a wide area network; or an intranet, for example.
[0056] In some implementations, computer 12 may include a data store, such as a database (e.g., relational database, object-oriented database, triplestore database, etc.) and may be located within any suitable memory location, such as storage device 16 coupled to computer 12. In some implementations, data, metadata, information, etc. described throughout the present disclosure may be stored in the data store. In some implementations, computer 12 may utilize any known database management system such as, but not limited to, DB2, in order to provide multi-user access to one or more databases, such as the above noted relational database. In some implementations, the data store may also be a custom database, such as, for example, a flat file database or an XML database. In some implementations, any other form(s) of a data storage structure and/or organization may also be used. In some implementations, system 100 may be a component of the data store, a standalone application that interfaces with the above noted data store and/or an applet / application that is accessed via client applications 22, 24, 26, 28. In some implementations, the above noted data store may be, in whole or in part, distributed in a cloud computing topology. In this way, computer 12 and storage device 16 may refer to multiple devices, which may also be distributed throughout the network.
[0057] In some implementations, computer 12 may execute application 20 for document signing (as discussed later in more detail). In some implementations, system 100 and/or application 20 may be accessed via one or more of client applications 22, 24, 26, 28. In some implementations, system 100 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within application 20, a component of application 20, and/or one or more of client applications 22, 24, 26, 28. In some implementations, application 20 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within system 100, a component of system 100, and/or one or more of client applications 22, 24, 26, 28. In some implementations, one or more of client applications 22, 24, 26, 28 may be a standalone application, or may be an applet / application / script / extension that may interact with and/or be executed within and/or be a component of system 100 and/or application 20. Examples of client applications 22, 24, 26, 28 may include, but are not limited to, a standard and/or mobile web browser, an email application (e.g., an email client application), a textual and/or a graphical user interface, a customized web browser, a plugin, an Application Programming Interface (API), or a custom application. The instruction sets and subroutines of client applications 22, 24, 26, 28, which may be stored on storage devices 30, 32, 34, 36, coupled to user devices 38, 40, 42, 44, may be executed by one or more processors and one or more memory architectures incorporated into user devices 38, 40, 42, 44.
[0058] In some implementations, one or more of storage devices 30, 32, 34, 36, may include but are not limited to: hard disk drives; flash drives, tape drives; optical drives; RAID arrays; random access memories (RAM); and read-only memories (ROM). Examples of user devices 38, 40, 42, 44 (and/or computer 12) may include, but are not limited to, a personal computer (e.g., user device 38), a laptop computer (e.g., user device 40), a smart/data-enabled, cellular phone (e.g., user device 42), a notebook computer (e.g., user device 44), a tablet (not shown), a server (not shown), a television (not shown), a smart television (not shown), a media (e.g., video, photo, etc.) capturing device (not shown), and a dedicated network device (not shown). User devices 38, 40, 42, 44 may each execute an operating system, examples of which may include but are not limited to, Android, Apple iOS, Mac OS X; Red Hat Linux, or a custom operating system.
[0059] In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of system 100 (and vice versa). Accordingly, in some implementations, system 100 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or system 100.
[0060] In some implementations, one or more of client applications 22, 24, 26, 28 may be configured to effectuate some or all of the functionality of application 20 (and vice versa). Accordingly, in some implementations, application 20 may be a purely server-side application, a purely client-side application, or a hybrid server-side / client-side application that is cooperatively executed by one or more of client applications 22, 24, 26, 28 and/or application 20. As one or more of client applications 22, 24, 26, 28, system 100, and application 20, taken singly or in any combination, may effectuate some or all of the same functionality, any description of effectuating such functionality via one or more of client applications 22, 24, 26, 28, system 100, application 20, or combination thereof, and any described interaction(s) between one or more of client applications 22, 24, 26, 28, system 100, application 20, or combination thereof to effectuate such functionality, should be taken as an example only and not to limit the scope of the disclosure.
[0061] In some implementations, one or more of users 46, 48, 50, 52 may access computer 12 and system 100 (e.g., using one or more of user devices 38, 40, 42, 44) directly through network 14 or through secondary network 18. Further, computer 12 may be connected to network 14 through secondary network 18, as illustrated with phantom link line 54. System 100 may include one or more user interfaces, such as browsers and textual or graphical user interfaces, through which users 46, 48, 50, 52 may access system 100.
[0062] In some implementations, the various user devices may be directly or indirectly coupled to communication network, such as communication network 14 and communication network 18, hereinafter simply referred to as network 14 and network 18, respectively. For example, user device 38 is shown directly coupled to network 14 via a hardwired network connection. Further, user device 44 is shown directly coupled to network 18 via a hardwired network connection. User device 40 is shown wirelessly coupled to network 14 via wireless communication channel 56 established between user device 40 and wireless access point (i.e., WAP) 58, which is shown directly coupled to network 14. WAP 58 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, Wi-Fi, RFID, and/or Bluetooth (including Bluetooth Low Energy) device that is capable of establishing wireless communication channel 56 between user device 40 and WAP 58. User device 42 is shown wirelessly coupled to network 14 via wireless communication channel 60 established between user device 42 and cellular network / bridge 62, which is shown directly coupled to network 14.
[0063] In some implementations, some or all of the IEEE 802.11x specifications may use Ethernet protocol and carrier sense multiple access with collision avoidance (i.e., CSMA/CA) for path sharing. The various 802.11x specifications may use phase-shift keying (i.e., PSK) modulation or complementary code keying (i.e., CCK) modulation, for example, Bluetooth (including Bluetooth Low Energy) is a telecommunications industry specification that allows, e.g., mobile phones, computers, smart phones, and other electronic devices to be interconnected using a short-range wireless connection. Other forms of interconnection (e.g., Near Field Communication (NFC)) may also be used.
[0064] The system 100 may include a server (such as server 200, as shown in FIG. 2) for document signing. Herein, FIG. 2 is a block diagram of an example of the server 200 capable of implementing embodiments according to the present disclosure. In one embodiment, an application server as described herein may be implemented on exemplary server 200. In the example of FIG. 2, the server 200 includes a processing unit 205 (hereinafter, referred to as CPU 205) for running software applications (such as, the application 20 of FIG. 1) and optionally an operating system. As illustrated, the server 200 further includes a database 210 (hereinafter, referred to as memory 210) which stores applications and data for use by the CPU 205. Storage 215 provides non-volatile storage for applications and data and may include fixed disk drives, removable disk drives, flash memory devices, and CD-ROM, DVD-ROM or other optical storage devices. An optional user input device 220 includes devices that communicate user inputs from one or more users to the server 200 and may include keyboards, mice, joysticks, touch screens, etc. A communication or network interface 225 is provided which allows the server 200 to communicate with other computer systems via an electronic communications network, including wired and/or wireless communication and including an Intranet or the Internet. In one embodiment, the server 200 receives instructions and user inputs from a remote computer through communication interface 225. Communication interface 225 can comprise a transmitter and receiver for communicating with remote devices. An optional display device 250 may be provided which can be any device capable of displaying visual information in response to a signal from the server 200. The components of the server 200, including the CPU 205, memory 210, data storage 215, user input devices 220, communication interface 225, and the display device 250, may be coupled via one or more data buses 260.
[0065] In the embodiment of FIG. 2, a graphics system 230 may be coupled with the data bus 260 and the components of the server 200. The graphics system 230 may include a physical graphics processing unit (GPU) 235 and graphics memory. The GPU 235 generates pixel data for output images from rendering commands. The physical GPU 235 can be configured as multiple virtual GPUs that may be used in parallel (concurrently) by a number of applications or processes executing in parallel. For example, mass scaling processes for rigid bodies or a variety of constraint solving processes may be run in parallel on the multiple virtual GPUs. Graphics memory may include a display memory 240 (e.g., a framebuffer) used for storing pixel data for each pixel of an output image. In another embodiment, the display memory 240 and/or additional memory 245 may be part of the memory 210 and may be shared with the CPU 205. Alternatively, the display memory 240 and/or additional memory 245 can be one or more separate memories provided for the exclusive use of the graphics system 230. In another embodiment, graphics processing unit 230 includes one or more additional physical GPUs 255, similar to the GPU 235. Each additional GPU 255 may be adapted to operate in parallel with the GPU 235. Each additional GPU 255 generates pixel data for output images from rendering commands. Each additional physical GPU 255 can be configured as multiple virtual GPUs that may be used in parallel (concurrently) by a number of applications or processes executing in parallel, e.g., processes that solve constraints. Each additional GPU 255 can operate in conjunction with the GPU 235, for example, to simultaneously generate pixel data for different portions of an output image, or to simultaneously generate pixel data for different output images. Each additional GPU 255 can be located on the same circuit board as the GPU 235, sharing a connection with the GPU 235 to the data bus 260, or each additional GPU 255 can be located on another circuit board separately coupled with the data bus 260. Each additional GPU 255 can also be integrated into the same module or chip package as the GPU 235. Each additional GPU 255 can have additional memory, similar to the display memory 240 and additional memory 245, or can share the memories 240 and 245 with the GPU 235. It is to be understood that the circuits and/or functionality of GPU as described herein could also be implemented in other types of processors, such as general-purpose or other special-purpose coprocessors, or within a CPU.
[0066] The system 100 may also include a user device 300 (as shown in FIG. 3). In embodiments of the present disclosure, the user device 300 may embody a smartphone, a personal computer, a tablet, or the like. Herein, FIG. 3 is a block diagram of an example of the user device 300 capable of implementing embodiments according to the present disclosure. In the example of FIG. 3, the user device 300 includes a processing unit 305 (hereinafter, referred to as CPU 305) for running software applications (such as, the application 20 of FIG. 1) and optionally an operating system. A user input device 320 is provided which includes devices that communicate user inputs from one or more users and may include keyboards, mice, joysticks, touch screens, and/or microphones. Further, a network interface 325 is provided which allows the user device 300 to communicate with other computer systems (e.g., the server 200 of FIG. 2) via an electronic communications network, including wired and/or wireless communication and including the Internet. The user device 300 may also include a decoder 355 may be any device capable of decoding (decompressing) data that may be encoded (compressed). A display device 350 may be provided which may be any device capable of displaying visual information, including information received from the decoder 355. In particular, as will be described below, the display device 350 may be used to display visual information received from the server 200 of FIG. 2. The components of the user device 300 may be coupled via one or more data buses 360.
[0067] The system 100 of the present disclosure provides a versatile and comprehensive solution for document signing, combining advanced features and technologies to provide an improved user experience and address the limitations of traditional document signing methods and existing e-signature platforms. The system 100 presents a robust solution for document execution, leveraging an array of interconnected features and components. Each element contributes to the overall efficacy of the system 100, combining to deliver a secure, efficient, and user-friendly solution.
[0068] The system 100 of the present disclosure provides a platform (with the two terms being interchangeably used hereinafter) which is an end-to-end solution for document creation, signing, and management, leveraging advanced technologies for enhanced user experience and efficiency. The system 100 facilitates the creation of custom contracts through an artificial intelligence (AI) based document editor (AI document editor), where users can generate documents based on specific keywords. The system 100 enables users to upload, manage, and sign documents electronically, eliminating geographical constraints and environmental impact associated with traditional paper-based signing. The system 100 employs a computer vision algorithm to detect signature locations automatically, increasing the accuracy and reducing manual effort. The system 100 also incorporates robust security features such as OTP, photo-KYC, and multi-factor authentication to ensure the secure execution of documents. Furthermore, the system 100 integrates with popular social media messaging systems, thereby making document sharing and signing more accessible. Thus, the system 100 streamlines the process of document creation, signing, and management while providing a secure, user-friendly, and efficient environment, overcoming the limitations of existing e-signature platforms.
[0069] The initial stage involves the input of documents into the system 100. This feature provides users with multiple options for document input, enabling the uploading of existing documents, the selection from a library of predefined templates, or the creation of unique, custom documents using the integrated AI document editor. This feature ensures the system 100 is adaptable to a variety of document execution requirements. An integral component of the system 100 is the AI document editor. This generative tool allows users to create custom contracts by specifying relevant keywords. The AI document editor aligns with the document input feature, providing an alternative method for generating documents that are ready for signature.
[0070] Upon the introduction of documents into the system 100, the system 100 facilitates the setup of documents and recipients. This component enables users to prepare single or multiple documents for signature and to assign recipients with specific roles such as signer, witness, consenting party, approver, or reviewer. This preparation stage lays the groundwork for the forthcoming signing workflow. The signature workflow feature in the system 100 ensures an efficient signing process. It allows users to define a workflow that determines the sequence of the signature process, be it sequential or parallel. This feature works in concert with the document and recipient setup, outlining the signing process based on the assigned roles and user preferences. The system 100 provides various options for the attachment of signatures. Users can affix their signatures, seals, stamp pads, or photographs using “Automagic” option, Presets option, or Manual mode option. These options collaborate with the signature workflow and document setup features, providing a personalized and efficient signing experience.
[0071] Security and authentication are critical aspects of the system 100. Access to documents is securely controlled through email or social media messaging systems, with authentication conducted using OTP or biometric recognition facilities. This security measure ensures that only authorized recipients can access and sign the documents, integrating with the signature workflow to maintain document integrity. A distinctive feature of the system 100 is the computer vision-based signature identification algorithms. This two-step algorithm classifies pages as signature or non-signature pages and identifies signature locations. This feature synergizes with the signature attachment process, particularly the automatic option, to automate the placement of signatures in the appropriate locations within the document.
[0072] These interconnected features and components of the system 100 work in unison to streamline the document signing process. The system 100 addresses common issues associated with traditional paper-based signing procedures and also overcomes limitations in existing e-signature solutions, offering a comprehensive and efficient solution for executing documents using electronic and digital signatures.
[0073] Referring to FIG. 4A, illustrated is a flowchart of a process workflow (as represented by reference numeral 400A) providing details for implementation of the present system 100 (hereinafter, generally, and interchangeably, referred to as “the platform” or “the digital signature platform,” without any limitations). As illustrated, the process workflow 400A may include following steps:
Signup: In the context of the system 100 for digital signing of contract documents, the process begins with the signup phase where users initiate their interaction with the system 100 by registering on the digital signature platform. During the signup, the system 100 requires users to provide basic personal information, which typically includes an email address and/or phone number. This information is used for creating a unique user profile within the system 100. Additionally, the system 100 offers the capability to integrate with social login options, such as Google or Facebook, which simplifies the registration process by allowing users to sign in using existing social media accounts. This integration enhances user convenience and also aids in quick profile creation while maintaining the security integrity of user data.
Profile Setup: Following the signup, the system 100 transitions users to the profile setup phase. In this phase, the system 100 prompts users to enter further personal details, which are used for customizing the user experience on the digital signature platform. During profile setup, users are also required to provide signature samples, which are used by the system 100 to authenticate the identity of the user in future document signings. Moreover, users select their preferred security measures from options such as one-time passwords (OTP) and/or biometric recognition. These security measures ensure that access to the digital signing functionalities and the execution of contract documents are securely managed, protecting sensitive user information and document integrity.
Success: Upon successful completion of the profile setup, the system 100 generates a success notification. This success notification serves as a confirmation that the user’s profile has been successfully established and is now active. This assurance is significant as it informs users that they have completed the necessary steps to begin utilizing the comprehensive services offered by the digital signature platform. The success notification is an important element in the process, confirming to users that they are prepared to proceed with the secure and efficient digital signing of documents.
Platform: The platform, as described in the system 100 for digital signing of contract documents, serves as the central interface through which users can access a comprehensive suite of features necessary for the digital document signing process. This platform is specifically designed to be user-friendly and intuitive, facilitating an efficient and straightforward document management experience. Within this platform, users are provided with multiple functionalities that are integral to the handling of digital signatures on contract documents. The platform provides a user interface configured to receive input documents for signing by uploading user documents, selecting pre-defined templates, or generating custom documents based on user keywords utilizing an artificial intelligence (AI) document editor. That is, by using the platform, users can upload or create documents, assign recipients, select signature types, set up workflows, and initiate the signing process. The platform integrates various advanced technological features to ensure that the process of document management and digital signing is as seamless and efficient as possible, providing a comprehensive infrastructure for secure and effective digital transactions.
Input Document: At the input document stage, the system 100 for digital signing of contract documents provides users with multiple methods to input documents for processing, enhancing flexibility and catering to diverse user needs. Users can choose from three primary methods to input documents into the system 100:
(a) Upload document: Users have the option to upload documents in various formats, including .doc, .pdf, or .odt. This capability ensures that users can work with documents in formats that they are already using without needing to convert these documents to a different format before uploading.
(b) Choose a template: The platform offers legally vetted custom templates for various common document types. This feature is particularly beneficial for users who need to create standard contractual documents but may not have the legal expertise to draft them from scratch. By selecting from these pre-defined templates, users can ensure compliance with legal standards while streamlining the document creation process.
(c) Create a custom document: The AI document editor of the platform allows users to generate custom contracts by specifying certain keywords. This advanced feature leverages a large language model trained on a corpus of contract documents, enabling the AI document editor to produce documents that are tailored to the specific needs of the users based on the inputted keywords. This method is especially useful for creating customized documents that require specific legal terminology or clauses that are not covered by standard templates.
Upload single or multiple documents: The system 100, or specifically the user interface therein, is configured to allow uploading of the input documents from one or more of: a local storage device, a cloud storage service, a document management system, or a network drive. Herein, the system 100 supports the upload of single or multiple documents simultaneously, offering significant flexibility and efficiency. Users can upload these documents from various sources, including local storage devices or popular cloud storage services such as One Drive, Google Drive, Dropbox, or Box. This feature caters to individual user preferences and also accommodates different organizational needs, allowing for the bulk handling of documents which is particularly useful in environments where high volumes of contracts need to be processed regularly. These uploaded documents can either form the basis of the contract document itself or can be appended to the inputted contract document as required, providing further adaptability in how the contract documents are handled and processed within the system 100.
Assign recipients and roles: In the system 100 for digital signing of contract documents, after the documents are inputted, the next step involves assigning recipients to these documents and defining their respective roles within the signing process. This functionality ensures that all necessary parties are correctly identified and that their roles are clearly established, facilitating a structured and legally binding signing process. In present embodiments, the platform allows users to assign multiple recipients to a document and explicitly define their roles, such as signer, witness, or consenting party. Each role carries specific responsibilities and legal implications:
(a) Signer: Signers are the primary actors who are required to provide their signatures on the document to signify agreement or acknowledgment of the document contents.
(b) Witness: Witnesses are assigned to observe the signing process by signers and provide an additional layer of authenticity and verification. Their involvement is required in certain legal contexts where witness verification is necessary to uphold the validity of a document.
(c) Consenting party: Consenting parties might not directly sign the document but must give their consent on the proceedings or terms laid out in the document, which may affect or involve them directly.
(d) Approvers: Approvers are individuals who have the authority to give final approval on documents before they are sent out for signing or executed. This ensures that the contents of the document meet all required criteria and company standards before moving forward in the process.
(e) Reviewers: Reviewers are tasked with examining the document's content thoroughly but may not have the authority to make final decisions on their execution. Their primary role is to provide feedback and suggest modifications to ensure the document is correct and meets the intended purpose.
By enabling users to assign these roles through the platform, the system 100 ensures that each document signing scenario is catered to according to specific requirements and legal standards. This assignment process is integral to organizing the workflow of document signing, especially in complex scenarios involving multiple stakeholders with different roles. Furthermore, the ability of the platform to handle this role assignment electronically simplifies a complex administrative task. This digital facilitation speeds up the process and also reduces the potential for errors that may occur in a manual handling environment, thereby enhancing the efficiency and reliability of the document signing process.
Select signature type: The system 100 for digital signing of contract documents further provides the ability for users to select the type of signature that will be applied to their documents. This selection process directly impacts the legal and security aspects of the signed documents. The system 100 offers users two primary types of signatures: an electronic signature (e-signature) and an Aadhaar-based digital signature. Each signature type caters to different user needs and compliance requirements, ensuring flexibility and security in the document signing process. The electronic signature, commonly referred to as an e-signature, is a widely accepted form of signature that can be used in various legal contexts. This type of signature is typically issued by a Certifying Authority (CA) which guarantees the authenticity and integrity of the signature. Electronic signatures are used to confirm consent or approval on electronic documents and forms, making them suitable for a vast array of transactions and agreements. On the other hand, an Aadhaar-based digital signature incorporates a higher level of security by linking the signature to the Aadhaar identity number of the signer, which is a unique identifier assigned to residents of India. This type of signature provides stronger authentication because it is based on biometric and demographic data stored in the Aadhaar database. Aadhaar-based digital signatures are particularly useful in contexts requiring stringent security measures and are pivotal in ensuring compliance with certain regulatory requirements in India. By providing these options, the system 100 allows users to tailor the signing process to meet specific legal standards and security needs. Users can choose the appropriate signature type based on the document’s significance, the required level of security, and the geographical and regulatory environment in which the document will be used.
Setup signature workflow: The system 100 further allows to define a signature workflow specifying a sequence for the recipients to sign. In the system 100 for digital signing of contract documents, setting up the signature workflow ensures the orderly execution of the signing process according to the specific needs of the document and the parties involved. This feature allows users to define how signatures are collected, providing significant flexibility and control over the signing sequence. The system 100 offers two main types of signature workflows: sequential and parallel.
(a) Sequential: In a sequential signature workflow, the system 100 specifies that parties sign the document in a specific predetermined order. This method is particularly useful for documents where the order of signatures is important, such as in legal or financial documents where the sequence may hold procedural or symbolic significance. For example, a sequential workflow might require a junior executive to sign before a senior executive approves and finalizes the document.
(b) Parallel: Conversely, a parallel signature workflow allows all parties to sign simultaneously, without a predefined order. This approach is advantageous for documents where the sequence of signatures does not affect the document’s validity or where expediency is critical. It is especially beneficial in scenarios involving multiple stakeholders who may be geographically dispersed, as it significantly speeds up the process by allowing concurrent signing actions.
By enabling the setup of these distinct signature workflows, the system 100 caters to a wide range of document types and signing requirements, enhancing the platform’s utility across various business contexts. This adaptability allows for managing complex transactions and agreements, ensuring that the signature process aligns perfectly with the operational and legal requirements of the organizations using the system 100.
Attach signatures, seals, stamp pads or photos: The system 100 further allows to automatically affix signatures, seals, stamps, or photos provided by the assigned recipients at the detected signature locations based on the defined signature workflow. In the system 100 for digital signing of contract documents, such ability to attach signatures, seals, stamp pads, or photos to the documents enhances the authenticity and legal standing of the signed documents. The documents can be signed either by scanning a QR code or uploading an image or by scribbling on the pad. The users may also affix a stored signature from the database by providing using biometric methods like finger print or face recognition on their phones or tablets or laptops, if those features exist. In general, the system 100 provides users with multiple options to affix these elements to the documents, ensuring flexibility and accuracy in the placement of such items according to the specific requirements of the document or the preferences of the parties involved, including:
(a) Automatic: The primary option implemented by the system 100 is the automatic detection and placement of signatures and other elements using artificial intelligence (AI). The system 100 employs a two-stage computer vision algorithm where the first stage involves classifying pages of the input documents into signature and non-signature pages using a machine learning image classification model. In the second stage, a machine learning object detection model detects the precise locations for signatures on the classified signature pages. This machine learning approach allows for the automatic placement of signatures, seals, or photos at the appropriate locations (all pages or on a single page) on the document, significantly reducing manual effort and increasing processing efficiency.
(b) Presets: The system 100 provides another option to users involving the use of presets. This feature allows users to set default positions for their signatures, seals, stamp pads, or photos on the documents. Presets are particularly useful in standardized document processes where the placement of these elements does not vary between documents. By using presets, users can streamline the attachment process, ensuring consistency and speed in handling multiple documents.
(c) Manual mode: The system 100 also offers a manual mode, which gives users the ability to manually select the location for placing their signatures, seals, stamp pads, or photos. This mode provides the highest level of control over the placement of these elements, allowing users to cater to specific layout requirements or personal preferences. Manual mode is especially beneficial in scenarios where unique document formatting or special considerations are necessary.
Launch workflow and send documents for signatures: Once all the necessary settings, including document uploads, recipient assignments, role definitions, and attachment of signatures or other elements, are confirmed and in place, users can initiate the signing process. This step is facilitated by a user-friendly interface within the system 100 that allows for the simple launch of the defined workflow. When the workflow is launched, the system 100 automatically distributes the documents to the assigned recipients according to the signature workflow previously set up, which could be either sequential or parallel, depending on the document’s requirements. This distribution is managed by backend processes, which ensure that each recipient receives the correct document at the correct time, guided by the workflow parameters. Furthermore, the system 100 provides features for tracking the progress of the signing process. Users can monitor real-time updates on the status of each document through a centralized dashboard provided by the platform. This tracking feature helps in maintaining oversight of the process and ensures that users are kept informed of each step as the document moves through the signing sequence, by allowing users to see which recipients have completed their signatures, which documents are pending, and any delays or issues that may arise during the process.
Secure access to documents: Once the workflow is launched and documents are sent out for signatures, the system 100 ensures that access to these documents is tightly controlled and secure. The system 100 utilizes a secure mechanism to distribute access to the documents via (i) email, and/or (ii) social media messaging systems such as WhatsApp. This is accomplished by sending a secure link to the document rather than the document itself. The secure link is generated by the system 100 and is unique to each recipient and document, which significantly mitigates the risk of unauthorized access. The use of secure links ensures that only intended recipients, who have been authenticated and authorized to view and sign the document, can access it. This approach leverages the existing security protocols of email and social media platforms, enhancing the overall security posture without compromising user convenience. Furthermore, the system 100 maintains high standards of data privacy and protection. This is achieved through encryption of the document links and the secure storage of documents within the system 100. Encryption ensures that any data intercepted during transmission remains unreadable and secure from potential breaches.
Authenticate access using OTP or biometric recognition: When a recipient receives a secure link to a document, the system 100 requires them to validate their identity before granting access to the document. The system 100 incorporates advanced authentication methods, specifically (i) OTP (One Time Password), and/or (ii) biometric recognition, to authenticate user access. For OTP authentication, the system 100 generates a unique, time-sensitive code that is sent to the recipient’s registered email address or mobile number. The recipient must enter this OTP into the system 100 to verify their identity. This method ensures that access is granted only to recipients who have the OTP, which acts as a strong, dynamic password. Biometric recognition, as a method of authentication, can include fingerprint scanning, facial recognition, or iris scanning, depending on the capabilities of the user’s device and the level of security required. This method is particularly useful in environments where extra security measures are required, and it also provides a quick and user-friendly way to authenticate identity without the need to remember passwords or codes. This additional layer of security ensures that only authorized individuals can access and interact with the documents.
[0074] Thus, the process workflow 400A provides a comprehensive and secure document signing process that is designed to be swift, efficient, and user-friendly. The advanced features provided by the system 100, such as the AI document editor and computer vision algorithm for signature placement, provide a unique and enhanced user experience. It may be appreciated that the above described steps for the process workflow 400A are non-limiting and are only illustrative, and other alternatives can also be provided where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the spirit and the scope of the present disclosure.
[0075] Referring now to FIG. 4B, illustrated is a process diagram (as represented by reference numeral 400B) for implementation of computer vision algorithm for automatic placement of signatures, in accordance with one or more exemplary embodiments of the present disclosure. The process diagram 400B illustrates the application of a two-stage computer vision algorithm within the digital signing platform, as deployed in the system 100 for digital signing of contract documents. This algorithm enhances capability of the system 100 to efficiently process and prepare documents for the signing process.
[0076] In the first stage of the computer vision algorithm (depicted as ‘Image Classification’ in the process diagram 400B), the system 100 utilizes a machine learning image classification model to differentiate between signature pages and non-signature pages within the input documents. This classification ensures that subsequent operations related to signature placement are only performed on the appropriate pages. The machine learning model typically used for this task is a convolutional neural network (CNN), which is well-suited for image analysis due to its ability to process pixel data and recognize patterns effectively. The CNN employed here is trained on a diverse set of document images, which enables it to accurately recognize and categorize pages that need signatures based on learned features from various document formats and layouts. This training involves exposing the CNN to numerous examples of both signature and non-signature pages, allowing it to learn the distinguishing features autonomously. This automated classification by the machine learning image classification model helps in streamlining the workflow within the digital signing platform. By automating the identification of signature pages, the system 100 reduces the dependency on manual review, which is prone to human error and can be labour-intensive, especially when dealing with large volumes of documents. Such use of advanced image classification optimizes the workflow, and enhances the reliability and accuracy of the document preparation process for digital signatures.
[0077] Once the signature pages have been identified in the initial stage of the computer vision algorithm, the system 100 for digital signing of contract documents progresses to the second stage (depicted as ‘Signature Box Detection’ in the process diagram 400B). This stage provides for the precise execution of digital signatures, as it involves locating the specific areas on the signature pages where signatures and other authorization marks are required. During this phase, the system 100 employs a machine learning object detection model that is particularly adept at recognizing and delineating signature boxes on the document pages. The technology typically used for this task is likely to involve region-based convolutional neural networks (R-CNNs). These networks are highly effective in object detection tasks because they combine the strengths of CNNs with region proposal algorithms to accurately identify and localize objects within images. In the context of signature box detection, R-CNNs analyse the features of the identified signature pages to pinpoint where signatures or seals should be precisely placed. This object detection capability allows for automating the placement of signatures in the exact locations on the documents. This automated precision significantly reduces the likelihood of errors that could arise from manual placement and ensures that the documents are executed correctly in accordance with legal and procedural requirements. By ensuring that signatures and authorization marks are accurately placed according to the predefined regions identified by the object detection model, the system 100 upholds the legal compliance required for contract documents and maintains the overall integrity of the signing process.
[0078] By integrating these two stages of computer vision, i.e., the image classification followed by signature box detection, the system 100 significantly streamlines the document preparation process for signing. This enhances the efficiency of the document signing process and also improves accuracy, reducing the likelihood of errors that could potentially compromise the validity of signed documents. Moreover, the automated nature of this process supports scalability, allowing the system 100 to handle large volumes of documents in a consistent and error-free manner.
[0079] Referring to FIG. 5, illustrated is a representative user interface (as represented by reference numeral 500) for managing a contract document, in accordance with one or more exemplary embodiments of the present disclosure. Herein, the user interface 500 provides a dashboard for managing the contract document. The user interface 500, as provided, may be implemented in the user device 300 as described. The user interface 500 of the present disclosure provides an intuitive and efficient method for users to handle document execution. Upon logging into the system 100, the user interface 500, via a first panel 502, present the user with three options for document execution: (a) Uploading documents in doc, pdf, or .odt formats, (b) Choosing from a list of legally vetted custom templates, or (c) Creating their own documents using a Generative AI document editor. These options are clearly displayed on the user interface 500, allowing users to easily select the most suitable method for their needs.
[0080] For users who choose to upload their documents, the user interface 500 provides an upload feature that supports single or multiple document uploads. Users can select multiple files from their local machine or upload a zip file. In addition, the system 100 integrates with popular cloud storage services such as One Drive, Google Drive, Dropbox, and Box, allowing users to upload documents directly from these sources. The users who need to create standard contractual documents but may not have the legal expertise to draft them from scratch can use available templates. By selecting from these pre-defined templates, the users can ensure compliance with legal standards while streamlining the document creation process. Once the documents are uploaded/finalized, they are displayed on the system 100 via the user interface 500, and the users can proceed with the signing process.
[0081] The present system 100 further provides another option to the user in the form of an AI document editor to create a custom document. In the user interface 500, the selection of the AI document editor option opens a new page with an intuitive text editor interface, as discussed later in reference to FIGS. 14A-14C. The AI document editor uses a generative algorithm based on a Large Language Model (LLM), which could be an off-the-shelf solution or a custom-developed model trained on a comprehensive dataset of legal documents. The AI document editor includes built-in word formatting features, either from an off-the-shelf solution or a custom-built application, which users can utilize to tailor the document to their needs. A field is provided on the page where users can input specific keywords, which the AI document editor then uses to generate a custom contract.
[0082] The AI document editor within the system 100 for digital signing of contract documents leverages a large language model to enhance the document creation process. This model is trained on an extensive corpus of contract documents, enabling it to understand and generate text that is legally coherent and contextually appropriate for various types of contracts. By training the model on a broad range of contract-related documents, it gains an understanding of legal terminology, clauses, and document formatting that are specific to contractual agreements. When users input specific keywords or phrases related to their document needs, the AI document editor utilizes the large language model to interpret these inputs and generate custom documents that align with the users’ requirements. This capability is particularly valuable in drafting contract documents that must meet precise legal standards and specific contractual obligations without the need for drafting from scratch. Moreover, the AI document editor provides text completion suggestions, enhancing user efficiency and accuracy when creating documents. As users begin to type or input content into the document, the AI document editor offers predictive text options that are contextually aligned with the rest of the document’s content, drawn from its training on the comprehensive contract document corpus. This feature speeds up the document creation process and helps ensure that the legal language used is consistent and accurate, minimizing the risk of errors and the potential legal ambiguities that could arise from improper terminology or phrasing. Such integration of a trained large language model into the AI document editor enables the system 100 to assist users in creating highly customized, legally correct contracts efficiently and with greater reliability.
[0083] Once the documents are ready for execution, the user interface 500 further allows the users to set up the documents for signatures, via a second panel 504, by assigning recipients and their respective roles. These roles could include signer, witness, consenting party, approver, reviewer, etc. The system 100 also allows the selection of signature type, such as e-signature or Aadhaar-based digital signature. This information can be manually entered by the user or populated from an attached contact book. The setup process can be done for each document individually or for multiple documents simultaneously.
[0084] After setting up the recipients, users can define the signature workflow, again via the second panel 504 of the user interface 500. The workflow indicates the sequence in which the signatures should be obtained, either sequentially or in parallel. This feature adds an extra layer of flexibility and control for the users, allowing them to customize the signing process to best fit their specific needs. This setup is intuitively integrated into the user interface 500, ensuring a seamless user experience.
[0085] Following the setup of signature workflows, the user interface 500 allows the users to proceed to attach signatures, seals, stamp pads, or photographs. The user interface 500 offers multiple options for this process, via a third panel 506, each designed to cater to different user needs and preferences. These options can be selected, unselected, or used in combination with each other, ensuring a high level of flexibility for the users.
[0086] In an example, as illustrated in an exemplary interface 600 of FIG. 6, the system 100 provides an “Automagic” feature which enables users to set up signatures on all pages of the document, including the signature page. The system 100 employs an underlying AI algorithm that uses computer vision techniques (as discussed later in more detail) to identify the location and signatories. The algorithm leverages object detection or similar techniques to determine the best location for the signature, creating a bounding box around this area. The signatory’s signature is then automatically affixed at this location, eliminating the need for manual placement and ensuring accurate and efficient document execution. It may be seen that the user interface 600 may use a tabbed structure for ease of navigation through multiple documents.
[0087] Further, in an example, as illustrated in an exemplary interface 700 of FIG. 7, the system 100 provides ‘Presets’ option which allows users to affix signatures, seals, or stamp pads as per their requirements, either on a specific page or on all pages of the document. Typically, the location of these elements is at the bottom of the page, but the system 100 allows users to choose a custom orientation based on their specific needs. The underlying algorithm automatically calculates the positions of signatures for the signatories and places the placeholders accordingly. This feature enhances the user experience by streamlining the signing process and reducing the need for manual adjustments.
[0088] Furthermore, in an example, as illustrated in an exemplary interface 800 of FIG. 8, the system 100 offers a ‘Manual’ mode. In this mode, the user interface 800 provides options to manually specify locations on the input documents for affixing the signatures, seals, stamps, or photos. That is, the users can manually specify the location of the signature, seal, or stamp pad for each signatory. They can do this by clicking on these placeholders and dragging them to the desired location. This option provides the highest level of control and customization, ensuring that users can tailor the document execution process to their exact specifications.
[0089] Referring back to FIG. 5, as illustrated, the user interface 500 also offers an option for users to attach digital stamp papers to their documents at predefined locations based on the document type. This option adds an extra layer of legality and authenticity to the signed documents, especially for certain legal documents that require stamp duty. Users can select the ‘Attach Stamp Paper’ option from the user interface 500. Once selected, the system 100 prompts the user to choose the value of the stamp paper as per the requirement of the document. Further, the system 100 automatically incorporates the digital stamp paper into the document in the correct sequence, ensuring legal compliance. For instance, in a contract, the stamp paper will be automatically placed at the beginning of the document. This feature simplifies the process of attaching stamp papers, eliminating manual efforts and reducing errors, thereby making the document execution process more seamless and efficient. This ensures that the final document is legally compliant and ready for execution, providing an all-in-one solution for the creation, customization, and execution of legal documents.
[0090] Further, once the document is prepared and all the necessary signatures, seals, stamp papers, and photographs have been affixed, the user interface 500 allows the user to proceed to send the documents for signatures via ‘Email Notification’ option or the like. In an example, upon selecting this option, the system 100 triggers the pre-defined workflow, which includes the order and method of signatures, as defined by the user during the setup phase. The documents are then sent to the designated recipients via their preferred method of communication, as set in the contact book.
[0091] In the present embodiments, the system 100 securely distributes encrypted document links to the assigned recipients through one or more communication channels for accessing the input documents with affixed signatures; and authenticates recipient access by one of: one-time passwords or biometric authentication mechanisms. These measures ensure that only authorized recipients can access the documents and that the documents remain secure throughout the transmission and signing process. For this purpose, firstly, the system securely distributes encrypted document links to the assigned recipients. This is done through one or more communication channels, such as email or social media messaging systems. The use of encrypted links ensures that the actual content of the documents is not transmitted openly over the internet. Instead, recipients receive a link that provides access to the document stored securely on a server of the system. This approach minimizes the risk of unauthorized access or interception during transmission, as the encryption safeguards the link itself and the document remains secured on the system's server. In addition to secure distribution, the system employs mechanisms to authenticate recipient access to the documents. This authentication can occur via one-time passwords (OTPs) or biometric authentication mechanisms. When recipients click on the encrypted link to access a document, they are prompted to authenticate their identity. If OTP is used, the system generates a unique password that is sent to the recipient’s registered phone number or email address. This password must be entered correctly for access to be granted, and it typically expires after a short duration to enhance security. Alternatively, if biometric authentication is enabled, recipients may need to verify their identity using a fingerprint, facial recognition, or other biometric data, which must match the pre-registered data on the system. For recipients using devices equipped with biometric sensors, the system can authenticate access through fingerprint scans, facial recognition, or iris scans. Biometric authentication provides a higher level of security by ensuring that the person accessing the document is the rightful recipient. These combined security features of securely distributing encrypted document links and recipient authentication ensure that the digital signing process is secure from end to end.
[0092] Additionally, in some examples, the system 100 also includes an option like ‘Affix my signature’, via a fourth panel 508, that allows users to access and utilize previously stored signatures. This feature is particularly beneficial for users who frequently sign documents and wish to maintain consistency in their signatures across multiple documents. Upon selecting this option, the user may be presented with a list of their previous documents and saved signatures. Upon selecting a stored signature, the user can affix it to the current document at the desired location.
[0093] In some embodiment, the system 100 (specifically, the user interface 500 therein) provides a centralized dashboard to manage and track progress of the signature workflow across multiple input documents. That is, the status of the document and the progress of the signing process can be tracked in real-time by the users on the dashboard as provided by the user interface 500. This dashboard is designed to provide users with an intuitive and accessible interface from which they can oversee all aspects of the document signing process, enhancing organizational efficiency and control. Such centralized dashboard may display key information such as the current status of each document, which recipients have signed, which are pending, and any delays or issues that might have arisen during the process. This real-time tracking capability allows users to monitor the progression of each document meticulously and ensures that the signature workflows are proceeding as planned. This feature provides users, particularly the initiators of the signing process, with an up-to-date view of the progress of the document signing process, enhancing transparency and control. This further allows the initiator to follow up with the recipients, if necessary. Additionally, the dashboard facilitates easy management of multiple documents simultaneously. Users can quickly navigate through different documents, update workflow settings, and communicate with recipients directly from the dashboard. This centralized management system is particularly beneficial in environments where documents need to be processed quickly and efficiently, such as legal, financial, or corporate settings where time-sensitive documents are common.
[0094] Referring to FIGS. 9-12 in combination, illustrated are process diagrams for implementation of computer vision-based algorithms for signature identification. This algorithm functions in two fundamental stages, each engaging a machine learning model to identify signature locations effectively. The first stage involves the classification of individual pages as signature pages or non-signature pages. This classification helps with identifying the precise location where a signature is required. It is achieved through the implementation of an image classification model, designed to identify the signature page typically present in the execution clause of the document. In particular, the said first stage, first, involves a training pipeline 900 of FIG. 9, in which a first step 902 includes creation of a dataset of images of signature pages and non-signature pages; then a step 904 which includes feature extraction using pretrained CNN models RESNET, Alexnet, Visual Geometry Group (VGG), Inception, GoogleLeNet, etc.; and finally a step 906 which includes model creation using standard machine learning algorithms, such as Random forests, Neural networks, Gradient boosting, AdaBoost, etc. Further, the first stage involves a validation pipeline 1000 of FIG. 10, in which a first step 1002 includes taking all pages of document as images, then a step 1004 which includes feature extraction using pretrained CNN models; and finally a step 1006 which includes predicting signature pages.
[0095] Further, the second stage engages an object detection model to detect the specific location on the signature page where the signature should be placed. This stage forms an essential part of the signature identification process, ensuring the correct placement of signatures within the document. In particular, the said second stage, first, involves a training pipeline 1100 of FIG. 11, in which a first step 1102 includes prepare a dataset of images of signature pages and annotate it to identify signature locations using rectangular boxes; and then a step 1104 which includes implementing the object detection model to predict the bounding boxes where signatures need to be placed, such as using RCNNs, YOLO etc. Also, the second stage involves a validation pipeline 1200 of FIG. 12, in which a first step 1202 includes extracting the signature page from a document and converting it into an image; and then a step 1204 which includes identifying the signature bounding box in the page using object detection algorithm.
[0096] Referring to FIG. 13, illustrated is a flowchart of a process flow 1300 for signature identification using the computer-vision based algorithm. The first step in the process flow 1300 is the input of document pages into the system 100. These pages can come from a variety of sources, be they user-uploaded documents, selected templates, or user-generated documents through the AI document editor. Following the input of the documents, the system 100 initiates the classification of pages using an image classification model. This model has been designed to differentiate between signature pages, which contain fields for signatures, and non-signature pages, which do not, and accordingly provides its outputs. This ensures that the system 100 focuses its signature detection efforts on the correct pages, thereby increasing the overall efficiency of the process. Once the pages have been classified, the system 100 then uses object detection to identify signature boxes on the designated signature pages. This step employs advanced machine learning techniques to detect the specific areas on a page where a signature is required. This ensures that signatures are placed correctly, aligning with the users’ needs and the document’s requirements. The final step of the process involves the automatic affixing of signatures at the detected locations. Based on the roles assigned to the recipients and the signature workflow, the system 100 applies the appropriate signatures, seals, stamp pads, or photos in the designated areas, to deliver the finalized, signed document.
[0097] Also, as discussed, the system 100 provides its users with an AI document editor, a tool that allows the creation of contract documents from scratch. This feature leverages advanced artificial intelligence technology, offering an innovative approach to document preparation. Referring to FIGS. 14A-14C, illustrated are exemplary interfaces for implementation of the AI document editor. As illustrated in an interface 1400A of FIG. 14A, the initiation of the contract creation process is facilitated by user input, with details provided in a designated field, and the process is set in motion with a simple click on the “generate document” function. The underlying AI model that powers this editor is a large language model, tailored specifically to the needs of the system 100. It may be trained using a corpus of company-specific custom documents. This specialized training regimen enables the model to generate legal documents that are contextually accurate and also adhere to the company’s unique legal language and style. Further, as illustrated in an interface 1400B of FIG. 14B, the AI document editor allows the user to add changes to the document through the inclusion of additional clauses. This is not just a simple addition of pre-written clauses, but rather an interactive process, where the AI document editor provides text completion capabilities. As the user begins to type, the AI document editor, leveraging its extensive training, predicts and suggests the completion of the clause, greatly reducing the user’s workload while ensuring legal accuracy. Additionally, as illustrated in an interface 1400C of FIG. 14C, the AI document editor aids in providing further recommendations on related clauses. As the user builds their document, the AI document editor, drawing from its extensive knowledge base, suggests related clauses that might be essential to the contract’s context. This feature supports users in creating more comprehensive and legally compliant documents. Further, as shown in FIGS. 14A-14C, the AI document editor also provides formatting options, ensuring that the document adheres to professional legal standards. This AI document editor is not just a tool for creating documents; it is an intelligent assistant that guides users through the entire contract creation process, ensuring accuracy, efficiency, and legal integrity.
[0098] Referring to FIG. 15, illustrated is a flowchart of a process flow 1500 for generation of a document using the AI document editor, which significantly simplifies and streamlines the process of contract generation. The first step in the process flow 1500 is initiated with user input, where they provide specific keywords related to the desired contract. These keywords serve as cues for the AI document editor, which then generates the contract based on these keywords. The intelligence of this editor is derived from a Large Language Model (LLM) or an in-built, self-trained LLM model, depending on the specific configuration of the system 100. These models have been extensively trained on a vast array of legal documents, allowing them to understand the context and the legal language required for contract generation. In addition to generating the contract, the AI document editor also incorporates word formatting features. These features enable the AI document editor to present the generated contract in a format that adheres to professional legal standards. This integration of formatting features within the AI document editor ensures that the contract generated is legally sound and properly formatted, thereby reducing the need for subsequent formatting adjustments by the user. Upon generation of the contract, the user is given the opportunity to review the document. They can make any necessary adjustments or additions, ensuring that the contract meets their exact needs and specifications. The AI document editor is designed to facilitate this review process, offering interactive editing features that make it easy for users to make changes. Once the user finalizes the contract, it is added to the system 100 for further processing, for example, the finalized document may then be used for further actions, such as sending it for signatures, as discussed in the preceding paragraphs.
[0099] Referring now to FIG. 16, the present disclosure further provides a computer-implemented method (as represented by a flowchart, referred by reference numeral 1600) for digital signing of contract documents. The method 1600 includes a series of steps. These steps are only illustrative, and other alternatives may be considered where one or more steps are added, one or more steps are removed, or one or more steps are provided in a different sequence without departing from the scope of the present disclosure. Various variants disclosed above, with respect to the aforementioned system 100 apply mutatis mutandis to the present method 200 without any limitations.
[00100] At step 1602, the method 1600 includes providing, via a user interface, an option to receive input documents for signing by uploading user documents, selecting pre-defined templates, or generating custom documents based on user keywords utilizing an artificial intelligence (AI) document editor. At step 1604, the method 1600 includes assigning recipients to the input documents and defining roles for the recipients including one or more of signers, witnesses, consenting parties, approvers, and reviewers. At step 1606, the method 1600 includes defining a signature workflow specifying a sequence for the recipients to sign, the signature workflow being one of a sequential order or a parallel order. At step 1608, the method 1600 includes employing a two-stage computer vision algorithm to: classify pages of the input documents into signature pages and non-signature pages using a machine learning image classification model, and detect signature locations on the classified signature pages using a machine learning object detection model. At step 1610, the method 1600 includes automatically affixing signatures, seals, stamps, or photos provided by the assigned recipients at the detected signature locations based on the defined signature workflow.
[00101] In one or more embodiments, the method 1600 further comprises allowing uploading of the input documents from one or more of: a local storage device, a cloud storage service, a document management system, or a network drive via the user interface.
[00102] In one or more embodiments, the AI document editor comprises a large language model trained on a corpus of contract documents to generate the custom documents based on the user keywords and provide text completion suggestions.
[00103] In one or more embodiments, the method 1600 further comprises providing, via the user interface, options to manually specify locations on the input documents for affixing the signatures, seals, stamps, or photos.
[00104] In one or more embodiments, the machine learning image classification model is a convolutional neural network pretrained on a dataset of signature and non-signature page images.
[00105] In one or more embodiments, the machine learning object detection model utilizes a region-based convolutional neural network approach to localize the signature locations within the classified signature pages.
[00106] In one or more embodiments, the method 1600 further comprises integrating with one or more cloud storage platforms to directly receive the input documents.
[00107] In one or more embodiments, the method 1600 further comprises securely distributing encrypted document links to the assigned recipients through one or more communication channels for accessing the input documents with affixed signatures; and authenticating recipient access by one of: one-time passwords or biometric authentication mechanisms.
[00108] In one or more embodiments, the method 1600 further comprises attaching digital stamp papers to the input documents at predefined locations based on the document type.
[00109] In one or more embodiments, the method 1600 further comprises providing, via the user interface, a centralized dashboard to manage and track progress of the signature workflow across multiple input documents.
[00110] The system 100 and the method 1600 (hereinafter, features described in terms of the system 100 only, but equally applies to the corresponding method 1600) of the present disclosure provide a versatile and comprehensive platform that addresses the limitations of traditional paper-based document signing and current e-signing platforms by implementing e-signatures, digital signatures, photo-KYC, and features such as presets, and manual approaches to add signatures, seals, and photographs at defined locations of documents. The system 100 employs a computer vision algorithm to detect signatures and determine their location for automatic affixation. The system 100 also offers an AI document editor that assists users in generating contracts based on keywords. Furthermore, the system 100 can leverage artificial intelligence to streamline the signing process and provide a seamless user experience. The present disclosure solves the aforementioned problems by offering a secure, user-friendly, and efficient platform that can be used from any location in the world.
[00111] The present system 100 addresses a multitude of challenges typically associated with traditional paper-based document signing and various e-signature platforms currently available in the market. One of these challenges is the time-consuming nature of traditional document signing, which is exacerbated when multiple parties are involved. This problem is often compounded by inefficiencies, such as errors during the signing process, lost or misplaced documents, and general delays that can disrupt the overall workflow. Geographical constraints, another significant challenge in traditional document signing, are also addressed by the system 100. The requirement for all parties to be physically present for signing documents can be particularly challenging for international agreements. The system 100, with its digital signature capabilities, allows users to sign documents from any location worldwide, effectively eliminating geographical barriers. Moreover, the system 100 acknowledges the environmental implications of traditional paper-based document signing and offers a more environmentally friendly alternative. By facilitating electronic document signing, the system 100 reduces the need for paper, thus mitigating the environmental impact.
[00112] The system 100 also caters to security issues that have been identified in many contemporary e-signature platforms. These issues are addressed through the implementation of various security measures, such as OTP (One-Time Password) authentication, photo-KYC (Know Your Customer), and MFA (Multi-Factor Authentication). This ensures the security of the system 100, providing users with the confidence that their documents are protected and their signatures are securely authenticated. Ease of usage is another critical aspect addressed by this innovative platform. Unlike traditional methods where users need access to specific applications to securely execute documents, the system 100 is designed to be completely online and user-friendly. The system 100 also provides secure access to document links through social media applications, further enhancing accessibility and convenience for users. The system 100 is further enhanced by an AI document editor that assists users in generating contracts based on specific keywords, which significantly streamlines the document creation process. The system 100 also employs a computer vision algorithm that accurately detects signature locations and automatically affixes signatures in the correct location. This feature reduces errors and significantly improves the efficiency of the document signing process. Thus, the system 100 provides a comprehensive, efficient, and user-friendly solution for document execution, addressing many of the limitations inherent in traditional and currently available e-signing methods.
[00113] The system 100 allows users to execute documents using multiple options, including, but not limited to, (a) uploading documents in various formats (doc, pdf, or .odt), (b) choosing legally wetted custom templates, or (c) creating their own documents using a Generative AI document editor. This AI document editor is based on a Large Language Model (LLM) and offers built-in word formatting features. Users can generate custom contracts by specifying keywords in the field provided on the page.
[00114] Documents can be set up for signature by assigning recipients and selecting the type of signature (e-signature or digital identity, like Aadhar, based digital signature). The system 100 supports the uploading of single or multiple documents at a time, and a tabbed structure is provided for ease of navigation.
[00115] Once recipients are set up, users can define a workflow indicating the sequence of the signature process, which can be sequential or parallel in nature. Signatures, seals, and stamp pads can be attached in multiple ways, such as using computer vision techniques for automatic signature and seal placement, presets (which allow users to affix signatures, seals, or stamp pads at specified locations), or manual (where users manually provide the location of the signature or seal).
[00116] The system 100 sends documents for signatures by launching the defined workflow. Security features include sending links to documents via email or social media messaging systems like WhatsApp or Telegram, with OTP-based authentication for accessing the link. Users can also access previously stored signatures using biometric recognition facilities on mobile phones or OTP-based authentication.
[00117] The computer vision-based signature identification algorithms may involve two steps: (i) identifying the signature page using image classification, and (ii) detecting the location of the signatures in the signature page using object detection.
[00118] The AI document editor enables users to create contract documents from scratch by providing details in a field and clicking on “generate document.” The underlying AI model is based on a company-specific Large Language Model trained using company-specific custom documents. Users can add changes to the document by adding additional clauses, and the AI model provides text completion capabilities and further recommendations on related clauses.
[00119] Thereby, the present disclosure offers a unique, advanced, and comprehensive solution for document signing, addressing the limitations of traditional paper-based document signing and current e-signing platforms. By incorporating AI document editors, computer vision techniques, and seamless integration with social media messaging systems, the system 100 enhances the user experience and streamlines the entire signing process.
[00120] While the present disclosure has been described in detail with reference to certain embodiments, it should be appreciated that the present disclosure is not limited to those embodiments. In view of the present disclosure, many modifications and variations may be present themselves, to those skilled in the art without departing from the scope of the various embodiments of the present disclosure, as described herein. The scope of the present disclosure is, therefore, indicated by the following claims rather than by the foregoing description. All changes, modifications, and variations coming within the meaning and range of equivalency of the claims are to be considered within their scope. ,CLAIMS:WE CLAIM:
1. A system for digital signing of contract documents, the system comprising:
one or more processors; and
a memory storing instructions that, when executed by the one or more processors, cause the system to:
provide a user interface configured to receive input documents for signing by uploading user documents, selecting pre-defined templates, or generating custom documents based on user keywords utilizing an artificial intelligence (AI) document editor;
assign recipients to the input documents and define roles for the recipients including one or more of signers, witnesses, consenting parties, approvers, and reviewers;
define a signature workflow specifying a sequence for the recipients to sign, the signature workflow being one of a sequential order or a parallel order;
employ a two-stage computer vision algorithm to:
classify pages of the input documents into signature pages and non-signature pages using a machine learning image classification model, and
detect signature locations on the classified signature pages using a machine learning object detection model; and
automatically affix signatures, seals, stamps, or photos provided by the assigned recipients at the detected signature locations based on the defined signature workflow.

2. The system as claimed in claim 1, wherein the user interface is further configured to allow uploading of the input documents from one or more of: a local storage device, a cloud storage service, a document management system, or a network drive.

3. The system as claimed in claim 1, wherein the AI document editor comprises a large language model trained on a corpus of contract documents to generate the custom documents based on the user keywords and provide text completion suggestions.

4. The system as claimed in claim 1, the user interface further provides options to manually specify locations on the input documents for affixing the signatures, seals, stamps, or photos.

5. The system as claimed in claim 1, wherein the machine learning image classification model is a convolutional neural network pretrained on a dataset of signature and non-signature page images.

6. The system as claimed in claim 1, wherein the machine learning object detection model utilizes a region-based convolutional neural network approach to localize the signature locations within the classified signature pages.

7. The system as claimed in claim 1, wherein the memory further stores instructions that, when executed, cause the system to integrate with one or more cloud storage platforms to directly receive the input documents.

8. The system as claimed in claim 1, wherein the memory further stores instructions that, when executed, cause the system to:
securely distribute encrypted document links to the assigned recipients through one or more communication channels for accessing the input documents with affixed signatures; and
authenticate recipient access by one of: one-time passwords or biometric authentication mechanisms.

9. The system as claimed in claim 1, wherein the memory further stores instructions that, when executed, cause the system to attach digital stamp papers to the input documents at predefined locations based on the document type.

10. The system as claimed in claim 1, wherein the user interface provides a centralized dashboard to manage and track progress of the signature workflow across multiple input documents.

11. A computer-implemented method for digital signing of contract documents, the method comprising:
providing, via a user interface, an option to receive input documents for signing by uploading user documents, selecting pre-defined templates, or generating custom documents based on user keywords utilizing an artificial intelligence (AI) document editor;
assigning recipients to the input documents and defining roles for the recipients including one or more of signers, witnesses, consenting parties, approvers, and reviewers;
defining a signature workflow specifying a sequence for the recipients to sign, the signature workflow being one of a sequential order or a parallel order;
employing a two-stage computer vision algorithm to:
classify pages of the input documents into signature pages and non-signature pages using a machine learning image classification model, and
detect signature locations on the classified signature pages using a machine learning object detection model; and
automatically affixing signatures, seals, stamps, or photos provided by the assigned recipients at the detected signature locations based on the defined signature workflow.

12. The method as claimed in 11, further comprising allowing uploading of the input documents from one or more of: a local storage device, a cloud storage service, a document management system, or a network drive via the user interface.

13. The method as claimed in 11, wherein the AI document editor comprises a large language model trained on a corpus of contract documents to generate the custom documents based on the user keywords and provide text completion suggestions.

14. The method as claimed in 11, further comprising providing, via the user interface, options to manually specify locations on the input documents for affixing the signatures, seals, stamps, or photos.

15. The method as claimed in 11, wherein the machine learning image classification model is a convolutional neural network pretrained on a dataset of signature and non-signature page images.

16. The method as claimed in 11, wherein the machine learning object detection model utilizes a region-based convolutional neural network approach to localize the signature locations within the classified signature pages.

17. The method as claimed in 11, further comprising integrating with one or more cloud storage platforms to directly receive the input documents.

18. The method as claimed in 11, further comprising:
securely distributing encrypted document links to the assigned recipients through one or more communication channels for accessing the input documents with affixed signatures; and
authenticating recipient access by one of: one-time passwords or biometric authentication mechanisms.

19. The method as claimed in 11, further comprising attaching digital stamp papers to the input documents at predefined locations based on the document type.

20. The method as claimed in 11, further comprising providing, via the user interface, a centralized dashboard to manage and track progress of the signature workflow across multiple input documents.