DESC:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
A SYSTEM AND METHOD FOR CONVERTING A TEXT MESSAGE INTO A VISUAL DATA

Applicants:
GUPSHUP TECHNOLOGY INDIA PRIVATE LIMITED
A company Incorporated in India
Having address:
Unit 101, 1st Floor, Silver Metropolis, Western Express Highway, Goregaon East, Mumbai - 400063,
Maharashtra, India

&

GUPSHUP INC.
A company Incorporated in United States of America
Having address:
38350 Fremont Blvd, Suite 203, Fremont, California 94536, United States of America

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[001] The present application claims priority to Indian Patent Application No 202021020937 titled “A SYSTEM AND METHOD FOR CONVERTING A TEXT MESSAGE INTO A VISUAL DATA” filed on 18th November 2020, the entity of which is hereby incorporated by reference.

TECHNICAL FIELD

[002] The present disclosure, in general, relates to a field of messaging applications. More particularly, the present disclosure relates to a system and method for converting a text message into a visual data for development of the messaging application.

BACKGROUND

[003] Messaging system is one of the widely used method among the mobile users to exchange text messages. Further, the messaging system is used to receive important messages related to bank transactions, ticket booking, mobile recharge etc. The structure of the message received may depend on features of the device used by a user.
[004] Further, the message received through the messaging device may be unstructured or the receiving device may not support the messaging format. In case of important message such as messages related to bank transactions and ticket bookings the user may miss the important information from the message due to unstructured nature of the message and same format throughout message content for displaying information.
[005] Though variety of systems are there for providing information display in variety of formats, however, none of the system so far is capable of representing information shared through a text message in a way which is easier to extract valuable information.
SUMMARY
[006] Before the present system and method are described, it is to be understood that this application is not limited to the particular device, machine or an apparatus, and methodologies described, as there can be multiple possible embodiments that are not expressly illustrated in the present disclosures. It is also to be understood that the terminology used in the description is for the purpose of describing the particular versions or embodiments only and is not intended to limit the scope of the present application. This summary is provided to introduce aspects related to a system for prediction breach of compliance, and the aspects are further elaborated below in the detailed description. This summary is not intended to identify essential features of the proposed subject matter nor is it intended for use in determining or limiting the scope of the proposed subject matter.
[007] In one implementation, a system facilitating information extraction from a content. The system comprising a user interface, a memory and a processor coupled to the memory. The processor is configured to execute instructions stored in the memory, wherein the processor is configured for receiving, a text input as the content from external sources. Further, the processor is configured to identifying, intent and entity from the text input by using Machine Learning (ML) techniques. The entity and the intent are used for constructing informative content from the text input by using predefined ontology. The processor is further configured to convert, the informative content into a visual format by using the Messaging Mark-up Language (MML).
[008] In one implementation, a method facilitating information extraction from a content. The method comprising receiving, by a processor, a text input as the content from external sources and identifying, through the processor, intent and entity from the text input by using Machine Learning (ML) techniques. The entity and the intent are used for constructing informative content from the text input by using predefined ontology. Further, the method comprises converting, by the processor, the informative content into a visual format by using the Messaging Mark-up Language (MML).

BRIEF DESCRIPTION OF DRAWINGS
[009] The foregoing summary, as well as the following detailed description of embodiments, is better understood when read in conjunction with the appended drawing. For the purpose of illustrating the disclosure, there is shown in the present document example constructions of the disclosure, however, the disclosure is not limited to the specific methods and apparatus disclosed in the document and the drawing:
[0010] The detailed description is described with reference to the accompanying figure. In the figure, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawing to refer like features and components.
[0011] Figure 1 illustrates a network implementation of a system 102 for converting a text message into a visual data, in accordance with an embodiment of the present subject matter.
[0012] Figure 2 illustrates a block diagram of the system 102 converting the text message into the visual data, in accordance with an embodiment of the present subject matter.
[0013] Figure 3 A illustrates a hierarchical enterprise specific ontology used for converting the text message into the visual data, in accordance with an embodiment of the present subject matter.
[0014] Figure 3 B illustrates different nodes in the hierarchical enterprise specific ontology used for converting the text message into the visual data, in accordance with an embodiment of the present subject matter.
[0015] Figure 4 illustrates exemplary embodiment showing conversion of the text message into the visual data, in accordance with an embodiment of the present subject matter.
[0016] Figure 5 illustrates a method for converting the text message into the visual data, in accordance with an embodiment of the present subject matter.
[0017] The figure depicts various embodiments of the present disclosure for purposes of illustration only. One skilled in the art will readily recognize from the following discussion that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION
[0018] Some embodiments of this disclosure, illustrating all its features, will now be discussed in detail. The words "comprising", “having”, and "including," and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods are now described. The disclosed embodiments are merely exemplary of the disclosure, which may be embodied in various forms.
[0019] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure is not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0020] Referring to Figure 1, a network implementation 100 of a system 102 facilitating conversion of a text message into a visual data is disclosed. Although the present subject matter is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented over a cloud network. Further, it will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…..104-N, collectively referred to as user device 104 hereinafter, or applications residing on the user device 104. Examples of the user device 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user device 104 may be communicatively coupled to the system 102 through a network 106 (also referred as the communication network 106).
[0021] In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices and the like.
[0022] Referring now to Figure 2A, block diagram of the system 102 facilitating conversion of the text message into the visual data is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204 (referred as the client interface), and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machine logic circuitries, and/or any device that manipulate signals based on operational instructions. Among other capabilities, at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
[0023] The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the user device 104. Further, the I/O interface 204 may enable the system 102 to communication with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communication within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc. and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
[0024] In one implementation, a user may access the system 102 via the I/O interface. The user may be registered using the I/O interface in order to use the system 102. In one aspect, the user may access the I/O interface of the system 102 for obtaining information, providing input information or configuring the system 102.
[0025] The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as Static Random-Access Memory (SRAM) and Dynamic Random-Access Memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory may include data.
[0026] The memory 206 is connected to a plurality of modules 208. The system 102 comprises the User Interface 204, a receiving module (212) may be configured to a text input as the content from external sources, an identifying module (214) may be configured to identify intent and entity from the text input and the converting module (216) may be configured to convert the informative content into a visual format.
[0027] The data 210, amongst other things, serve as a database for storing data process, received, and generated by one or more of the modules 208. The data 210 may also include a database 222, and other data 224. In one embodiment, the other data 224 may include data generated as a result of the execution of one or more modules in the other modules.
[0028] In an embodiment information receiving module (212) is configured to receive the text input as the content from an external source. The external source may include a cellular service provider, a web application or a mobile application. Further, using a set of predefined set of possible user actions and the text input a sub ontology of a predefined ontology may be defined. The received text input may comprise a SMS, push message, push notification, or internet messages. In exemplary embodiment, the user action may be reporting a fraud occurring in a financial transaction, completion of a transaction using a received one time password (OTP).
[0029] Once the text input is received the identifying module (214) may identify an intent and entity from the text input by using a Machine Learning (ML) technique. The entity and the intent may be used for constructing informative content from the text input by using a sub ontology. In an embodiment the ML technique may comprise Natural Language Processing (NLP) like Named Entity Recognition and Dual Intent and Entity Extraction frameworks.
[0030] Referring to Figure 3A and 3B in combination, the predefined ontology is shown. The predefined ontology comprises a hierarchical enterprise specific ontology. Further each node (308) in the hierarchical enterprise ontology comprises predefined entity and a predefine intent defined specifically for each of the organization Examples of predefined entity and intent may be “payment status-invoice generated”, “credit-success” when the organization is the banking.
[0031] Further, the predefined entity and the intent may be mapped with the text input for identifying the entity and the intent used for constructing informative content from the text input. In an embodiment the enterprise ontology may be built by using the ML technologies and the predefined ontology may then be stored in a semantic database.
[0032] Now Figure 3B, shows nodes (308) in the hierarchical enterprise ontology related to banking. As observed a root node is further divided into a transaction and promotion nodes. Further, the transaction nodes may be further split into authentication, financial transaction and purchase and the process of splitting of the entity and intent may continue till the final action is reached. Further, at each node (308) the entity and the intent may be identified and further converted extracted and converted into the visual format.
[0033] In an embodiment, after identifying the intent and entity from the text input, the converting module (216) may convert the informative content into a visual format by using a Messaging Mark-up Language (MML). The MML may use CSS, html or java script for converting the informative content into the visual format. The visual format may comprise an image generated in a JPEG format.
[0034] Referring to Figure 4, in an exemplary embodiment, the user may receive the text message (402) for a movie ticket. The system 102 identifies the intent and entity from the text message (movie ticket details received through the SMS after booking an online ticket) and further converts the extracted intent and entity combinations to MML. The booking details is visualized as a card (404) named “Joker 3D” showing details of movie ticked in form of a card (Figure 4B).
[0035] In another exemplary embodiment, as illustrated in Figure 4, the bank transaction message (406) received via a messaging system as the text message is converted into the visual data (408) showing all the relevant details of the transactions.
[0036] Referring now to Figure 5, a method 500 facilitating conversion of the text message into the visual data, is disclosed in accordance with an embodiment of the present subject matter. The method 500 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structure, procedure, modules, functions, and the like, that perform particular functions or implement particular abstract data types. The method 500 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are lined though a communications network. In a distributed computing environment. Computer executable instructions may be located in both local and remote computer storage media, including memory storage device.
[0037] The order in which the method 500 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method or alternate methods, Additionally, individual blocks may be deleted from the method 500 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 500 can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 500 may be considered to be implemented in the above-described system 102.
[0038] At block 502, the text input may be received as the content from external source. Further, using a set of predefined set of possible user actions and the text input a sub ontology of a predefined ontology may be defined.
[0039] At block 504, the processor identifies the intent and entity from the text input by using Machine Learning (ML) techniques.
[0040] At block 506, the informative content may be converted into the visual format by using the Messaging Marked up Language (MML).
[0041] Exemplary embodiments discussed above may provide certain advantages. Though not required to practice aspects of the disclosure, these advantages may include those provided by the following features.
[0042] Some object of the present invention provides visualization of the received text message.
,CLAIMS:

1. A system facilitating information extraction from a content, the system comprising:
a user interface (204);
a memory (206);
a processor (202) coupled to the memory (204), wherein the processor (202) is configured to execute instructions stored in the memory, wherein the processor is configured for:
receiving, a text input as the content from external sources;
using a set of predefined set of possible User Actions and the text input to identify a sub ontology of a predefined ontology;
identifying, intent and entity from the text input by using Machine Learning (ML) techniques, wherein the entity and the intent are used for constructing informative content from the text input by using the sub ontology; and
converting, the informative content into a visual format by using a Messaging Mark-up Language (MML).

2. The system as claimed in claim 1, wherein the text input comprises SMS, push messages, push notifications, or internet messages.
3. The system as claimed in claim 1, wherein the ML techniques comprise Natural Language Processing (NLP).
4. The system as claimed in claim 1, wherein the MML uses CSS, html, or java script for converting the informative content into the visual format.
5. The system as claimed in claim 1, wherein the visual format comprises an image generated in a JPEG format.
6. The system as claimed in claim 1, wherein the predefined ontology (302) comprises a hierarchical enterprise specific ontology, wherein each node in the hierarchical enterprise specific ontology comprises predefined entity (304) and predefined intent (306) to be mapped with the text input for identifying the entity and the intent used for constructing informative content.
7. A method facilitating information extraction from a content, the method comprising:
receiving, by a processor (202), a text input as the content from external sources
using a set of predefined set of possible User Actions and the text input to identify a sub ontology of a predefined ontology;
identifying, through the processor (202), intent and entity from the text input by using Machine Learning (ML) techniques, wherein the entity and the intent are used for constructing informative content from the text input by using the sub ontology; and
converting, by the processor (202), the informative content into a visual format by using the Messaging Mark-up Language (MML).

8. The method as claimed in claim 7, wherein the text input comprises SMS, push messages, push notifications, or internet messages.

9. The method as claimed in claim 7, wherein the ML techniques comprise Natural Language Processing (NLP), and wherein the MML uses CSS, html, or java script for converting the informative content into the visual format, and wherein the visual format comprises an image generated in a JPEG format.
10. The method as claimed in claim 7, wherein the predefined ontology comprises a hierarchical enterprise specific ontology (302), wherein each node (308) in the hierarchical enterprise specific ontology comprises predefined entity (304) and predefined intent (306) to be mapped with the text input for identifying the entity and the intent used for constructing informative content.