1. An intelligent invoice management system comprising: a data ingestion module for receiving invoice data from a plurality of sources; an intelligent invoice extraction module for extracting relevant information from the received invoice data; a data storage module for storing the extracted information; a machine learning module for analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data; a rules engine for applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions; an alert mechanism for generating alerts based on the results of the rules engine and sending the alerts to designated users or systems; and a reporting module for generating reports based on the extracted invoice data and the results of the rules engine.

2. The system of claim 1, further comprising a user interface for displaying the extracted invoice data, exceptions, alerts, and reports and a collaboration module for facilitating communication and collaboration between users involved in the invoice management process.

3. The system of claim 1, wherein the alert mechanism is configured to generate alerts for a variety of exceptions including, but not limited to, invoices with missing information, invalid data, or duplicate entries.

4. The system of claim 1, wherein the alert mechanism is configured to send alerts to designated users or systems via email, text message, or through an internal messaging system.

5. The system of claim 1, wherein the machine learning module is configured to learn from the exceptions flagged by the rules engine, and adjust the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.

6. A method for managing invoices using an intelligent invoice management platform with alert mechanism, the method comprising: receiving invoice data from a plurality of sources using a data ingestion module; extracting relevant information from the received invoice data using an intelligent invoice extraction module; storing the extracted information in a data storage module; analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data using a machine learning module; applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions using a rules engine; generating alerts based on the results of the rules engine and sending the alerts to designated users or systems using an alert mechanism; and generating reports based on the extracted invoice data and the results of the rules engine using a reporting module.

7. The method of claim 6, further comprising: displaying the extracted invoice data, exceptions, alerts, and reports using a user interface; and facilitating communication and collaboration between users involved in the invoice management process using a collaboration module.

8. The method of claim 6, wherein the alert mechanism generates alerts based on user-defined thresholds for certain exceptions or invoice values.

9. The method of claim 6, wherein the alert mechanism generates real-time alerts for urgent exceptions, and scheduled alerts for routine exceptions.

10. The method of claim 6, wherein the machine learning module learns from the exceptions flagged by the rules engine, and adjusts the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.   INTELLIGENT INVOICE MANAGEMENT PLATFORM Abstract Certain implementations of the present disclosure may comprise an intelligent invoice management system. One possible component of this system is an invoice data intake module for ingesting bills from various sources. In certain implementations, a separate component known as an intelligent invoice extraction module can be added to parse the incoming invoice data and pull out actionable insights. In certain implementations, the extracted data is stored in a data storage module. Embodiments may additionally comprise a machine learning module that analyses the extracted information and learns from the extracted information in order to automatically identify and retrieve relevant invoice data. Certain implementations of the invention may further include a rules engine that applies user-defined rules to the extracted invoice data to validate the invoice and identify exceptions. In certain implementations, an alert mechanism is also in place, whose job it is to generate alerts in response to the rules engine's output and send those alerts on to the necessary people or systems. Fig. 1 , Claims:Claims :

1. An intelligent invoice management system comprising: a data ingestion module for receiving invoice data from a plurality of sources; an intelligent invoice extraction module for extracting relevant information from the received invoice data; a data storage module for storing the extracted information; a machine learning module for analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data; a rules engine for applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions; an alert mechanism for generating alerts based on the results of the rules engine and sending the alerts to designated users or systems; and a reporting module for generating reports based on the extracted invoice data and the results of the rules engine.

2. The system of claim 1, further comprising a user interface for displaying the extracted invoice data, exceptions, alerts, and reports and a collaboration module for facilitating communication and collaboration between users involved in the invoice management process.

3. The system of claim 1, wherein the alert mechanism is configured to generate alerts for a variety of exceptions including, but not limited to, invoices with missing information, invalid data, or duplicate entries.

4. The system of claim 1, wherein the alert mechanism is configured to send alerts to designated users or systems via email, text message, or through an internal messaging system.

5. The system of claim 1, wherein the machine learning module is configured to learn from the exceptions flagged by the rules engine, and adjust the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.

6. A method for managing invoices using an intelligent invoice management platform with alert mechanism, the method comprising: receiving invoice data from a plurality of sources using a data ingestion module; extracting relevant information from the received invoice data using an intelligent invoice extraction module; storing the extracted information in a data storage module; analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data using a machine learning module; applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions using a rules engine; generating alerts based on the results of the rules engine and sending the alerts to designated users or systems using an alert mechanism; and generating reports based on the extracted invoice data and the results of the rules engine using a reporting module.

7. The method of claim 6, further comprising: displaying the extracted invoice data, exceptions, alerts, and reports using a user interface; and facilitating communication and collaboration between users involved in the invoice management process using a collaboration module.

8. The method of claim 6, wherein the alert mechanism generates alerts based on user-defined thresholds for certain exceptions or invoice values.

9. The method of claim 6, wherein the alert mechanism generates real-time alerts for urgent exceptions, and scheduled alerts for routine exceptions.

10. The method of claim 6, wherein the machine learning module learns from the exceptions flagged by the rules engine, and adjusts the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.

Description:INTELLIGENT INVOICE MANAGEMENT PLATFORM
Field of the Invention
[0001] The present invention relates generally to financial transaction system. More particularly, to a system and method for intelligent invoice management.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] An invoice is a document that itemizes and records a transaction between a buyer and a seller. It typically includes details such as the product or service purchased, the quantity, the price, the payment terms, and any applicable taxes or discounts. Invoices are used as a means of requesting payment from the buyer and serve as a record of the transaction for both parties. They may be sent as a paper or digital document and are often used in business-to-business (B2B) transactions.
[0004] Invoice management is the process of organizing, monitoring, and processing invoices in a systematic and efficient manner. It involves tasks such as recording invoice data, tracking payment status, ensuring compliance with payment terms, reconciling payments with invoices, and generating reports on invoice activity and payment history. Effective invoice management can help businesses improve cash flow, reduce the risk of late payments and associated penalties, and enhance overall financial visibility and control. Invoice management can be performed manually or using specialized software platforms that automate and streamline the process.
[0005] Invoices are critical documents that help businesses request payment for goods or services rendered. However, managing invoices can be a time-consuming and error-prone process, particularly for businesses that have a high volume of invoices. Inefficient invoice management can result in delayed payments, missed payment deadlines, and financial losses for the business. Additionally, manually managing invoices can be resource-intensive and may require significant staff hours to ensure invoices are processed correctly and paid on time. To address these challenges, many businesses have turned to specialized platforms to automate and streamline the invoice management process. These platforms can help businesses digitize invoices, automate payment processing, and track payment status, among other features. This can help reduce the risk of errors, improve cash flow, and free up staff time to focus on other core business activities. Following documents disclose exemplary invoice management technique.
[0006] The US20140067633A1 (By: SAP SE) includes architectures and methods for automated management of invoices. Embodiments of the present invention may include techniques for receiving and unifying invoice data, retrieving information about each invoice, verifying each invoice and resolving invoice exceptions. The present invention includes software components for efficiently processing invoices. In other embodiments, the present invention includes methods of processing an invoice.
[0007] The DE10037633A1 (By: International Business Machines Corp) includes an input unit for input and storage of invoices, a database tool with goods received receipts and delivery orders. A balancing tool is connected to the input unit and the database tool for periodically polling the database tool to determine if a new goods receipt exists, and for performing a logical triple balancing among each invoice, the goods receipt(s) and the delivery orders. A transmission tool transmits the results to the database tool, including those stored invoices for which the balancing tool has found a correspondence. Independent claims are included for a method of processing invoices, and a computer program product, and a process step for controlling a computer.
[0008] The US8666854B2 (By: Oracle International Corp) provides a unified view of invoice and revenue information for a contract. One embodiment includes receiving a request to display information about a contract, and displaying, in response to the request, a financial summary interface including invoice and revenue information for the contract in the same financial summary interface. The invoice and revenue information for the contract may include contract value, invoiced amount, accrued revenue, and backlog amount.
[0009] The US20130144782A1 (By: Bottomline Technologies Inc) relates to a system and method for predicting a status change of a particular invoice from among a plurality of invoices within a database, where an invoice status relates to an event occurring in a timeline of events associated with payment of the invoice. The system and method may predict when the status of the particular invoice will change by analyzing those invoices of the plurality of invoices that are associated with a given buyer, where the given buyer is the buyer associated with the particular invoice. The prediction may be a function of the date of a previous event in the sequence of events associated with the particular invoice and an average timeline of events associated with payment of invoices by the given buyer.
[00010] These solutions are associated with several limitations such as lower accuracy, speed up payment processing, and many more. Thus, effective invoice management is a critical component of a successful business operation. By implementing efficient and accurate invoice management processes, businesses can improve financial performance, increase operational efficiency, and enhance their overall competitiveness in the marketplace.

Summary
[00011] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00012] The following paragraphs provide additional support for the claims of the subject application.
[00013] The present invention relates generally to financial transaction system. More particularly, to a system and method for intelligent invoice management.
[00014] Embodiments of the present disclosure may include an intelligent invoice management system including a data ingestion module for receiving invoice data from a plurality of sources. Embodiments may also include an intelligent invoice extraction module for extracting information from the received invoice data. Embodiments may also include a data storage module for storing the extracted information.

[00015] Embodiments may also include a machine learning module for analyzing the extracted information and train the extracted information to automatically identify and extract relevant invoice data. Embodiments may also include a rules engine for applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions. Embodiments may also include an alert mechanism for generating alerts based on the results of the rules engine and sending the alerts to designated users or systems. Embodiments may also include a reporting module for generating reports based on the extracted invoice data and the results of the rules engine.
[00016] In some embodiments, the system may include a user interface for displaying the extracted invoice data, exceptions, alerts, and reports. The systema may include a collaboration module for facilitating communication and collaboration between users involved in the invoice management process. In some embodiments, the alert mechanism may be configured to generate alerts for a variety of exceptions including, but not limited to, invoices with missing information, invalid data, or duplicate entries.
[00017] In some embodiments, the alert mechanism may be configured to send alerts to designated users or systems via email, text message, or through an internal messaging system. In some embodiments, the machine learning module may be configured to learn from the exceptions flagged by the rules engine, and adjust the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.
[00018] Embodiments of the present disclosure may also include a method for managing invoices using an intelligent invoice management platform with alert mechanism including, receiving invoice data from a plurality of sources using the data ingestion module. Embodiments may also include extracting relevant information from the received invoice data using the intelligent invoice extraction module.
[00019] Embodiments may also include storing the extracted information in a data storage module. Embodiments may also include analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data using a machine learning module. Embodiments may also include applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions using a rules engine. Embodiments may also include generating alerts based on the results of the rules engine and sending the alerts to designated users or systems using an alert mechanism. Embodiments may also include generating reports based on the extracted invoice data and the results of the rules engine using a reporting module.
[00020] In some embodiments, the method may include displaying the extracted invoice data, exceptions, alerts, and reports using a user interface. Embodiments may also include Facilitating communication and collaboration between users involved in the invoice management process using a collaboration module. In some embodiments, the alert mechanism generates alerts based on user-defined thresholds for certain exceptions or invoice values. In some embodiments, the alert mechanism generates real-time alerts for urgent exceptions, and scheduled alerts for routine exceptions.
Brief Description of the Drawings
[00021] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00022] FIG. 1 is a block diagram illustrating an intelligent invoice management system, according to some embodiments of the present disclosure.
[00023] FIG. 2 is a flowchart illustrating a method for managing invoices, according to some embodiments of the present disclosure.
[00024] FIG. 3 is a detailed flowchart further illustrating the method for managing invoices from FIG. 2, according to some embodiments of the present disclosure.

Detailed Description
[00025] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00026] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00027] The present invention relates generally to financial transaction system. More particularly, to a system and method for intelligent invoice management.
[00028] A block diagram of an intelligent invoice management system 100 (interchangeably referred as system 100) is shown in FIG. 1. This system is described in accordance with various aspects of the current disclosure. The intelligent invoice management system 100 may, in some implementations, include a data ingestion module 110 for the purpose of receiving invoice data from a number of different sources, an intelligent invoice extraction module 120 for the purpose of extracting relevant information from the received invoice data, a data storage module 130 for the purpose of storing the information that has been extracted, a machine learning module 140 for the purpose of analysing the information that has been extracted and training the data that has been extracted to automatically identify and resolve issues with the extracted information.
[00029] A user interface for displaying the extracted invoice data, exceptions, alerts, and reports may also be included in certain embodiments of the intelligent invoice management system 100. Additionally, the intelligent invoice management system 100 may include a collaboration module that facilitates communication and collaboration between users who are involved in the process of invoice management. In certain implementations, the alert mechanism 160 may be programmed to provide alerts in response to a wide range of exceptions. Invoices with missing information, inaccurate data, or duplicate entries are some examples, however this is not an exhaustive list.
[00030] In certain implementations, the alert mechanism 160 can be programmed to deliver notifications to certain people or systems through email, text message, or an internal messaging system. The machine learning module 140 may be designed, in some implementations, to learn from the exceptions that are highlighted by the rules engine 150 and then update the rules and alert thresholds accordingly in order to enhance the accuracy and efficiency of the system 100. In some implementations, the machine learning module140 is responsible for gaining insight from the exceptions that are flagged by the rules engine 150. This insight is then used to make appropriate adjustments to the rules and alert thresholds in order to make the system 100 more accurate and effective.
[00031] The method for managing invoices is illustrated in flowchart form in FIG. 2, which provides a description of the technique in accordance with various embodiments of the current disclosure. Receiving invoice data from many sources using a data ingestion module is an optional step that may be included in some implementations of the technique at the step 210. At step 220, the technique can involve employing an intelligent invoice extraction module to extract pertinent information from the data of the received invoices. The information that was extracted can be saved in a data storage module at step 230, if the procedure specifies this step. The technique may, at step 240, involve performing an analysis on the information that has been collected and conducting training on the data that has been extracted in order to automatically identify and retrieve relevant invoice data using a machine learning module. At step 250 of the process, it is possible to apply user-defined rules to the retrieved invoice data using a rules engine in order to validate the invoice and flag any exceptions that may occur. At step 260, the technique can involve making use of a reporting module to generate reports based on the retrieved invoice data and the conclusions reached by the rules engine.
[00032] The method for managing invoices shown in FIG. 2 is illustrated in more detail by the flowchart seen in FIG. 3, which is provided in accordance with some implementations of the current disclosure. Showing the extracted invoice data, exceptions, alerts, and reports via a user interface is an optional step that may be included in certain implementations of the procedure at the 310 step. The method may include, at step 320, the use of a collaboration module to facilitate communication and cooperation amongst users who are actively participating in the process of invoice management.
[00033] A data receiver that is capable of receiving invoice data from one or more sources is one component that may be included in embodiments of the present disclosure as part of a system for managing invoices. In addition, embodiments may comprise a platform for managing invoices, which would be used to organise and store the invoice data, with a unique identification being given to each individual invoice. The monitoring of the invoice data and the generation of alerts based on specified criteria, such as due dates, payment amounts, or other pertinent information, may also be included in certain embodiments as an optional alert mechanism.
[00034] A notification system that may deliver the warnings to selected recipients by email, text message, or other communication channels is another component that may be included in embodiments. Some embodiments may additionally comprise a payment processing system that makes it easier for customers to pay their bills and keeps track of the status of those invoices accordingly. A reporting system that is capable of creating individualised reports on invoice activity, payment history, and other pertinent metrics may also be included in embodiments.
[00035] In certain implementations, the alert mechanism provides the capability to personalise notifications in accordance with the unique requirements posed by a variety of users and organisations. In some implementations, the reporting system makes it possible to conduct an analysis of invoice data and payment history, which reveals recurring tendencies and patterns in the activity related to invoices. The alert mechanism and the payment processing system can, in some implementations, make it possible to easily retrieve and share invoice data and payment history with authorised parties. This helps to reduce the risk of unauthorised access or tampering with the data.
[00036] In certain implementations, the alert mechanism and the payment processing system make it possible to automatically generate and send bills depending on predetermined schedules or events using smart contracts. This is made possible by the combination of the two systems. It's possible that certain implementations of the approach involve automatically matching up payments with their associated invoices through the use of the payment processing system.
[00037] The technique may, in certain implementations, comprise performing an analysis of the invoice data and payment history using the reporting system in order to discover trends and patterns in the activity of the invoices. The approach may, in certain implementations, incorporate the step of automatically carrying out compliance checks, with the goal of ensuring that all payments are in accordance with the relevant regulations and laws.
[00038] The current disclosure may additionally include a method for managing invoices, which may comprise receiving invoice data from one or more sources. This technique may be included in embodiments of the present disclosure. Invoices can be given their own unique identifiers, and the data on those invoices can be organised and stored on a platform that manages invoices. These are all examples of possible embodiments. A monitoring of the invoice data using an alert system and the generation of notifications based on specified criteria, such as due dates, payment amounts, or other pertinent information, may also be included in some embodiments.
[00039] Some embodiments may further comprise delivering the notifications to selected recipients using various communication channels, such as email, text message, or other channels. A payment processing system may also be used to facilitate the payment of invoices and to update the invoice status in accordance with the changes made to the status of the payment. A reporting system can be used to generate individualised reports on invoice activity, payment history, and any other metrics that are pertinent to the situation. This can be included in certain embodiments.
[00040] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00041] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00042] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00043] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00044] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. An intelligent invoice management system comprising:
a data ingestion module for receiving invoice data from a plurality of sources;
an intelligent invoice extraction module for extracting relevant information from the received invoice data;
a data storage module for storing the extracted information;
a machine learning module for analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data;
a rules engine for applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions;
an alert mechanism for generating alerts based on the results of the rules engine and sending the alerts to designated users or systems; and
a reporting module for generating reports based on the extracted invoice data and the results of the rules engine.

2. The system of claim 1, further comprising a user interface for displaying the extracted invoice data, exceptions, alerts, and reports and a collaboration module for facilitating communication and collaboration between users involved in the invoice management process.

3. The system of claim 1, wherein the alert mechanism is configured to generate alerts for a variety of exceptions including, but not limited to, invoices with missing information, invalid data, or duplicate entries.

4. The system of claim 1, wherein the alert mechanism is configured to send alerts to designated users or systems via email, text message, or through an internal messaging system.

5. The system of claim 1, wherein the machine learning module is configured to learn from the exceptions flagged by the rules engine, and adjust the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.

6. A method for managing invoices using an intelligent invoice management platform with alert mechanism, the method comprising:
receiving invoice data from a plurality of sources using a data ingestion module;
extracting relevant information from the received invoice data using an intelligent invoice extraction module;
storing the extracted information in a data storage module;
analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data using a machine learning module;
applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions using a rules engine;
generating alerts based on the results of the rules engine and sending the alerts to designated users or systems using an alert mechanism; and
generating reports based on the extracted invoice data and the results of the rules engine using a reporting module.

7. The method of claim 6, further comprising:
displaying the extracted invoice data, exceptions, alerts, and reports using a user interface; and
facilitating communication and collaboration between users involved in the invoice management process using a collaboration module.

8. The method of claim 6, wherein the alert mechanism generates alerts based on user-defined thresholds for certain exceptions or invoice values.

9. The method of claim 6, wherein the alert mechanism generates real-time alerts for urgent exceptions, and scheduled alerts for routine exceptions.

10. The method of claim 6, wherein the machine learning module learns from the exceptions flagged by the rules engine, and adjusts the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.
 

INTELLIGENT INVOICE MANAGEMENT PLATFORM
Abstract
Certain implementations of the present disclosure may comprise an intelligent invoice management system. One possible component of this system is an invoice data intake module for ingesting bills from various sources. In certain implementations, a separate component known as an intelligent invoice extraction module can be added to parse the incoming invoice data and pull out actionable insights. In certain implementations, the extracted data is stored in a data storage module. Embodiments may additionally comprise a machine learning module that analyses the extracted information and learns from the extracted information in order to automatically identify and retrieve relevant invoice data. Certain implementations of the invention may further include a rules engine that applies user-defined rules to the extracted invoice data to validate the invoice and identify exceptions. In certain implementations, an alert mechanism is also in place, whose job it is to generate alerts in response to the rules engine's output and send those alerts on to the necessary people or systems.

Fig. 1 , Claims:Claims
I/We Claim:
1. An intelligent invoice management system comprising:
a data ingestion module for receiving invoice data from a plurality of sources;
an intelligent invoice extraction module for extracting relevant information from the received invoice data;
a data storage module for storing the extracted information;
a machine learning module for analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data;
a rules engine for applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions;
an alert mechanism for generating alerts based on the results of the rules engine and sending the alerts to designated users or systems; and
a reporting module for generating reports based on the extracted invoice data and the results of the rules engine.

2. The system of claim 1, further comprising a user interface for displaying the extracted invoice data, exceptions, alerts, and reports and a collaboration module for facilitating communication and collaboration between users involved in the invoice management process.

3. The system of claim 1, wherein the alert mechanism is configured to generate alerts for a variety of exceptions including, but not limited to, invoices with missing information, invalid data, or duplicate entries.

4. The system of claim 1, wherein the alert mechanism is configured to send alerts to designated users or systems via email, text message, or through an internal messaging system.

5. The system of claim 1, wherein the machine learning module is configured to learn from the exceptions flagged by the rules engine, and adjust the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.

6. A method for managing invoices using an intelligent invoice management platform with alert mechanism, the method comprising:
receiving invoice data from a plurality of sources using a data ingestion module;
extracting relevant information from the received invoice data using an intelligent invoice extraction module;
storing the extracted information in a data storage module;
analyzing the extracted information and training on the extracted data to automatically identify and extract relevant invoice data using a machine learning module;
applying user-defined rules to the extracted invoice data to validate the invoice and flag exceptions using a rules engine;
generating alerts based on the results of the rules engine and sending the alerts to designated users or systems using an alert mechanism; and
generating reports based on the extracted invoice data and the results of the rules engine using a reporting module.

7. The method of claim 6, further comprising:
displaying the extracted invoice data, exceptions, alerts, and reports using a user interface; and
facilitating communication and collaboration between users involved in the invoice management process using a collaboration module.

8. The method of claim 6, wherein the alert mechanism generates alerts based on user-defined thresholds for certain exceptions or invoice values.

9. The method of claim 6, wherein the alert mechanism generates real-time alerts for urgent exceptions, and scheduled alerts for routine exceptions.

10. The method of claim 6, wherein the machine learning module learns from the exceptions flagged by the rules engine, and adjusts the rules and alert thresholds accordingly to improve the accuracy and efficiency of the system.