# US 12,217,318 B2 · AI-augmented auditing platform including techniques for automated document processing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,318 B2 - Document ID: US12217318B2 - Status: Granted - Kind: B2 - Application number: 17/854,348 - Filed: 2022-06-30 - Published: 2023-01-05 - Granted: 2025-02-04 - Earliest claimed: 2021-06-30 - Assignees: PwC Product Sales LLC - Applicants: PwC Product Sales LLC - Inventors: Chung-Sheng Li; Winnie Cheng; Mark John Flavell; Lori Marie Hallmark; Nancy Alayne Lizotte; Anand Srinivasa Rao; Kevin Ma Leong; Di Zhu; Timothy Delille; Maria Jesus Perez Ramirez; Yuan Wan; Ratna Raj Singh; Vishakha Bansal; Shaz Hoda; Amitoj Singh; Siddhesh Shivaji Zanj - Web page: https://patentgrep.com/patent/US12217318B2 ## CPC - G06F 18/22: Matching criteria, e.g. proximity measures - G06Q 40/12: Accounting - G06F 16/3347: {using vector based model} - G06F 16/353: into predefined classes - G06F 16/93: Document management systems - G06F 18/29: {Graphical models, e.g. Bayesian networks} - G06F 40/30: Semantic analysis - G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks - G06N 3/045: Combinations of networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/096: Transfer learning - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 5/041: {Abduction} - G06N 5/045: Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence - G06N 5/048: Fuzzy inferencing - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06Q 10/0635: Risk analysis of enterprise or organisation activities - G06Q 30/018: Certifying business or products - G06V 30/412: Layout analysis of documents structured with printed lines or input boxes, e.g. business forms or tables - G06V 30/416: Extracting the logical structure, e.g. chapters, sections or page numbers; Identifying elements of the document, e.g. authors - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Systems and methods for automated document processing for use in AI-augmented auditing platforms are provided. A system for determining the composition of document bundles extracts substantive content information and metadata information from a document bundle and generates, based on the extracted information regarding a composition of the document bundle. A system for validating signatures in documents extracts data representing a spatial location for respective signatures and generates a confidence level for respective signatures, and determines, based on location and confidence level, whether signature criteria are met. A system for extracting information from documents applies a set of data conversion processing steps to a plurality received documents to generate structured data, and then applies a set of knowledge-based modeling processing steps to the structured data to generating output data extracted from the plurality of electronic documents. ## Claims ### Claim 1 A system for determining the composition of document bundles, the system comprising one or more processors configured to cause the system to: receive data comprising a document bundle; extract, from the document bundle, first information comprising substantive content of one or more documents of the document bundle; extract, from the document bundle, second information comprising metadata associated with one or more documents of the document bundle; and generate output data representing a composition of the document bundle, based on the first information, the second information, and context information; wherein the context information is received from a data source separate from the document bundle and comprises data characterizing a request; and wherein the data comprising the document bundle was received by the system in response to the request. ### Claim 2 (depends on 1) The system of claim 1, wherein the output data representing a composition of the document bundle represents one or more delineations between page boundaries in the document bundle. ### Claim 3 (depends on 1) The system of claim 1, wherein the context information comprises enterprise resource planning (ERP) data received from an ERP system of an entity associated with the document bundle. ### Claim 4 (depends on 1) The system of claim 1, wherein the context information comprises data specifying a predefined set of events associated with a process associated with the document bundle. ### Claim 5 (depends on 1) The system of claim 1, wherein the context information comprises data characterizing an automation process flow for acquiring the data. ### Claim 6 (depends on 1) The system of claim 1, wherein the metadata comprises one or more of: a file name, a file extension, a file creator, and a file date. ### Claim 7 (depends on 1) The system of claim 1, wherein extracting the first information comprises applying embedded object type detection. ### Claim 8 (depends on 1) The system of claim 1, wherein generating the output data comprises applying a page similarity assessment model to a plurality of pages of the document bundle. ### Claim 9 (depends on 1) The system of claim 1, wherein generating the output data comprises applying a finite state modeling data processing operation to the document bundle. ### Claim 10 A non-transitory computer-readable storage medium storing instructions for determining the composition of document bundles, the instructions configured to be executed by one or more processors of a system to cause the system to: receive data comprising a document bundle; extract, from the document bundle, first information comprising substantive content of one or more documents of the document bundle; extract, from the document bundle, second information comprising metadata associated with one or more documents of the document bundle; and generate output data representing a composition of the document bundle, based on the first information, the second information, and context information; wherein the context information is received from a data source separate from the document bundle and comprises data characterizing a request; and wherein the data comprising the document bundle was received by the system in response to the request. ### Claim 11 A method for determining the composition of document bundles, wherein the method is performed by a system comprising one or more processors, the method comprising: receiving data comprising a document bundle; extracting, from the document bundle, first information comprising substantive content of one or more documents of the document bundle; extracting, from the document bundle, second information comprising metadata associated with one or more documents of the document bundle; and generating output data representing a composition of the document bundle, based on the first information, the second information, and context information; wherein the context information is received from a data source separate from the document bundle and comprises data characterizing a request; and wherein the data comprising the document bundle was received by the system in response to the request.