US-20260127456-A1 - CONTEXTUAL SEMANTIC DERIVATION OF DATA RELATIONSHIPS

US20260127456A1US 20260127456 A1US20260127456 A1US 20260127456A1US-20260127456-A1

Abstract

The present invention includes novel methods and systems for deriving meaning from context, enabling the automation of processes that currently require significant human judgment and intervention. An autonomous event-driven system runs on a continuous basis over time, detecting and responding to new events as new information is obtained (including the mere passage of time) to implement virtually any scenario in which relationships among data within and across documents are difficult to discern (without human intervention) from the explicit information contained in the documents (DDRs). Trained models perform contextual semantic derivation (CSD), often in parallel, to derive meaning from context within and across documents in the form of DDRs and other relationships stored in an iteratively traversed and updated knowledge graph, which is leveraged to perform lower-level document processing tasks (capture, classification, matching, reconciliation, etc.) as well as higher-level tasks (natural-language interrogation, anomaly detection and resolution, decisioning and analytics).

Inventors

James Thomas
Stephen Markle
Christopher Ryan Courtade

Assignees

Itemize Corporation

Dates

Publication Date: 20260507
Application Date: 20251229

Claims (5)

1 . A method for deriving meaning from the context of data within and across a plurality of documents, the method comprising the following steps: (a) receiving information including one or more documents, each document including a plurality of fields; (b) identifying a plurality of relationships among the plurality of fields, and updating a current knowledge graph to reflect the plurality of identified relationships; and (c) traversing the updated knowledge graph in response to the update to reassess the plurality of identified relationships in the updated knowledge graph.
2 . A method for deriving meaning from the context of data within and across a plurality of documents, the method comprising the following steps: (a) receiving information including one or more documents, each document including a plurality of fields; (b) invoking in parallel both a first process and a second process to analyze the information, wherein the first process identifies a plurality of relationships among the plurality of fields and updates a current knowledge graph to reflect the plurality of identified relationships; and (c) wherein the second process, in response to the update, traverses the updated knowledge graph and reassesses the plurality of identified relationships in the updated knowledge graph.
3 . A method for deriving meaning from the context of data within and across a plurality of documents, the method comprising the following steps: (a) receiving information including one or more documents, each document including a plurality of fields; (b) invoking in parallel both a first process and a second process to analyze the information, wherein the first process identifies a plurality of relationships among the plurality of fields, including, with respect to at least one of the plurality of relationships, a corresponding confidence value reflecting the level of confidence that such relationship is accurate, and updates a current knowledge graph to reflect the plurality of identified relationships and corresponding confidence values; and (c) wherein the second process, in response to the update, traverses the updated knowledge graph and reassesses the plurality of identified relationships and corresponding confidence values in the updated knowledge graph.
4 . The method of claim 1 , wherein the traversal of the knowledge graph facilitates the performance of one or more tasks dependent upon at least one of the plurality of relationships.
5 . The method of claim 1 , wherein the traversal of the knowledge graph facilitates the detection of an anomaly with respect to one or more of the plurality of relationships.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. utility patent application Ser. No. 19/010,696, filed Jan. 6, 2025 and entitled “Contextual Semantic Derivation of Data Relationships,” which claims priority to U.S. provisional patent application Ser. No. 63/559,369, filed Feb. 29, 2024 and entitled “Contextual Semantic Derivation of Data Relationships,” the disclosures of which are hereby incorporated by reference as if fully set forth herein. FIELD OF ART The present invention relates generally to the automation of personal, business and other processes, and more particularly to the automation of processes, such as those inherent in financial transactions, in which relationships among data within and across documents are difficult to discern (without human intervention) from the explicit information contained in those documents. DESCRIPTION OF RELATED ART Despite the dramatic impact of computerization on our personal and business lives over the past century, the myth of the “paperless office” remains just that-a myth describing a goal that may well never become reality. For a variety of reasons, paper is likely to be here to stay for the foreseeable future. Moreover, even if “electronic documents” ever completely replace paper, even these documents often reveal an incomplete picture of the underlying “narrative” or “story” of the transactions and related processes spawning such documents. For example, such documents are far from error-free (e.g., due to data entry errors), and relationships among data within and across such documents are often not self-evident (e.g., where a missing or incorrect vendor name must be inferred from other relevant “matching” information across multiple documents). As will become apparent, these “difficult-to-derive relationships” (or “DDR”s) are the source of many problems in which “hidden” context results in a failure to derive meaning. These problems currently are “resolved” by an overreliance on human judgment and intervention. It is therefore not surprising that, despite the ever-increasing level of computerization and automation of certain aspects of a process, human judgment remains not only a necessary but a pervasive component of the implementation of virtually any non-trivial process. Eliminating human judgment entirely may well be a fool's errand. But, as will become evident, there currently exist many opportunities to automate processes that are currently relegated to human judgment and intervention due to the difficulty of deriving meaning from context. While much of the following discussion relates to financial business transactions, it will become apparent that the concepts discussed herein apply to other types of personal, business, educational, governmental and other processes, including those outside the financial realm. Virtually any process that currently involves interrelated “documents” (paper, electronic or otherwise)—in which relationships among data within and across such documents are difficult to discern from the explicit information contained in such documents—presents similar problems that are currently addressed via frequent human judgment and intervention. For example, various non-financial scenarios involve DDRs. Consider a college admissions officer examining applicants' transcripts, test scores, personal essays and other application details, and employing human judgment to determine which applicants merit admission. Or a medical doctor examining patients' current and historical test results, examination and treatment records and other data, and similarly employing human judgment to form diagnoses of patient conditions and prognoses for the future progression of such conditions. In each of these and other financial and non-financial scenarios, human judgment is employed in part due to the difficulty of deriving relationships among data within and across documents (i.e., DDRs) despite access to the explicit information contained within such documents. Moreover, the concept of “documents” as described herein is more expansive than the characters and words found in typical paper or electronic documents or files. It covers not only the locations of characters and words on a page and the structure of such data (e.g., in a database or spreadsheet file), but virtually any other medium, metadata or other attribute of information from which “signal” can be distinguished from “noise” to reveal meaning. For example, consider audio from which one's tone of voice can be discerned in addition to spoken words, permitting inferences of meaning, such as the relative importance of particular information. A phone conversation with an agitated key supplier regarding an overdue invoice might result in the escalation of that payment request (as contrasted with other similar requests). Similarly, a customer service agent might discern far more understanding of a customer's problem from a phone call than from a text chat. Humans frequent