{"type":"DefensivePublication","slug":"507dd66d","title":"A system and method for managing licensing terms between parties","abstract":"A system and method for managing licensing terms between parties are disclosed. The system employs embedding similarity analysis to compare received licensing terms against a reference set of fraud examples. An embedding representation of the licensing terms is generated and then compared to the reference set of fraud examples, or their corresponding embedding representations. A similarity score is calculated based on this comparison. If the calculated similarity score exceeds a predetermined threshold, such as 0.85, the licensing transaction is flagged. This flagging indicates a potential issue, such as fraud, associated with the licensing transaction, thereby facilitating automated identification of problematic licensing transactions.","body_markdown":"## Field\n\nField of Invention\n\nThe present invention relates to data processing, contract management, and fraud detection. More particularly, the present invention relates to systems and methods for managing licensing terms and identifying potentially problematic transactions using embedding similarity analysis.\n\n## Background\n\nBackground of the Invention\n\nManaging licensing terms between parties can involve complex processes and a large volume of data. Identifying potential issues, such as fraudulent terms or non-compliance, within these transactions is a persistent challenge. Conventional methods for identifying such issues may rely on manual review or rule-based systems that can be time-consuming, prone to error, or lack the ability to detect nuanced patterns. There is a need for improved systems and methods that can efficiently and automatically identify potentially problematic licensing transactions.\n\n## Summary\n\nSummary of the Invention\n\nIn one embodiment, a system and method for managing licensing terms between parties are disclosed. The system utilizes embedding similarity analysis to compare received licensing terms against a reference set of fraud examples. An embedding representation of the licensing terms is generated. This embedding representation is then compared to the reference set of fraud examples, or their corresponding embedding representations, to calculate a similarity score. If the calculated similarity score exceeds a predetermined threshold, such as 0.85, the licensing transaction is flagged. This flagging indicates a potential issue, such as fraud, associated with the licensing transaction, thereby enabling automated identification of problematic licensing transactions.\n\n## Detailed Description\n\nDetailed Description\n\nIn the following description, for purposes of explanation, specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention can be practiced without these specific details.\n\nIn one embodiment, a system for managing licensing terms between parties is disclosed. The system is configured to process licensing terms to identify potential issues, such as fraud. The system operates by employing embedding similarity analysis against a known set of fraud examples.\n\nThe system comprises various components configured to perform a series of operations. In operation, the system first receives licensing terms from one or more parties. These licensing terms may be in various forms, such as textual contracts, agreements, or other structured or unstructured data describing the terms of a license.\n\nUpon receiving the licensing terms, the system is configured to generate an embedding representation of the received licensing terms. An embedding representation is a numerical vector that captures semantic or contextual information of the licensing terms, allowing for mathematical comparisons. The method of generating this embedding representation may utilize various machine learning or natural language processing techniques, as known in the art [clarification needed for specific embedding technique].\n\nSubsequently, the system compares the generated embedding representation of the received licensing terms to a reference set of fraud examples. This reference set comprises examples of known fraudulent licensing terms, or more specifically, their pre-generated embedding representations. The comparison aims to determine a measure of similarity between the current licensing terms and the known fraud examples.\n\nThe system calculates a similarity score based on this comparison. The similarity score quantifies how closely the received licensing terms' embedding matches the embeddings within the reference set of fraud examples. In one specific embodiment, if the calculated similarity score exceeds a predetermined threshold, the licensing transaction is flagged. For example, the predetermined threshold may be set to 0.85. If the similarity score is greater than 0.85, the system flags the transaction.\n\nFlagging the licensing transaction may involve various actions, such as generating an alert, sending a notification to a user or administrator, quarantining the transaction for further manual review, or preventing the transaction from proceeding. The flagging indicates a potential for fraud or other undesirable characteristics identified by the similarity to the reference set of fraud examples.\n\nThus, the disclosed system provides an automated and efficient mechanism for identifying potentially problematic licensing transactions by leveraging the power of embedding similarity analysis.\n\n## Abstract\n\nA system and method for managing licensing terms between parties are disclosed. The system employs embedding similarity analysis to compare received licensing terms against a reference set of fraud examples. An embedding representation of the licensing terms is generated and then compared to the reference set of fraud examples, or their corresponding embedding representations. A similarity score is calculated based on this comparison. If the calculated similarity score exceeds a predetermined threshold, such as 0.85, the licensing transaction is flagged. This flagging indicates a potential issue, such as fraud, associated with the licensing transaction, thereby facilitating automated identification of problematic licensing transactions.\n\n## Claims (illustrative — NOT for monopoly)\n\n1. 1. A method for managing licensing terms, comprising:\n  receiving licensing terms between parties;\n  generating an embedding representation of the received licensing terms;\n  comparing the embedding representation of the received licensing terms to a reference set of embedding representations of fraud examples;\n  calculating a similarity score between the embedding representation of the received licensing terms and the reference set of embedding representations of fraud examples; and\n  flagging a licensing transaction if the calculated similarity score exceeds a predetermined threshold.\n2. 2. The method of claim 1, wherein the predetermined threshold is 0.85.\n3. 3. The method of claim 1, wherein flagging the licensing transaction comprises indicating a potential for fraud associated with the licensing transaction.\n4. 4. A system for managing licensing terms, comprising:\n  a processing unit configured to:\n    receive licensing terms between parties;\n    generate an embedding representation of the received licensing terms;\n    compare the embedding representation of the received licensing terms to a reference set of embedding representations of fraud examples;\n    calculate a similarity score between the embedding representation of the received licensing terms and the reference set of embedding representations of fraud examples; and\n    flag a licensing transaction if the calculated similarity score exceeds a predetermined threshold.\n","published_at":"2026-06-16T09:29:04.441730Z","publication_url":"https://depatent.com/p/507dd66d","license":"CC0-1.0","authors":[],"anonymous":true,"cpc_codes":[],"ai_disclosure":"Drafted with AI assistance · not legal advice","proofs":[{"kind":"doi","state":"missing","value":null,"href":null},{"kind":"rfc3161","state":"missing","value":null,"href":null},{"kind":"archive","state":"present","value":"web.archive.org","href":"https://web.archive.org/web/20261030022648/https://depatent.com/p/507dd66d"}]}