公开(公告)号：US20230342114A1

公开(公告)日：2023-10-26

申请号：US17729983

申请日：2022-04-26

Applicant: Accenture Global Solutions Limited

Inventor： Suma S. Joshi ,  Subhashini LakshmiNarayanan ,  Shantanu Shirish Sahasrabudhe ,  Rajashree Chandrashekar ,  Gopali Raval Contractor

IPC: G06F8/41 ,  G06F8/10

CPC classification number: G06F8/10 ,  G06F8/427 ,  G06F8/433 ,  G06F8/436

Abstract: An automated system and method of converting legacy decision logic to a target format. The legacy files are received by the decision logic translation system, which outputs the business rule content in a standard rule structure, according to the selected target format. The process involves decision logic-based rule extraction. In general, methods or processes for extracting business rules have been difficult to reproduce and do not present clearly the extracted rules regarding the concepts of business rules, their composition and categorization. These drawbacks lead to incomplete extraction of rules and massive manual effort to achieve a complete extraction and verification. In contrast, the proposed system overcomes these drawbacks, and outputs files that can be easily used to migrate the business rules to a new platform.

公开(公告)号：US20230259812A1

公开(公告)日：2023-08-17

申请号：US17671092

申请日：2022-02-14

Applicant: Accenture Global Solutions Limited

Inventor： Bhushan Gurmukhdas Jagyasi ,  Siva Rama Sarma Theerthala ,  Saurabh Pashine ,  Soumit Bhowmick ,  Gopali Raval Contractor

IPC: G06N20/00 ,  G06N3/12

CPC classification number: G06N20/00 ,  G06N3/126

Abstract: This application discloses a system and method for federated collaborative machine learning model development using local training datasets that are not shared. An adaptive and evolutionary approach is used to select local training nodes that are most fit from one training round to the next training round to optimize an overall cost and performance function for the federated learning, to cross-over model architecture between local training nodes, and to perform model architecture mutation within local training nodes. The local training nodes are further clustered to account for the inhomogeneity in the local datasets. Such adaptive, evolutionary, and collaborative federated learning thus provides cost-effective and high-performance model development.

公开(公告)号：US20230072297A1

公开(公告)日：2023-03-09

申请号：US17461232

申请日：2021-08-30

Applicant: Accenture Global Solutions Limited

Inventor： Subhashini Lakshminarayanan ,  Gopali Raval Contractor ,  Rajashree Chandrashekar

IPC: G06Q50/18 ,  G06F40/279 ,  G06N5/04

Abstract: A knowledge graph based reasoning recommendation system and method may analyze past concluded legal cases to find patterns and predict the outcomes of new legal cases before or during litigation. These patterns and outcomes may be used to determine a recommendation for a legal strategy. Input documents and/or enterprise claim data from past concluded cases may be combined and processed to calculate an association rule for the legal outcome associated with one or more of the claim type, counsel, and judge for the group of similar cases based on the analysis of individual cases within the group. Features extracted from the input documents from the past concluded cases and the calculated association rules may be incorporated into a knowledge graph, along with features extracted from input documents from new legal cases. A Policy-Guided Path Reasoning (PGPR) may be applied over the knowledge graph to calculate which legal strategy to recommend. The recommended legal strategy, as well as the reasoning for recommending the legal strategy may be displayed to a user.