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.

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.

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.

Abstract: A device may consolidate the input data associated with vehicle insurance claims to generate processed input data. The device may process claims data, treatment data, and repair shop data to resolve ambiguities in the processed input data and to generate resolved data. The device may process the resolved data to generate related data identifying relations between persons and vehicle accidents. The device may process notes of claims adjusters and vehicle accident descriptions to extract feature data identifying features. The device may process the feature data to determine contradiction data identifying contradictions in the feature data. The device may process weather data, location data, and telematics data to determine weather conditions and locations associated with the accidents. The device may process the related data, the contradiction data, the weather conditions, and the locations to generate a knowledge graph. The device may identify a fraud ring based on the knowledge graph.

Abstract: Examples of a sentence phrasing system are provided. The system may obtain a user question from a user. The system may obtain question entailment data from a plurality of data sources. The system may implement an artificial intelligence component to identify a word index from the question entailment data and to identify a question premise from the user question. The system may implement a first cognitive learning operation to determine an answer premise corresponding to the question premise comprising a second-word data set. The system may determine a subject component corresponding to the question premise. The system may generate an object component and a predicate component from the second-word data set corresponding to the subject component. The system may generate an integrated answer relevant for resolving the user question and comprising the subject component, the object component, and the predicate component concatenated to form an answer sentence.

Abstract: A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Abstract: A domain-specific question answering system receives an end-user's question, identifies relevant information from domain-specific documents, composes a natural-language based response including the relevant information to be provided to the end-user. A knowledge base includes a domain-specific ontology and is built from data structures configured to preserve the hierarchy of the informational elements within the domain-specific documents. The question received from the end-user is analyzed to extract the features and the key phrases which are used to identify the relevant data structures. A pre-trained model that is further trained on domain-specific data is used to identify the sentences that include information responsive to the question. A response to the end-user's question is composed from the sentences. The question answering system also enables validation of the answers provided to the users' questions.

Abstract: A device may consolidate the input data associated with vehicle insurance claims to generate processed input data. The device may process claims data, treatment data, and repair shop data to resolve ambiguities in the processed input data and to generate resolved data. The device may process the resolved data to generate related data identifying relations between persons and vehicle accidents. The device may process notes of claims adjusters and vehicle accident descriptions to extract feature data identifying features. The device may process the feature data to determine contradiction data identifying contradictions in the feature data. The device may process weather data, location data, and telematics data to determine weather conditions and locations associated with the accidents. The device may process the related data, the contradiction data, the weather conditions, and the locations to generate a knowledge graph. The device may identify a fraud ring based on the knowledge graph.

Abstract: A device may receive transaction data associated with transactions. The transaction data may be associated with transaction entries that are associated with the transactions. The device may process, using a matching model, the transaction entries to classify the transaction entries into a set of matched transaction entries and a set of unmatched transaction entries. The device may update a transaction grouping model based on the set of matched transaction entries to create an updated transaction grouping model. The device may determine, using the updated transaction grouping model, that a subset of the set of unmatched transaction entries are associated with a same transaction. The device may classify the subset of the set of unmatched transaction entries as grouped transaction entries. The device may provide an indication that the grouped transaction entries and the set of matched transaction entries are reconciled transactions.

Abstract: A domain-specific question answering system receives an end-user's question, identifies relevant information from domain-specific documents, composes a natural-language based response including the relevant information to be provided to the end-user. A knowledge base includes a domain-specific ontology and is built from data structures configured to preserve the hierarchy of the informational elements within the domain-specific documents. The question received from the end-user is analyzed to extract the features and the key phrases which are used to identify the relevant data structures. A pre-trained model that is further trained on domain-specific data is used to identify the sentences that include information responsive to the question. A response to the end-user's question is composed from the sentences. The question answering system also enables validation of the answers provided to the users' questions.

Abstract: Examples of a sentence phrasing system are provided. The system may obtain a user question from a user. The system may obtain question entailment data from a plurality of data sources. The system may implement an artificial intelligence component to identify a word index from the question entailment data and to identify a question premise from the user question. The system may implement a first cognitive learning operation to determine an answer premise corresponding to the question premise comprising a second-word data set. The system may determine a subject component corresponding to the question premise. The system may generate an object component and a predicate component from the second-word data set corresponding to the subject component. The system may generate an integrated answer relevant for resolving the user question and comprising the subject component, the object component, and the predicate component concatenated to form an answer sentence.