Abstract: Described is a system for identification of correlations in customer observables (COs). The system extracts key phrases representing COs from textual inputs from multiple data sources, wherein the COs are related to a consumer product. A unified hypergraph is constructed that models co-occurrences of COs. The unified hypergraph includes nodes and types of hyperedges connecting the nodes, where COs are represented by nodes and data sources are represented by different types of hyperedges. Each node of the unified hypergraph is embedded into a latent feature space. The unified hypergraph is partitioned into clusters within the latent feature space, where each cluster contains correlated CO data. The correlated CO data from a cluster are used to generate and provide targeted messages specific to the consumer product to a display device.

Abstract: Described is a system for identification of correlations in customer observables (COs). The system extracts key phrases representing COs from textual inputs from multiple data sources, wherein the COs are related to a consumer product. A unified hypergraph is constructed that models co-occurrences of COs. The unified hypergraph includes nodes and types of hyperedges connecting the nodes, where COs are represented by nodes and data sources are represented by different types of hyperedges. Each node of the unified hypergraph is embedded into a latent feature space. The unified hypergraph is partitioned into clusters within the latent feature space, where each cluster contains correlated CO data. The correlated CO data from a cluster are used to generate and provide targeted messages specific to the consumer product to a display device.

Abstract: A system and method of modifying a vehicle service database includes: accessing a database containing previously-received symptom text that has been associated with a vehicle identifier and one or more vehicular service solutions for the previously-received symptom text; determining a statistical likelihood that one or more additional vehicular service solutions apply to previously-received symptom text based on a correlation between the previously-received symptom text and additional vehicular service solutions; determining that the statistical likelihood is above a predetermined threshold; and associating the previously-received symptom text with the additional vehicular service solutions.

Abstract: Methods and systems are provided for automatically comparing, combining and fusing vehicle data. First data is obtained pertaining to a first plurality of vehicles. Second data is obtained pertaining to a second plurality of vehicles. One or both of the first data and the second data include abbreviated terms. The abbreviated terms are disambiguating at least in part by identifying, from a domain ontology stored in a memory, respective basewords that are associated with each of the abbreviated terms, filtering the basewords, performing a set intersection of the basewords, and calculating posterior probabilities for the basewords based at least in part on the filtering and the set intersection. The first data and the second data are combined, via a processor, based on semantic and syntactic similarity between respective data elements of the first data and the second data and the disambiguating of the abbreviated terms.

Abstract: A system and method of modifying a vehicle service database includes: accessing a database containing previously-received symptom text that has been associated with a vehicle identifier and one or more vehicular service solutions for the previously-received symptom text; determining a statistical likelihood that one or more additional vehicular service solutions apply to previously-received symptom text based on a correlation between the previously-received symptom text and additional vehicular service solutions; determining that the statistical likelihood is above a predetermined threshold; and associating the previously-received symptom text with the additional vehicular service solutions.

Abstract: A method of generating ontology models from requirement documents and software and performing consistency checks among requirement documents and software code utilizing ontology models. Terms in the plurality of requirement documents obtained from a database are identified. A processor assigns a part-of-speech tag to each term. The part-of-speech tag indicates a grammatical use of each term in the requirement documents. The processor classifies each term based on the part-of-speech tags. The classification identifies whether the each term is a part, symptom, action, event, or failure mode to constitute an ontology. The processor constructs an ontology-based consistency engine as a function of the ontologies. A consistency check is performed by applying the ontology-based consistency engine between ontologies extracted from two context documents. Inconsistent terms are identified between the context documents. At least one of the context documents having inconsistent terms is corrected.

Abstract: A method of automatic identifying linking relationships of requirements in a plurality of requirement documents. Terms in the plurality of requirement documents are identified. A part-of-speech tag is assigned to each term. Each identified term is selected as a focal term. Co-occurring terms within a predetermined distance of the selected focal term are determined. A linking relationship probability is calculated for each co-occurring term associated with the selected focal term. The selected focal terms and associated co-occurring terms between the plurality of requirement documents are compared. A degree of linking relationship is identified between two requirements as a function of a comparison between selected focal terms and the associated co-occurring terms between the plurality of requirement documents. An analysis report identifying the degree of linking relationships between two respective requirements is output.

Abstract: A method of automatically developing an ontology for product function and failure mode documentation for an apparatus. The apparatus is identified. A function-flow model is generated for the identified apparatus for identifying a composite structure of the apparatus. Functions and failure modes associated with the identified apparatus are enumerated. Failure data is obtained from a plurality of heterogeneous data sources. A semantic similarity module is applied to the enumerated failures by comparing a plurality of documents between the data sources. The semantic similarity module bridges a variety of terms used in the heterogeneous data to describe a respective failure. Failures associated with the enumerated apparatus functions are extracted from the plurality of documents between heterogeneous data sources. A composite of related terminology is generated for each identified failure mode.

Abstract: A method of automatically developing an ontology for product function and failure mode documentation for an apparatus. The apparatus is identified. A function-flow model is generated for the identified apparatus for identifying a composite structure of the apparatus. Functions and failure modes associated with the identified apparatus are enumerated. Failure data is obtained from a plurality of heterogeneous data sources. A semantic similarity module is applied to the enumerated failures by comparing a plurality of documents between the data sources. The semantic similarity module bridges a variety of terms used in the heterogeneous data to describe a respective failure. Failures associated with the enumerated apparatus functions are extracted from the plurality of documents between heterogeneous data sources. A composite of related terminology is generated for each identified failure mode.

Abstract: A method of automatic identifying linking relationships of requirements in a plurality of requirement documents. Terms in the plurality of requirement documents are identified. A part-of-speech tag is assigned to each term. Each identified term is selected as a focal term. Co-occurring terms within a predetermined distance of the selected focal term are determined. A linking relationship probability is calculated for each co-occurring term associated with the selected focal term. The selected focal terms and associated co-occurring terms between the plurality of requirement documents are compared. A degree of linking relationship is identified between two requirements as a function of a comparison between selected focal terms and the associated co-occurring terms between the plurality of requirement documents. An analysis report identifying the degree of linking relationships between two respective requirements is output.

Abstract: Methods and systems are provided for automatically comparing, combining and fusing vehicle data. First data is obtained pertaining to a first plurality of vehicles. Second data is obtained pertaining to a second plurality of vehicles. The first data and the second data are compared and combined based on syntactic similarity between respective data elements of the first data and the second data collected during different stages of vehicle life cycle development.

Abstract: A warranty database stores service repair verbatims. An ontology database that specifies relationships between service terms includes linking relationships between vehicle terminology and cluster categories. The ontology database is reconfigurable for allowing a user to add, delete, and modify contents within the ontology database. A verbatim extraction tool extracts service repair verbatims from the warranty database as function of user selected parameters and a user selected ontology. The user selected ontology is a subset of the ontology database. The service verbatims are segregated into a plurality of cluster categories as a function of the selected parameters and the user selected ontology. A report generating device selectively generated reports based on segregating service verbatims into a plurality of cluster categories.

Abstract: A document may be received at a processing module. One or more tags may be applied to the document, each tag applied to a term, each tag representing a part of speech. One or more terms may be extracted from the document based on the tag. A weighting assignment parameter may be determined for each of the one or more extracted terms. Based on the weighting assignment parameter associated with each of the extracted terms, it may be determined whether the domain ontology includes the one or more extracted terms. If the domain ontology does not include the one or more extracted terms, the domain ontology may be augmented such that the domain ontology comprises the one or more extracted terms.

Abstract: A method for extracting data from service repair verbatims in a vehicle service reporting system. Each service repair verbatim includes a technician's comments concerning a part, a symptom associated with the part, and a repair action associated with the symptom. Each service repair verbatim includes information relating to an identified problem with at least one vehicle part. A diagnostic and prognostic ontology database is provided that is structured by vehicle part classification, a vehicle part sub-class classification, and a relationship classification, wherein the relationship classification includes symptom relationships and action relationships. Each of the service repair verbatims are reconstructed utilizing the diagnostic and prognostic ontology database. Combinations of information are extracted from the reconstructed service repair verbatims as a function of user input criteria. A frequency is determined of each combination extracted in the reconstructed service repair verbatims.

Abstract: A method and system for splitting a text document into individual sentences using sentence boundary detection, and establishing co-relationships between terms which are present in the same sentence. A document corpus, or collection of text records, is provided, containing text with terms to be extracted. The text records in the document corpus are divided into individual sentences, using a set of rules for sentence boundary detection. The individual sentences are then analyzed to extract and correlate terms, such as parts and symptoms, symptoms and actions, or parts and failure modes. The correlated terms are then validated based on frequency of occurrence, with term pairs being considered valid if their frequency of occurrence exceeds a minimum frequency threshold. The validated term correlations can be used for fault model development, document classification, and document clustering.

Abstract: A document may be received at a processing module. One or more tags may be applied to the document, each tag applied to a term, each tag representing a part of speech. One or more terms may be extracted from the document based on the tag. A weighting assignment parameter may be determined for each of the one or more extracted terms. Based on the weighting assignment parameter associated with each of the extracted terms, it may be determined whether the domain ontology includes the one or more extracted terms. If the domain ontology does not include the one or more extracted terms, the domain ontology may be augmented such that the domain ontology comprises the one or more extracted terms.

Abstract: A method and system for splitting a text document into individual sentences using sentence boundary detection, and establishing co-relationships between terms which are present in the same sentence. A document corpus, or collection of text records, is provided, containing text with terms to be extracted. The text records in the document corpus are divided into individual sentences, using a set of rules for sentence boundary detection. The individual sentences are then analyzed to extract and correlate terms, such as parts and symptoms, symptoms and actions, or parts and failure modes. The correlated terms are then validated based on frequency of occurrence, with term pairs being considered valid if their frequency of occurrence exceeds a minimum frequency threshold. The validated term correlations can be used for fault model development, document classification, and document clustering.

Abstract: A method and system for developing fault models from unstructured text documents, such as text verbatim descriptions from customers and service technicians. An ontology, or data model, and heuristic rules are used to identify and extract descriptive terms from the text verbatim document. The descriptive terms are then classified into types, including symptoms, failure modes, and parts. Like-meaning but differently-worded descriptive terms are then merged using text similarity scoring techniques. The resultant symptoms, failure modes, parts, and correlations are then assembled into a fault model, which can be used for real-time fault diagnosis onboard a vehicle, or off-board at service shops.

Abstract: A method and system for developing reliability models from unstructured text documents, such as text verbatim descriptions from service technicians. An ontology, or data model, and heuristic rules are used to identify and extract failure modes and parts from the text verbatim comments associated with specific labor codes from service events. Like-meaning but differently-worded terms are then merged using text similarity scoring techniques. The resultant failure modes are used to create enhanced reliability models, where component reliability is predicted in terms of individual failure modes instead of aggregated for the component. The enhanced reliability models provide improved reliability prediction for the component, and also provides insight into aspects of the component design which can be improved in the future.

Abstract: A method is provided for extracting data from service repair verbatims in a vehicle service reporting system. Each service repair verbatim includes a technician's comments concerning a part, a symptom associated with the part, and a repair action associated with the symptom. Each service repair verbatim includes information relating to an identified problem with at least one vehicle part. A diagnostic and prognostic ontology database is provided that is structured by vehicle part classification, a vehicle part sub-class classification, and a relationship classification, wherein the relationship classification includes symptom relationships and action relationships. Each of the service repair verbatims are reconstructed utilizing the diagnostic and prognostic ontology database. Combinations of information are extracted from the reconstructed service repair verbatims as a function of user input criteria. A frequency is determined of each combination extracted in the reconstructed service repair verbatims.