Abstract: A method is provided to implement a conversational system with artifact generation. A middleware component receives a user input and determines whether there is sufficient information in the user input and a conversation space in a context storage of the conversational system to identify user intent associated with the user input. Responsive to the middleware component determining there is not sufficient information to identify user intent, a communications handler component sends a natural language query to an external data source via a natural language query (NLQ) interface and receives a natural language response from the external data source. The middleware component updates the conversation space based on the natural language response and returns a user response based on the natural language response.

Abstract: A computer-implemented method for generating an ontology-driven conversational interface includes generating an ontology from a description of a domain schema of a Data Analysis (DA) model, in which the DA model is a defined in terms of quantifiable, qualifying or categorical entities and their relationships as described by the domain schema. Conversational artifacts of a conversation space including a conversational pattern framework are generated by extracting DA-related intents, entities, and a dialog from the generated ontology for the conversational interface. A dialog logic table maps DA-related patterns to intents, extracted quantifiable, qualifying or categorical attributes to entities, and the dialog to user-prompts for one or more parameters in an identified DA pattern. The conversation space is integrated with at least one of an external data source or an analytics platform that stores and processes data.

Abstract: A method is provided to implement a conversational system with artifact generation. A middleware component receives a user input and determines whether there is sufficient information in the user input and a conversation space in a context storage of the conversational system to identify user intent associated with the user input. Responsive to the middleware component determining there is not sufficient information to identify user intent, a communications handler component sends a natural language query to an external data source via a natural language query (NLQ) interface and receives a natural language response from the external data source. The middleware component updates the conversation space based on the natural language response and returns a user response based on the natural language response.

Abstract: One embodiment provides a method for developing a text analytics program for extracting at least one target concept including: utilizing at least one processor to execute computer code that performs the steps of: initiating a development tool that accepts user input to develop rules for extraction of features of the at least one target concept within a dataset comprising textual information; developing, using the rules for feature extraction, an evaluation dataset comprising at least one document annotated with the at least one target concept to be extracted by the text analytics program; creating, using the rules for feature extraction, a rule-based annotator to extract the at least one target concept; training, using the evaluation dataset, a machine-learning annotator to extract the at least one target concept within the dataset; combining the rule-based annotator and the machine learning annotator to form a combined annotator; evaluating, using the evaluation dataset, extraction performance of the combine

Abstract: One embodiment provides a method for developing a text analytics program for extracting at least one target concept including: utilizing at least one processor to execute computer code that performs the steps of: initiating a development tool that accepts user input to develop rules for extraction of features of the at least one target concept within a dataset comprising textual information; developing, using the rules for feature extraction, an evaluation dataset comprising at least one document annotated with the at least one target concept to be extracted by the text analytics program; creating, using the rules for feature extraction, a rule-based annotator to extract the at least one target concept; training, using the evaluation dataset, a machine-learning annotator to extract the at least one target concept within the dataset; combining the rule-based annotator and the machine learning annotator to form a combined annotator; evaluating, using the evaluation dataset, extraction performance of the combine

Abstract: A method for analyzing predefined subject matter in a patent database being for use with a set of target patents, each target patent related to the predefined subject matter, the method comprising: creating a feature space based on frequently occurring terms found in the set of target patents; creating a partition taxonomy based on a clustered configuration of the feature space; editing the partition taxonomy using domain expertise to produce an edited partition taxonomy; creating a classification taxonomy based on structured features present in the edited partition taxonomy; creating a contingency table by comparing the edited partition taxonomy and the classification taxonomy to provide entries in the contingency table; and identifying all significant relationships in the contingency table to help determine the presence of any white space.

Abstract: A method and analytics tools for information mining incorporating domain specific knowledge and conceptual structures are disclosed, the method including: providing a first set of documents related to a first topic of interest; using a first taxonomy to categorize the first set of documents into a set of categories; providing a second set of documents related to a second topic of interest; categorizing the second set of documents according to the set of categories of the first set of documents; using an element of domain knowledge to re-categorize the first set of documents; and examining a category to identify a document of interest.

Abstract: A method and analytics tools for locating experts with specific sets of expertise are disclosed, the method including providing a collection of documents P0; generating categories representing fields of expertise derived from the collection of documents P0; refining the taxonomy of the categories by applying user domain knowledge; extracting structured fields from the collection of documents P0; constructing a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and using the contingency table to identify a set of experts having a related expertise. The method may also include a network graph analysis that aids visualization of the relationship between people and expertise.

Abstract: A vectorization process is employed in which chemical identifier strings are converted into respective vectors. These vectors may then be searched to identify molecules that are identical or similar to each other. The dimensions of the vector space can be defined by sequences of symbols that make up the chemical identifier strings. The International Chemical Identifier (InChI) string defined by the International Union of Pure and Applied Chemistry (IUPAC) is particularly well suited for these methods.

Abstract: A data warehouse is created using an input file that can contain sub-documents of different formats. A root document model including path names to all nodes among the sub-documents is generated, and a table is generated with columns being derived from the path names of the root model. The sub-documents are shredded to populate the table. Then, the dimensions of the data warehouse are defined by selecting respective columns. A routine such as a DDL may then be generated to populate the data warehouse with data.

Abstract: A vectorization process is employed in which chemical identifier strings are converted into respective vectors. These vectors may then be searched to identify molecules that are identical or similar to each other. The dimensions of the vector space can be defined by sequences of symbols that make up the chemical identifier strings. The International Chemical Identifier (InChI) string defined by the International Union of Pure and Applied Chemistry (IUPAC) is particularly well suited for these methods.

Abstract: A vectorization process is employed in which chemical identifier strings are converted into respective vectors. These vectors may then be searched to identify molecules that are identical or similar to each other. The dimensions of the vector space can be defined by sequences of symbols that make up the chemical identifier strings. The International Chemical Identifier (InChI) string defined by the International Union of Pure and Applied Chemistry (IUPAC) is particularly well suited for these methods.

Abstract: A method for analyzing predefined subject matter in a patent database being for use with a set of target patents, each target patent related to the predefined subject matter, the method comprising: creating a feature space based on frequently occurring terms found in the set of target patents; creating a partition taxonomy based on a clustered configuration of the feature space; editing the partition taxonomy using domain expertise to produce an edited partition taxonomy; creating a classification taxonomy based on structured features present in the edited partition taxonomy; creating a contingency table by comparing the edited partition taxonomy and the classification taxonomy to provide entries in the contingency table; and identifying all significant relationships in the contingency table to help determine the presence of any white space.

Abstract: A method and system for interesting relationships in text documents includes generating a dictionary of keywords in the text documents, forming categories of the text documents using the dictionary and an automated algorithm, counting occurrences of the structured variables, categories and structured variable/category combinations in the text documents, and calculating probabilities of occurrences of the structured variable/category combinations.

Abstract: A method and analytics tools for locating experts with specific sets of expertise are disclosed, the method including providing a collection of documents P0; generating categories representing fields of expertise derived from the collection of documents P0; refining the taxonomy of the categories by applying user domain knowledge; extracting structured fields from the collection of documents P0; constructing a contingency table having a first axis defined by the extracted structured fields and a second axis defined by the categories; and using the contingency table to identify a set of experts having a related expertise. The method may also include a network graph analysis that aids visualization of the relationship between people and expertise.

Abstract: A method is disclosed for use with at least one initial document describing a technical concept suitable for licensing, the method comprising: retrieving a set of intellectual property documents from a data warehouse; partitioning the set of intellectual property documents into a plurality of document categories; classifying the set of intellectual property documents by an industry parameter; constructing a contingency table that includes a listing of industry classifications for each of the document categories, and identifying documents within a particular one of the document categories that have different industry classifications so as to identify at least one potential new licensee industry of the technical concept described in the initial document.

Abstract: A method is disclosed for use with at least one initial document describing a technical concept suitable for licensing, the method comprising: retrieving a set of intellectual property documents from a data warehouse; partitioning the set of intellectual property documents into a plurality of document categories; classifying the set of intellectual property documents by an industry parameter; constructing a contingency table that includes a listing of industry classifications for each of the document categories, and identifying documents within a particular one of the document categories that have different industry classifications so as to identify at least one potential new licensee industry of the technical concept described in the initial document.

Abstract: A method and structure converts a document corpus containing an ordered plurality of documents into a compact representation in memory of occurrence data, where the representation is to be based on a dictionary previously developed for the document corpus and where each term in the dictionary has associated therewith a corresponding unique integer. The method includes developing a first vector for the entire document corpus, the first vector being a sequential listing of the unique integers such that each document in the document corpus is sequentially represented in the listing according to the occurrence in the document of the corresponding dictionary terms. A second vector is also developed for the entire document corpus and indicates the location of each of the document's representation in the first vector.

Abstract: A method is disclosed for use with at least one initial document describing a technical concept suitable for licensing, the method comprising: retrieving a set of intellectual property documents from a data warehouse; partitioning the set of intellectual property documents into a plurality of document categories; classifying the set of intellectual property documents by an industry parameter; constructing a contingency table that includes a listing of industry classifications for each of the document categories, and identifying documents within a particular one of the document categories that have different industry classifications so as to identify at least one potential new licensee industry of the technical concept described in the initial document.

Abstract: A structure and method for automatically creating a dictionary for clustering text documents performs a first pass for each of the documents to determine a frequency of each word in each of the documents, creates a Hashtable of most frequently occurring words in the documents, performs a second pass for each of the documents to determine a frequency of phrases in each of the documents that contain only words in the Hashtable, adds the most frequently occurring phrases to the Hashtable, and outputting outputs the most frequently occurring words and the most frequently occurring phrases as the dictionary.