A system and method for patent mapping are provided. The system comprises a database of patent sets and a database of patents, each patent stored in the database of patents associated with one or more patent sets stored in the database of patent sets; and one or more modules to perform a portion of one or more of the following: receive input identifying a set of keyword source claims associated with a first patent set; automatically extract at least one keyword from the source claims; receive or formulate a search query associated with the first patent set, the search query including the at least one keyword; search the entire first patent set as a function of the search query; generate search results, the search results including one or more patent claims associated with the search query; and map the one or more patent claims to a patent concept.

A system and method for patent mapping are provided. The system comprises a database of patent sets and a database of patents, each patent stored in the database of patents associated with one or more patent sets stored in the database of patent sets; and one or more modules to perform a portion of one or more of the following: receive input identifying a set of keyword source claims associated with a first patent set; automatically extract at least one keyword from the source claims; receive or formulate a search query associated with the first patent set, the search query including the at least one keyword; search the entire first patent set as a function of the search query; generate search results, the search results including one or more patent claims associated with the search query; and map the one or more patent claims to a patent concept.

A system, method, and computer-readable medium are disclosed for performing a lexicon construction operation. The lexicon construction operation includes: identifying a corpus, the corpus comprising a plurality of training events, each of the plurality of training events comprising a term; grouping terms from the plurality of training events into topic clusters; analyzing the plurality of topic clusters, the analyzing providing a plurality of classified clusters; and, deriving a plurality of learned lexicons from the plurality of classified clusters.

A system, method, and computer-readable medium are disclosed for performing a lexicon construction operation. The lexicon construction operation includes: identifying a corpus, the corpus comprising a plurality of training events, each of the plurality of training events comprising a term; grouping terms from the plurality of training events into topic clusters; analyzing the plurality of topic clusters, the analyzing providing a plurality of classified clusters; and, deriving a plurality of learned lexicons from the plurality of classified clusters.

Artificial intelligence (AI) technology can be used in combination with composable communication goal statements to facilitate a user's ability to quickly structure story outlines using “explanation” communication goals in a manner usable by an NLG narrative generation system without any need for the user to directly author computer code. This AI technology permits NLG systems to determine the appropriate content for inclusion in a narrative story about a data set in a manner that will satisfy a desired explanation communication goal such that the narratives will express various ideas that are deemed relevant to a given explanation communication goal.

A concept associated with a feature used in machine learning model can be determined, the feature extracted from a first data source. A second data source containing the concept can be identified. An additional feature can be generated by performing a natural language processing on the second data source. The feature and the additional feature can be merged. A second machine learning model can be generated, which use the merged feature. A prediction result of the first machine learning model can be compared with a prediction result of the second machine learning model relative to ground truth data, to evaluate effective of the merged feature. Based on the evaluated effectiveness, the feature can be augmented with the merged feature in machine learning.

A virtual agent can implement a “chatbot” to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events.

A virtual agent can implement a “chatbot” to provide output based on predictive/prescriptive models for incidents. The virtual agent can integrate with natural language processor for text analysis and summary report generation. The virtual agent can integrate with cognitive search to enable processing of search requests and retrieval of search results. The virtual agent uses computing processes with self-learning systems that use data mining, pattern recognition and natural language processing to mimic the way the human brain works. The virtual agent provides an automated IT system that is capable of resolving incidents without requiring human assistance. The virtual agent can display condensed summaries of a large amount of data and can link the summaries to predictive models and operational risk models to identify risk events and provide summaries of those events.

Systems and methods are disclosed herein for generating supplemental content for media content. One disclosed technique herein generates for display a page of an electronic book. A noun, and a word contextually related to the noun, are identified from the displayed page of the electronic book. Content structures are searched for a content structure that includes a matching object having an object name matching the noun. The content structure includes objects, where each object has attribute table entries. Upon finding an identified attribute table entry of the matching object that matches the related word, a new content structure is generated. The new content structure includes the matching object and the identified attribute table entry. A content segment is generated for output (e.g., for display on the electronic book) based on the new content structure.

Systems and methods are disclosed herein for generating supplemental content for media content. One disclosed technique herein generates for display a page of an electronic book. A noun, and a word contextually related to the noun, are identified from the displayed page of the electronic book. Content structures are searched for a content structure that includes a matching object having an object name matching the noun. The content structure includes objects, where each object has attribute table entries. Upon finding an identified attribute table entry of the matching object that matches the related word, a new content structure is generated. The new content structure includes the matching object and the identified attribute table entry. A content segment is generated for output (e.g., for display on the electronic book) based on the new content structure.