Primal Fusion Inc.

Peter Joseph Sweeney

Ihab Francis Ilyas

Alexander David Black

Robert Charles Good

Nadiya Yampolska

Anne Jude Hunt

Robert Barlow-Busch

Jean-Paul Dupuis

Mathew Whitney Wilson

Zhengrong Yao

David Goodwin

David Janik-Jones

Naim Khan

Mohamed A Soliman

Wu Zhou

Wayne Oldford

Mark William Connolly

Robert George Barlow-Busch

Ihab F Ilyas

Disclosed is a system and method for enhancing value for advertisers by helping them select and initiate the purchase of an advertisement associated with an advertising (ad) words or phrases that have strong semantic relationships to a given context, but which are not necessarily the most popular ad words or phrases with the highest costs. Advertisements associated with ad words or phrases that have strong semantic relationships to a given context, and yet are still cost effective in that their calculated value exceeds the costs of purchasing the ad keywords, are bid for and bought. In an embodiment, the system and method may be adapted to automatically purchase advertisements associated with ad words or phrases when the ad words or phrases fall within a desired price range based on their calculated value. As the prices of advertisements associated with these words or phrases fluctuate over time based on their popularity of the words or phrases, the automated bidding and buying of advertisements may be used to purchase advertisements associated with words or phrases at a price desirable to a given ad purchaser. By automatically purchasing such advertisements, the return on investment (ROI) for an advertiser may be improved.

System and method for purchasing advertisements associated with words and phrases

Systems and methods are provided for generating training data for a machine-learning classifier. A knowledge representation synthesized based on an object of interest is used to assign labels to content items. The labeled content items can be used as training data for training a machine learning classifier. The labeled content items can also be used as validation data for the classifier.

System and method for generating training data for machine learning classifier

Techniques for customizing knowledge representation systems including identifying, based on a plurality of concepts in a knowledge representation (KR), a group of one or more concepts relevant to user context information, and providing the identified group of one more concepts to a user. The KR may include a combination of modules. The modules may include a kernel and a customized module customized for the user. The kernel may accessible via a second KR.

Methods and devices for customizing knowledge representation systems

Computer-implemented systems find methods for synthesis of concept definitions and concept relationships from a domain of data, utilizing different semantic processing protocols such formal concept analysis and faceted classification synthesis from existing domain concepts that have a confidence gradient built into them. A cognitive or an input agent provides an input of an active concept which is matched against existing domain concepts. The resultant pool of relevant domain concepts is then used to derive virtual concept definitions using a semantic processing protocol. The derivation is then overlaid with a concept of relative proximity of an attribute from another within an attribute set. An additional layer of coherence is given by the relative proximity measure. The end result is a pool of related virtual concept definitions in a tree structure.

Systems and methods for semantic concept definition and semantic concept relationship synthesis utilizing existing domain definitions

This invention relates generally to classification systems. More particularly this invention relates to a system, method, and computer program to dynamically generate a domain of information synthesized by a classification system or semantic network. The invention discloses a method, system, and computer program providing a means by which an information store comprised of knowledge representations, such as a web site comprised of a plurality of web pages or a database comprised of a plurality of data instances, may be optimally organized and accessed based on relational links between ideas defined by one or more thoughts identified by an agent and one or more ideas embodied by the data instances. Such means is hereinafter referred to as a 'thought network'.

Method, system, and computer program for user-driven dynamic generation of semantic networks and media synthesis

A method of semantic searching. The method may include receiving a first search query, obtaining a disambiguation term for semantically disambiguating the first search query, and creating, with a processor, a second search query based at least in part on the first search query and the disambiguation term. The method may also include at least one of outputting search results obtained from a search conducted based at least in part on the second search query and sending the second search query to a search service for outputting search results.

Methods and apparatus for searching of content using semantic synthesis

There is disclosed a system and method for using a knowledge representation to provide relevant information based on environmental inputs. In an embodiment, the system and method considers environmental information from members in a crowd to generate a pool of interests based on the semantic relevance concepts associated with those interests. The most prominent concepts of interest may then be the basis for presenting content to the crowd as a whole. In another embodiment, environmental inputs and other surrounding inputs are considered as a user context. The concepts may be identified as relevant from the environmental context and used to present information relevant to the user given his or her surroundings.

System and method for using a knowledge representation to provide information based on environmental inputs

Computer-implemented systems and methods for synthesis of concept definitions and concept relationships from a domain of data, utilizing different semantic processing protocols such as formal concept analysis and faceted classification synthesis from existing domain concepts that have a confidence gradient built into them. A cognitive or an input agent provides an input of an active concept which is matched against existing domain concepts. The resultant pool of relevant domain concepts is then used to derive virtual concept definitions using a semantic processing protocol. The derivation is then overlaid with a concept of relative proximity of an attribute from another within an attribute set. An additional layer of coherence is given by the relative proximity measure. The end result is a pool of related virtual concept definitions in a tree structure.

Techniques for analyzing and synthesizing complex knowledge representations (KRs) may utilize an atomic knowledge representation model including both an elemental data structure and knowledge processing rules stored as machine-readable data and/or programming instructions. One or more of the knowledge processing rules may be applied to analyze an input complex KR to deconstruct its complex concepts and/or concept relationships to elemental concepts and/or concept relationships to be included in the elemental data structure. One or more of the knowledge processing rules may be applied to synthesize an output complex KR from the stored elemental data structure in accordance with context information. Methods of populating an elemental data structure and methods of synthesizing complex KRs from the elemental data structure may depend on user models, context information, preference information, and/or preference models.

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Kitchener, Canada

Peter Joseph Sweeney