Ip.com I, LLC

Textwise LLC

Textwise Company, LLC

Wen Ruan

Robert Solmer

William Yurich Fowlkes

Samuel C Baxter

James Thomas Durkin

David L Snyder

George B Osborne

Bo Yuan

Mary E McKenna

Natalia Victorovna Britvikhina

Clinton P Mah

Mary Louise Howatt

Maurice J Forrester

Young No

Randall Jay Calistri-Yeh

Ju Suk Lee

The present teaching relates to method, system, medium, and implementations for modeling an entity based on textual information. When information about an entity to be modeled is received, textual information related to the entity is searched, including a plurality of documents related to at least one aspect of the entity. First aggregated semantic models are obtained for the plurality of documents. Each first aggregated semantic model represents one of the plurality of documents and includes a semantic feature vector and a semantic signature. Such first aggregated semantic models are clustered into at least one group, each of which corresponds to one of the at least one aspect of the entity and includes a set of first aggregated semantic models representing semantics of documents related to the aspect of the entity. A second aggregated semantic model is then derived based on each group of first aggregated semantic models to characterize one aspect of the entity, yielding one or more second aggregated semantic models. A second aggregated semantic model for an aspect of the entity comprises an aggregated semantic feature vector and an aggregated semantic signature for characterizing the aspect of the entity and facilitating a search with respect to the aspect.

System and method for aggregated modeling, search, visualization, and summarization and applications thereof

Methods, systems, and computer program products for analyzing one or more perceived or technical problems or proposed solutions, and proposing a result are disclosed. In accordance therewith, a query is received as an input, one or more documents that are most closely semantically related to the query are retrieved, a set of concept terms derived from each of the query and the retrieved semantically related documents is obtained, a list of generic Solution Prompts, each of which generic Solution Prompt thereof includes a placeholder for insertion of a word or phrase from the set of concept terms, is provided, and a morphological analysis is applied to combine the list of generic Solution Prompts with the obtained set of concept terms to create a list of Specific Solution Prompts.

Automating solution prompts based upon semantic representation

The present teaching relates to method and system for assessing semantic distance. A query is received and processed, by a document representation constructor, to obtain a first query representation. The query is then processed by a query representation constructor to obtain a second query representation. Based on the first and second query representations, a dynamic norm is determined. Based on the second query representation, a plurality of documents are identified as being related to the query and a semantic distance between the query and the plurality of documents is determined based on the dynamic norm.

System and method for dynamically normalized semantic distance and applications thereof

Method and system for visualizing documents. N-dimensional compact representations are obtained for a set of documents. A plurality of documents are then retrieved with the corresponding N-dimensional compact representations. Each of the retrieved documents is associated with at least one concept. Each of the retrieved documents is projected to a point on a K-dimensional map based on its N-dimensional compact representation so that projected document points in the K-dimensional map maintain the relative distances among the retrieved documents in the N-dimensional space. Regions in the K-dimensional map associated with a concept are identified. A label is generated for each concept in each identified region. Then generated labels are rendered on the K-dimensional map in a corresponding region identified.

Method and system for visualizing documents

Method and system for visualizing documents. N-dimensional compact representations are obtained for a set of documents. A plurality of documents is retrieved with corresponding N-dimensional compact representations and each of the retrieved documents is associated with one or more concepts. Each retrieved document is projected to a point on a K-dimensional map based on its N-dimensional compact representation so that projected document points in the K-dimensional map maintain the relative distances among the retrieved documents in the N-dimensional space. Regions in the K-dimensional map associated with concepts are identified. A label is generated for each concept in each identified region. Then generated labels are then rendered on the K-dimensional map in a corresponding region identified.

Method, system, and programs for information search and retrieval. A query is received and is processed to generate a feature-based vector that characterizes the query. A unified representation is then created based on the feature-based vector, that integrates semantic and feature based characterizations of the query. Information relevant to the query is then retrieved from an information archive based on the unified representation of the query. A query response is generated based on the retrieved information relevant to the query and is then transmitted to respond to the query.

Method and system for unified information representation and applications thereof

An advertisement placement method and system for relating an advertisement to a dataset based on a trainable semantic vector (TSV) associated with the dataset and respective semantic representations of the advertisements. The trainable semantic vector associated with the dataset is generated based on at least one data point included in the dataset and known relationships between predetermined data points and predetermined categories. A comparison process is performed to determine a similarity between the trainable semantic vector associated with the dataset and the semantic representation associated with each of the plurality of advertisements. The system selectively relates one or more of the advertisements with the dataset based on a result of the comparison process. The selected advertisement or advertisements may be displayed with the dataset.

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Wen Ruan