COMPUTER SOFTWARE SEMANTIC INFERENCE

Brand Owner (click to sort) Address Description
ADAPTIVE SEMANTIC Lucomm Technologies Inc. 14400 NE BEL RED RD STE 208 BELLEVUE WA 98007 Computer software for semantic inference;Cloud computing featuring software for use in semantic inference;
PREDICTIVE SEMANTIC Lucomm Technologies Inc. 14400 NE BEL RED RD STE 208 BELLEVUE WA 98007 Computer software for semantic inference;Cloud computing featuring software for use in semantic inference;
SEMANTIC SHIELD Lucomm Technologies Inc. 14400 NE BEL RED RD STE 208 BELLEVUE WA 98007 Computer software for semantic inference used in data protection; Computer software for semantic inference used in device's resources access control; Computer software platforms for semantic inference used in data protection; Computer software platforms for semantic inference used in devices' resources access control; Computer application software for mobile phones, namely, software for use in device's resources access control; Computer application software for mobile phones, namely, software for use in data protection;SEMANTIC;Cloud computing featuring software for use in semantic inference related to data protection; Cloud computing featuring software for use in semantic inference related to devices' resources access protection;
SEMANTIC TRAIL Lucomm Technologies Inc. 14400 NE BEL RED RD STE 208 BELLEVUE WA 98007 Computer software for semantic inference; Computer software for data processing and storage;SEMANTIC;Cloud computing featuring software for use in semantic inference; Cloud computing featuring software for use in data processing and storage;
 

Where the owner name is not linked, that owner no longer owns the brand

    
Technical Examples
  1. Codifying the "most prominent measurement points" of a document can be used to measure semantic distances given an area of study (e.g., white papers on some subject area). A semantic abstract is created for each document. The semantic abstract is a semantic measure of the subject or theme of the document providing a new and unique mechanism for characterizing content. The semantic abstract includes state vectors in the topological vector space, each state vector representing one lexeme or lexeme phrase about the document. The state vectors can be dominant phrase vectors in the topological vector space mapped from dominant phrases extracted from the document. The state vectors can also correspond to words in the document that are most significant to the document's meaning (the state vectors are called dominant vectors in this case). One semantic abstract can be directly compared with another semantic abstract, resulting in a numeric semantic distance between the semantic abstracts being compared.