18485726. SYSTEMS AND METHODS FOR DETERMINING SEMANTIC POINTS IN HUMAN-TO-HUMAN CONVERSATIONS simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

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SYSTEMS AND METHODS FOR DETERMINING SEMANTIC POINTS IN HUMAN-TO-HUMAN CONVERSATIONS

Organization Name

SAMSUNG ELECTRONICS CO., LTD.

Inventor(s)

Ranjan Kumar Samal of Odisha (IN)

Vivek Paul Joseph of Kozhikode (IN)

Raghavendra Hanumatasetty Ramasetty of Bangalore (IN)

SYSTEMS AND METHODS FOR DETERMINING SEMANTIC POINTS IN HUMAN-TO-HUMAN CONVERSATIONS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18485726 titled 'SYSTEMS AND METHODS FOR DETERMINING SEMANTIC POINTS IN HUMAN-TO-HUMAN CONVERSATIONS

Simplified Explanation

The abstract describes a system and method for determining semantic points in human-to-human conversations by analyzing dialogue turns and natural language attributes to derive conversation nuances and semantic relations.

  • Identifying human-to-human conversations with multiple dialogue turns
  • Analyzing natural language attributes of each dialogue turn
  • Deriving a transient state based on the NL attributes
  • Deriving conversation nuances based on NL attributes
  • Dynamically storing information based on NL attributes, transient state, and conversation nuances
  • Determining semantic relations and dialogue timelines within the conversation
  • Generating semantic points based on the determined semantic relations and dialogue timelines

Potential Applications

This technology could be applied in customer service interactions, language learning platforms, and sentiment analysis tools.

Problems Solved

This technology helps in understanding the context and nuances of human conversations, enabling better analysis and interpretation of communication.

Benefits

The system provides a deeper insight into human interactions, improves communication analysis, and enhances the overall understanding of conversations.

Potential Commercial Applications

Potential commercial applications include chatbot development, market research tools, and customer feedback analysis software.

Possible Prior Art

One possible prior art could be sentiment analysis tools that analyze text data for emotional cues and context, but this system focuses specifically on human-to-human conversations and semantic points.

Unanswered Questions

1. How does the system handle different languages and dialects in human conversations? 2. What is the accuracy rate of determining semantic points in various types of conversations?


Original Abstract Submitted

A system and a method for determining semantic points in a human-to-human conversation is provided. The method includes identifying the human-to-human conversation including a plurality of dialogue turns and determining natural language (NL) attributes form each dialogue turn. Further, the method includes deriving a transient state, based on the one or more NL attributes. Further, the method includes deriving one or more conversation nuances associated with the human-to-human conversation based on the one or more NL attributes. Moreover, the method includes dynamically storing information associated with the human-to-human conversation based on the one or more NL attributes, the transient state, and the one or more conversation nuances associated with each dialogue turn and determining one or more semantic relations and associated dialogue timelines within the human-to-human conversation based on the dynamically stored information. Additionally, the method includes generating semantic points corresponding to the determined one or more semantic relations and the associated dialogue timelines within the human-to-human conversation.

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