PATENT CLAIM ANALYSIS

Application Number: 16112611
Application Type: Utility
Filing Date: 2018-08
Publication Date: 2018-12
Patent Classification: ["709", "206000"]

Abstract:
Examples are generally directed towards context-sensitive generation of conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of training context-message-response n-tuples. A response generation engine is trained on the set of training context-message-response n-tuples. The trained response generation engine automatically generates a context-sensitive response based on a user generated input message and conversational context data. A digital assistant utilizes the trained response generation engine to generate context-sensitive, natural language responses that are pertinent to user queries.

Claim (Index 17):
The computer-implemented method of  claim 12 , further comprising:\n deriving, by a context data driven derivation component, the non-linguistic context data from sensor data received in real-time from a set of sensors, wherein the set of sensors comprises at least one of a camera, an audio sensor, a global positioning system (GPS) sensor, an infrared sensor, a pressure sensor, a motion sensor, an orientation sensor, temperature sensor, or speed sensor, wherein the context data derivation component provides the conversational context data to the response generation engine.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 96.0
- Lexical Diversity: 1.75
- Patent Class: 709.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14726562', '14726569', '15816282', '11580926', '15672424']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2649258656373442
- 35 USC 102 Novelty (BERT): 0.5520474165003955
- Combined Prediction Score: 0.2936380207236494
- Mean Citation Score: 264.69212400000004
- Max Citation Score: 439.5038
- Similarity Product: 305.47297765104764

Labels:
- Claim Label 101: 0
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 0

Dataset: test