20240028920. METHODS AND SYSTEMS FOR DERIVING A BEHAVIOR KNOWLEDGE MODEL FOR DATA ANALYTICS simplified abstract (Tata Consultancy Services Limited)
METHODS AND SYSTEMS FOR DERIVING A BEHAVIOR KNOWLEDGE MODEL FOR DATA ANALYTICS
Organization Name
Tata Consultancy Services Limited
Inventor(s)
Swaminathan Natarajan of Chennai (IN)
Srinivasa Raghavan Venkatachari of Chennai (IN)
METHODS AND SYSTEMS FOR DERIVING A BEHAVIOR KNOWLEDGE MODEL FOR DATA ANALYTICS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240028920 titled 'METHODS AND SYSTEMS FOR DERIVING A BEHAVIOR KNOWLEDGE MODEL FOR DATA ANALYTICS
Simplified Explanation
This disclosure relates to methods and systems for deriving a behavior knowledge model for data analytics. The current automated technical solutions for monitoring the health status or behavior pattern, that apply a domain knowledge for the data analytics are very limited. Hence the conventional techniques for monitoring the health status or behavior pattern are manual, application centric and inaccurate. The present disclosure automatically leverages relevant domain knowledge and the sensor data for building a behavior knowledge model which further enhanced by the deviations identified using a machine leaning model. The present disclosure facilitates development a knowledge-driven simulator that generates sensor data sets for typical resident behavior, based on definable activity patterns and pattern influencers of interest (e.g., diabetes, nocturia).
- The disclosure provides methods and systems for deriving a behavior knowledge model for data analytics.
- The current automated solutions for monitoring health status or behavior pattern are limited and inaccurate.
- The disclosure leverages domain knowledge and sensor data to build a behavior knowledge model.
- The behavior knowledge model is enhanced by deviations identified using a machine learning model.
- The disclosure enables the development of a knowledge-driven simulator that generates sensor data sets for typical resident behavior.
- The simulator is based on definable activity patterns and pattern influencers of interest, such as diabetes or nocturia.
Potential applications of this technology:
- Healthcare monitoring and analysis
- Smart home systems
- Elderly care and assisted living
- Industrial process monitoring
- Environmental monitoring
Problems solved by this technology:
- Limited and inaccurate automated monitoring of health status or behavior pattern
- Manual and application-centric monitoring techniques
- Lack of leveraging relevant domain knowledge for data analytics
Benefits of this technology:
- Improved accuracy and efficiency in monitoring health status or behavior pattern
- Automatic leveraging of domain knowledge for data analytics
- Enhanced behavior knowledge model through machine learning
- Development of a knowledge-driven simulator for generating sensor data sets
- Better understanding and analysis of resident behavior patterns
Original Abstract Submitted
this disclosure relates generally to methods and systems for deriving a behavior knowledge model for data analytics. the current automated technical solutions for monitoring the health status or behavior pattern, that apply a domain knowledge for the data analytics are very limited. hence the conventional techniques for monitoring the health status or behavior pattern are manual, application centric and inaccurate. the present disclosure automatically leverages relevant domain knowledge and the sensor data for building a behavior knowledge model which further enhanced by the deviations identified using a machine leaning model. the present disclosure facilitates development a knowledge-driven simulator that generates sensor data sets for typical resident behavior, based on definable activity patterns and pattern influencers of interest (e.g., diabetes, nocturia).
