US Patent Application 18204767. TECHNIQUES TO ADD SMART DEVICE INFORMATION TO MACHINE LEARNING FOR INCREASED CONTEXT simplified abstract
TECHNIQUES TO ADD SMART DEVICE INFORMATION TO MACHINE LEARNING FOR INCREASED CONTEXT
Organization Name
Inventor(s)
Alan Salimov of San Bruno CA (US)
Anish Khazane of San Francisco CA (US)
Omar Florez Choque of Oakland CA (US)
TECHNIQUES TO ADD SMART DEVICE INFORMATION TO MACHINE LEARNING FOR INCREASED CONTEXT - A simplified explanation of the abstract
This abstract first appeared for US patent application 18204767 titled 'TECHNIQUES TO ADD SMART DEVICE INFORMATION TO MACHINE LEARNING FOR INCREASED CONTEXT
Simplified Explanation
The patent application describes an apparatus, system, and computer readable medium that process non-dialog information from a smart device.
- The processing circuitry receives non-dialog information and determines the data type of the received data.
- Based on the data type, the processing circuitry transforms the data using a machine learning algorithm into standardized data.
- The standardized transformed data is suitable for training chatbot systems.
- This innovation allows the underutilized non-dialog information to be used as training input to improve context and conversation flow between a chatbot and a user.
Original Abstract Submitted
Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.
