Panasonic had 14 patents in big data during Q4 2023. Panasonic Holdings Corp filed patents in Q4 2023 for a system using statistical background subtraction to generate causal inference machine learning models, a data analysis device for calculating difference values and deriving a difference model, a control apparatus for reserving recording regions and transmitting access information to video processing apparatuses, a data distribution method using secure computation on encrypted history information, and a testing and verification system for an equivalent physical configuration of an in-flight entertainment and communications system using virtual machines and test interfaces. GlobalData’s report on Panasonic gives a 360-degreee view of the company including its patenting strategy. Buy the report here.
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Panasonic grant share with big data as a theme is 42% in Q4 2023. Grant share is based on the ratio of number of grants to total number of patents.
Recent Patents
Application: Causal inference machine learning with statistical background subtraction (Patent ID: US20230419184A1)
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The patent filed by Panasonic Holdings Corp. describes a system and method for generating causal inference machine learning models using statistical background subtraction. The system involves receiving historical sales data and corresponding data on causal variables, deconfounding the cause-effect relationship, defining sample weights for statistical background subtraction, and training machine learning models to predict individual causal effects on demand quantities. The method also includes predicting demand quantities during a specific period by training a second machine learning model. Additionally, the system can handle causal variables like sending personalized coupons and restrict the learning of causal factors to specific dependencies.
The system and method outlined in the patent involve conducting randomized controlled A/B group trials to deconfound the cause-effect relationship between historical sales data and causal variables. By defining sample weights for statistical background subtraction and training machine learning models using an iterative approach, the system can predict individual causal effects on demand quantities accurately. Furthermore, the system can predict what-if volume scenarios based on hypothetical causal factors and individual causal effects on gross margin. The system is also capable of modeling demand using distributions like negative binomial or Poisson-Gamma, providing a comprehensive approach to generating causal inference machine learning models for demand forecasting and analysis.
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