View source for Nec corporation (20240127965). DATA PROCESSING DEVICE, SYSTEM, DATA PROCESSING METHOD, AND RECORDING MEDIUM simplified abstract

=DATA PROCESSING DEVICE, SYSTEM, DATA PROCESSING METHOD, AND RECORDING MEDIUM=


==Organization Name==

[[:Category:nec corporation|nec corporation]]


[[Category:nec corporation]]

==Inventor(s)==

[[:Category:Kenichiro Fukushi of Tokyo (JP)|Kenichiro Fukushi of Tokyo (JP)]][[Category:Kenichiro Fukushi of Tokyo (JP)]]

[[:Category:Chenhui Huang of Tokyo (JP)|Chenhui Huang of Tokyo (JP)]][[Category:Chenhui Huang of Tokyo (JP)]]

[[:Category:Zhenwei Wang of Tokyo (JP)|Zhenwei Wang of Tokyo (JP)]][[Category:Zhenwei Wang of Tokyo (JP)]]

[[:Category:Fumiyuki Nihey of Tokyo (JP)|Fumiyuki Nihey of Tokyo (JP)]][[Category:Fumiyuki Nihey of Tokyo (JP)]]

[[:Category:Kentaro Nakahara of Tokyo (JP)|Kentaro Nakahara of Tokyo (JP)]][[Category:Kentaro Nakahara of Tokyo (JP)]]

==DATA PROCESSING DEVICE, SYSTEM, DATA PROCESSING METHOD, AND RECORDING MEDIUM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240127965 titled 'DATA PROCESSING DEVICE, SYSTEM, DATA PROCESSING METHOD, AND RECORDING MEDIUM

==Simplified Explanation==

The data processing device described in the patent application includes a classification unit that categorizes living-body information data based on user attributes, and a learning unit that generates models for estimating interpolation data to fill in missing information using correlations among sensor values, measurement time, and measurement position.

* Classification unit categorizes living-body information data based on user attributes
* Learning unit generates models for estimating interpolation data using correlations among sensor values, measurement time, and measurement position

== Potential Applications ==
This technology could be applied in various fields such as healthcare, fitness tracking, personalized medicine, and biometric identification systems.

== Problems Solved ==
1. Efficiently categorizing and analyzing living-body information data
2. Estimating interpolation data to fill in missing information accurately

== Benefits ==
1. Improved data processing and analysis
2. Enhanced accuracy in estimating missing information
3. Personalized user experience based on individual attributes

== Potential Commercial Applications ==
Optimizing healthcare services, developing advanced fitness trackers, enhancing biometric security systems, and improving personalized medicine practices.

== Possible Prior Art ==
One possible prior art could be the use of machine learning algorithms in data processing devices to analyze and categorize user data for various applications.

=== What are the potential privacy concerns related to the collection and processing of living-body information data? ===
Privacy concerns may arise regarding the collection and storage of sensitive user data, such as health information and biometric data. Users may be worried about the security of their personal information and how it is being used by companies or third parties.

=== How can the accuracy of the interpolation data generated by the learning unit be validated and ensured? ===
The accuracy of the interpolation data can be validated through testing and validation processes using real-world data sets. Cross-validation techniques and comparison with ground truth data can help ensure the reliability and accuracy of the generated interpolation data.


==Original Abstract Submitted==

a data processing device that includes a classification unit that classifies at least one piece of living-body information data into at least one group based on an attribute of at least one user, the at least one piece of living-body information data including a sensor value relating to a living body of the user, and a measurement time and a measurement position of the sensor value, and a learning unit that generates, for each of the group, a model for estimating interpolation data for interpolating a deficit of the living-body information data using a correlation among the sensor value included in the living-body information data, the measurement time, and the measurement position.



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