Nissin Foods has filed a patent for a uric acid level estimation device that aims to eliminate the need for invasive blood collection. The device uses attribute information and non-invasive biological data to generate a uric acid level estimation model through machine learning. The device then calculates the uric acid level of a user based on their attribute information and non-invasive biological data using the estimation model. GlobalData’s report on Nissin Foods gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Nissin Foods, Intelligent cooking vessels was a key innovation area identified from patents. Nissin Foods's grant share as of September 2023 was 25%. Grant share is based on the ratio of number of grants to total number of patents.
Non-invasive device for estimating uric acid levels
A recently filed patent (Publication Number: US20230317218A1) describes a uric acid level estimation device. The device includes an information acquisition unit that collects attribute information and non-invasive biological information from a user. It also has an estimation model storage unit that stores a uric acid level estimation model, and an estimation processing unit that calculates the estimated uric acid level of the user based on the acquired information and the estimation model.
The attribute information can include age and sex, while the non-invasive biological information can include BMI, blood pressure, pulse wave data, electrocardiogram data, and biological impedance. The device also includes a training data storage unit that stores a training data set, and a learning processing unit that generates the uric acid level estimation model using machine learning based on the training data set. The training data set includes attribute information, non-invasive biological information, and a blood-measured uric acid value of a subject.
The device aims to estimate uric acid levels without the need for invasive blood tests. It utilizes attribute information and non-invasive biological information to calculate the estimated uric acid level. The patent claims that there is a coefficient of correlation of 0.6 or higher between the estimated uric acid level and the blood-measured uric acid level.
Additionally, the device can also estimate the risk of uric acid levels. It can calculate a uric acid level risk estimated value based on non-invasive biological information and the blood-measured uric acid level. The learning processing unit provides labels indicating the existence of uric acid level risk to the training data set, and adjusts the number of sample data to reduce any differences in the number of data with and without uric acid level risk.
The patent also describes a non-invasive uric acid level estimation system that includes the uric acid level estimation device and a biological information measurement device for measuring non-invasive biological information.
In summary, the patent describes a uric acid level estimation device that uses attribute information and non-invasive biological information to estimate uric acid levels. It also includes features for estimating the risk of uric acid levels. The device utilizes machine learning to generate an estimation model based on training data sets. The patent also mentions a non-invasive uric acid level estimation system and a method for estimating uric acid levels using a computer program.
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