system

Abstract

The system according to this embodiment aims to improve the accuracy of lifespan prediction even when it is difficult to collect data from each company. [Solution] The system according to the embodiment comprises a collection unit, a prediction unit, a supply unit, and a learning unit. The collection unit collects data. The prediction unit analyzes the data collected by the collection unit and predicts the lifespan. The supply unit provides products based on the diagnostic results obtained by the prediction unit. The learning unit performs federated learning when it is difficult to collect data from each company.

Description

Technical Field

[0005]

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to collect data from each company and it is difficult to improve the accuracy of life prediction.

[0005] The system according to the embodiment aims to improve the accuracy of life prediction even when it is difficult to collect data from each company.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a prediction unit, a supply unit, and a learning unit. The data collection unit collects data. The prediction unit analyzes the data collected by the data collection unit and predicts the lifespan. The supply unit provides products based on the diagnostic results obtained by the prediction unit. The learning unit performs federated learning when it is difficult to collect data from each company. [Effects of the Invention]

[0007] The system according to this embodiment can improve the accuracy of lifespan prediction even when it is difficult to collect data from each company. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The life expectancy prediction system according to an embodiment of the present invention is a model that predicts life expectancy by sharing and collecting data among various companies. This life expectancy prediction system collects data from various companies and constructs a model to predict life expectancy. The data to be collected includes genetic information, lifestyle habits, medical data, environmental factors, etc. Next, according to the diagnostic results, it provides an optimal life plan using various products and services. For example, based on the life expectancy prediction, it proposes health support, preventive medicine, insurance, life plans, travel plans, healthy diets, etc. Furthermore, if it is difficult to collect data from various companies, associative learning is applied to improve learning accuracy using only gradient information while keeping the data confidential. For example, by sharing only gradient information while keeping the data held by each company confidential and performing learning, it is possible to improve the accuracy of the life expectancy prediction model while protecting data privacy. As a result, the life expectancy prediction system can predict the user's life expectancy and use it as a reference for health management and life planning. Furthermore, it is possible to improve the accuracy of the life expectancy prediction model while protecting data privacy. For example, by proposing health support, preventive medicine, insurance, life plans, travel plans, healthy diets, etc. based on the life expectancy prediction, it is possible to improve the quality of life of the user.

[0029] The life expectancy prediction system according to this embodiment comprises a data collection unit, a prediction unit, a provision unit, and a learning unit. The data collection unit collects data. The data collection unit collects data such as genetic information, lifestyle habits, medical data, and environmental factors. The data collection unit collects data such as family medical history and genetic information, smoking and drinking habits, exercise habits, diet, medical history, test results, treatment history, living environment, and occupation. The prediction unit analyzes the data collected by the data collection unit and predicts life expectancy. The prediction unit predicts the user's life expectancy by considering, for example, genetic information, lifestyle habits, medical data, and environmental factors. The prediction unit predicts life expectancy using, for example, a machine learning algorithm. The provision unit provides products based on the diagnostic results obtained by the prediction unit. The provision unit proposes, for example, health support, preventive medicine, insurance, life plans, travel plans, and healthy eating based on the life expectancy prediction. The provision unit proposes a health support program based on the user's life expectancy prediction result. The learning unit performs federated learning when it is difficult to collect data from each company. The learning unit improves learning accuracy using only gradient information while keeping the data confidential. For example, the learning unit learns by sharing only gradient information while keeping the data held by each company confidential. As a result, the lifespan prediction system according to the embodiment can collect data, predict lifespan, and provide products based on the diagnostic results.

[0030] The data collection unit collects data such as genetic information, lifestyle data, medical data, and environmental factors. Specifically, for genetic information, it collects the user's DNA sequencing data and information on specific gene mutations. This involves using saliva or blood samples provided by the user and having them analyzed at a specialized genetic analysis institution. For lifestyle data, it collects information about the user's daily actions and habits. For example, it records the user's steps, heart rate, sleep patterns, and exercise level using smartphones or wearable devices. It also collects information such as the user's diet, smoking, and drinking frequency. For medical data, it collects detailed medical information such as the user's medical history, test results, and treatment history. This includes data provided by electronic medical record systems and medical institutions. For environmental factor data, it collects information about the user's living and working environment. For example, it collects information on air pollution levels, water quality, and noise levels in the area where the user lives, and for the working environment, it records the type of occupation, working conditions, and stress levels. This allows the data collection unit to centrally collect a wide range of data, enabling a comprehensive understanding of users' health status and living environment. Furthermore, the data collection unit securely manages this data and encrypts or anonymizes it to protect privacy. This ensures that highly accurate data collection is achieved while protecting users' personal information.

[0031] The prediction unit analyzes the data collected by the collection unit and predicts lifespan. The prediction unit predicts the user's lifespan by considering factors such as genetic information, lifestyle, medical data, and environmental factors. Specifically, it uses machine learning algorithms to predict lifespan. For example, it analyzes the impact of specific gene mutations on lifespan from genetic information, and evaluates the risk that lack of exercise and unhealthy eating habits pose to lifespan from lifestyle data. From medical data, it analyzes how past medical history and treatment history affect future health risks, and from environmental factor data, it evaluates the impact of living and working environments on health. By integrating this data and using machine learning algorithms, the prediction unit predicts the user's lifespan with high accuracy. Specifically, it uses algorithms such as deep learning, random forest, and support vector machines to analyze features obtained from multiple data sources and construct a lifespan prediction model. Furthermore, the prediction unit continuously improves the accuracy of the prediction model by utilizing past data and statistical information. For example, it optimizes the model parameters using past user data and updates the model each time new data is collected. This allows the prediction unit to always provide highly accurate lifespan predictions based on the latest information.

[0032] The service provider offers products based on the diagnostic results obtained by the prediction service provider. Specifically, based on life expectancy predictions, they propose health support, preventive medicine, insurance, life plans, travel plans, and healthy eating. For example, based on the user's life expectancy prediction, they propose a health support program. This includes individual exercise plans, meal plans, and stress management programs. For preventive medicine, they propose regular health checkups, vaccinations for specific diseases, and testing programs for early detection. For insurance products, they propose life insurance, medical insurance, and long-term care insurance tailored to the user's health risks. For life plans, based on the user's life expectancy prediction, they propose future asset management, pension plans, and plans for home purchase and renovation. For travel plans, based on the user's health status and life expectancy prediction, they propose appropriate travel destinations, travel duration, and health management plans during travel. For healthy eating, they propose meal plans, recipes, and food selection methods tailored to the user's nutritional status and health risks. In this way, the service provider can provide products tailored to the user's individual needs based on the user's life expectancy prediction results, supporting the user's health maintenance and improvement of quality of life. Furthermore, the service department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. This allows the service department to provide users with the most suitable products and services, thereby increasing their satisfaction.

[0033] The learning unit performs federated learning when it is difficult to collect data from each company. Specifically, it improves learning accuracy using only gradient information while keeping the data confidential. For example, it shares only gradient information while keeping the data held by each company confidential and performs learning. This allows for the construction of a highly accurate model while protecting data privacy. In the federated learning process, each company's data is held locally and is not sent to the central server. The model is trained locally using each company's data, and only its gradient information is sent to the central server. The central server integrates the gradient information sent from each company and updates the overall model. The updated model is then distributed to each company again, and local learning continues. By repeating this process, a highly accurate model can be built while keeping each company's data confidential. Furthermore, to improve the accuracy of federated learning, the learning unit can use anomaly detection algorithms to detect and remove fraudulent data and abnormal gradient information. This allows the learning unit to build a highly accurate lifetime prediction model and improve the overall system performance while protecting data privacy.

[0034] The data collection unit can collect data such as genetic information, lifestyle habits, medical data, and environmental factors. For example, the data collection unit can collect DNA sequence data and gene markers as genetic information. For example, the data collection unit can collect diet, exercise, and sleep patterns as lifestyle habits. For example, the data collection unit can collect medical records, test results, and prescription information as medical data. For example, the data collection unit can collect temperature, humidity, and air quality as environmental factors. By collecting diverse data in this way, the accuracy of lifespan prediction is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using gene analysis AI to obtain genetic information.

[0035] The prediction unit can analyze collected data and predict the lifespan of individual users. For example, the prediction unit analyzes collected genetic information, lifestyle habits, medical data, and environmental factors. The prediction unit predicts lifespan using, for example, machine learning algorithms. The prediction unit can also predict lifespan using, for example, statistical analysis. The prediction unit can also predict lifespan using, for example, neural networks. This allows for the provision of individualized life plans by predicting the lifespan of each user. Some or all of the above-described processes in the prediction unit may be performed using, for example, AI, or without AI. For example, the prediction unit can predict lifespan using an AI model that takes collected data as input and outputs a lifespan prediction.

[0036] The service provider can propose health support, preventive medicine, insurance, life plans, travel plans, healthy meals, etc., based on life expectancy predictions. For example, the service provider can propose health support programs based on life expectancy prediction results. For example, the service provider can propose vaccinations and health checkups as preventive medicine. For example, the service provider can propose life insurance and medical insurance as insurance products. For example, the service provider can propose asset management plans and retirement plans as life plans. For example, the service provider can propose travel destinations and travel insurance as travel plans. For example, the service provider can propose nutritionally balanced meal menus and ingredients as healthy meals. In this way, the service provider can provide the user with the most suitable products based on life expectancy predictions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can propose products using an AI model that takes life expectancy prediction results as input and outputs the most suitable products.

[0037] The learning unit can improve learning accuracy using only gradient information while keeping the data confidential. For example, the learning unit can learn by sharing only gradient information while keeping the data held by each company confidential. For example, the learning unit can improve learning accuracy while protecting data privacy by applying federative learning. For example, the learning unit can learn using confidential data after anonymizing or encrypting it. For example, the learning unit can learn using gradient descent or gradient boosting. This makes it possible to improve learning accuracy while protecting data privacy. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can take confidential data as input and perform learning using an AI model that improves learning accuracy.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting the types of data that the user has frequently provided in the past. For example, the data collection unit selects the most efficient collection timing from the user's past data collection history. For example, the data collection unit analyzes the accuracy of the data the user has provided in the past and proposes the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can take the user's past data collection history as input and perform data collection using an AI model that selects the optimal collection method.

[0039] The data collection unit can filter data based on the user's current health status and lifestyle. For example, if the user is in poor health, the data collection unit can reduce the burden by limiting data collection. For example, if the user is in good health, the data collection unit can collect more detailed data to improve accuracy. For example, the data collection unit can adjust the frequency and timing of data collection according to the user's lifestyle. This reduces the burden on the user by adjusting data collection according to their health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's health status and lifestyle as input and filters the data collection.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit will prioritize the collection of data related to the destination region. For example, if the user is staying in a specific location for an extended period, the data collection unit will prioritize the collection of data related to that location. This enables highly accurate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's geographical location information as input and prioritizes the collection of highly relevant data to perform data collection.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can prioritize collecting data related to the user's interests from the user's social media activity. For example, the data collection unit can collect relevant data based on information about accounts followed by the user on social media. In this way, data related to the user's interests can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data collection using an AI model that takes the user's social media activity data as input and collects relevant data.

[0042] The prediction unit can adjust the level of detail of its predictions based on the importance of the data. For example, the prediction unit can make detailed predictions based on high-importance data. For example, the prediction unit can make simplified predictions based on low-importance data. For example, the prediction unit can adjust the level of detail of its predictions in stages according to the importance of the data. This allows for efficient predictions by adjusting the level of detail of the predictions according to the importance of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes data importance as input and adjusts the level of detail of the predictions.

[0043] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a gene analysis algorithm for predictions based on genetic information. For example, the prediction unit applies a behavioral analysis algorithm for predictions based on lifestyle data. For example, the prediction unit applies a medical statistics algorithm for predictions based on medical data. By applying an appropriate prediction algorithm according to the data category, prediction accuracy is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data category as input and applies an appropriate prediction algorithm.

[0044] The prediction unit can determine the priority of predictions based on the data collection timing during the prediction process. For example, the prediction unit may prioritize predictions based on the latest data. For example, the prediction unit may lower the priority of predictions based on older data. For example, the prediction unit may adjust the prediction priority in stages according to the data collection timing. This enables efficient prediction by determining the prediction priority based on the data collection timing. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data collection timing as input and determines the prediction priority.

[0045] The prediction unit can adjust the order of predictions based on the relevance of the data during the prediction process. For example, the prediction unit may prioritize predictions based on highly relevant data. For example, the prediction unit may postpone predictions based on less relevant data. For example, the prediction unit may adjust the order of predictions in stages according to the relevance of the data. This allows for efficient prediction by adjusting the order of predictions based on the relevance of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the relevance of the data as input and adjusts the order of predictions.

[0046] The service provider can analyze a user's past purchase history to select the most suitable product when providing products. For example, the service provider can suggest related products based on products the user has purchased in the past. For example, the service provider can select the most suitable product from the user's past purchase history. For example, the service provider can suggest the most suitable product based on the user's evaluation of products they have purchased in the past. In this way, by analyzing past purchase history, the service provider can provide the user with the most suitable product. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes the user's past purchase history as input and selects the most suitable product.

[0047] The service provider can customize the products offered based on the user's current living situation when providing products. For example, if the user is in poor health, the service provider will prioritize providing health support products. For example, if the user is planning a trip, the service provider will suggest a travel plan. For example, if the user is experiencing a specific life event, the service provider will provide products related to that event. By customizing products according to the user's living situation, more appropriate products can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes the user's living situation data as input and customizes the products offered to provide products.

[0048] The service provider can provide the most suitable products by considering the user's geographical location information when providing products. For example, if the user is in a specific region, the service provider can provide products related to that region. For example, if the user is on the move, the service provider can provide products related to the destination region. For example, if the user is staying in a specific location for an extended period, the service provider can provide detailed information related to that location. This enables the provision of more appropriate information by providing the most suitable products based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes the user's geographical location information as input and provides the most suitable products.

[0049] The service provider can analyze a user's social media activity and suggest relevant products when providing products. For example, the service provider can provide relevant products based on information shared by the user on social media. For example, the service provider can prioritize providing products related to the user's interests based on their social media activity. For example, the service provider can provide relevant products based on information about accounts followed by the user on social media. In this way, by analyzing social media activity, the service provider can provide products related to the user's interests. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes user social media activity data as input and suggests relevant products.

[0050] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective algorithm. For example, the learning unit can adjust parameters to improve learning accuracy based on past learning data. For example, the learning unit can identify areas for improvement in the learning algorithm based on past learning data and perform optimization. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can take past learning data as input and perform learning using an AI model that optimizes the learning algorithm.

[0051] The learning unit can apply different learning methods during training depending on the data's level of anonymity. For example, the learning unit can apply associative learning to data with a high level of anonymity. For example, the learning unit can apply a learning method that involves partial data sharing to data with a moderate level of anonymity. For example, the learning unit can apply a conventional centralized learning method to data with a low level of anonymity. This improves learning accuracy by applying the appropriate learning method according to the data's level of anonymity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that takes the data's level of anonymity as input and applies an appropriate learning method.

[0052] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign higher weights to the most recent data, or lower weights to older data. The learning unit can also adjust the weighting of the training data in stages according to the data collection timing. This improves training accuracy by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that takes the data collection timing as input and weights the training data.

[0053] The learning unit can select training data based on the relevance of the data during training. For example, the learning unit may prioritize learning highly relevant data. For example, the learning unit may postpone learning less relevant data. For example, the learning unit may adjust the selection of training data in stages according to the relevance of the data. This improves the learning accuracy by selecting training data based on the relevance of the data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that takes data relevance as input and selects training data.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting the types of data that the user has frequently provided in the past. For example, the data collection unit selects the most efficient collection timing from the user's past data collection history. For example, the data collection unit analyzes the accuracy of the data the user has provided in the past and proposes the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can take the user's past data collection history as input and perform data collection using an AI model that selects the optimal collection method.

[0056] The data collection unit can filter data based on the user's current health status and lifestyle. For example, if the user is in poor health, the data collection unit can reduce the burden by limiting data collection. For example, if the user is in good health, the data collection unit can collect more detailed data to improve accuracy. For example, the data collection unit can adjust the frequency and timing of data collection according to the user's lifestyle. This reduces the burden on the user by adjusting data collection according to their health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's health status and lifestyle as input and filters the data collection.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit will prioritize the collection of data related to the destination region. For example, if the user is staying in a specific location for an extended period, the data collection unit will prioritize the collection of data related to that location. This enables highly accurate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's geographical location information as input and prioritizes the collection of highly relevant data to perform data collection.

[0058] The prediction unit can adjust the level of detail of its predictions based on the importance of the data. For example, the prediction unit can make detailed predictions based on high-importance data. For example, the prediction unit can make simplified predictions based on low-importance data. For example, the prediction unit can adjust the level of detail of its predictions in stages according to the importance of the data. This allows for efficient predictions by adjusting the level of detail of the predictions according to the importance of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes data importance as input and adjusts the level of detail of the predictions.

[0059] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a gene analysis algorithm for predictions based on genetic information. For example, the prediction unit applies a behavioral analysis algorithm for predictions based on lifestyle data. For example, the prediction unit applies a medical statistics algorithm for predictions based on medical data. By applying an appropriate prediction algorithm according to the data category, prediction accuracy is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data category as input and applies an appropriate prediction algorithm.

[0060] The prediction unit can determine the priority of predictions based on the data collection timing during the prediction process. For example, the prediction unit may prioritize predictions based on the latest data. For example, the prediction unit may lower the priority of predictions based on older data. For example, the prediction unit may adjust the prediction priority in stages according to the data collection timing. This enables efficient prediction by determining the prediction priority based on the data collection timing. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data collection timing as input and determines the prediction priority.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data. The data collection unit collects data such as genetic information, lifestyle habits, medical data, and environmental factors. Specifically, it collects data such as family medical history and genetic information, smoking and drinking habits, exercise habits, diet, medical history, test results, treatment history, living environment, and occupation. Step 2: The prediction unit analyzes the data collected by the collection unit and predicts lifespan. The prediction unit predicts the user's lifespan by considering factors such as genetic information, lifestyle, medical data, and environmental factors. The prediction unit uses machine learning algorithms to predict lifespan. Step 3: The service provider offers products based on the diagnostic results obtained by the prediction service provider. For example, the service provider proposes health support, preventive medicine, insurance, life plans, travel plans, healthy eating, etc., based on the life expectancy prediction. Specifically, it proposes a health support program based on the user's life expectancy prediction results. Step 4: The learning unit performs federated learning when it is difficult to collect data from each company. For example, the learning unit improves learning accuracy using only gradient information while keeping the data confidential. Specifically, it performs learning by sharing only the gradient information while keeping the data held by each company confidential.

[0063] (Example of form 2) The life expectancy prediction system according to an embodiment of the present invention is a model that predicts life expectancy by sharing and collecting data among various companies. This life expectancy prediction system collects data from various companies and constructs a model to predict life expectancy. The data to be collected includes genetic information, lifestyle habits, medical data, environmental factors, etc. Next, according to the diagnostic results, it provides an optimal life plan using various products and services. For example, based on the life expectancy prediction, it proposes health support, preventive medicine, insurance, life plans, travel plans, healthy diets, etc. Furthermore, if it is difficult to collect data from various companies, associative learning is applied to improve learning accuracy using only gradient information while keeping the data confidential. For example, by sharing only gradient information while keeping the data held by each company confidential and performing learning, it is possible to improve the accuracy of the life expectancy prediction model while protecting data privacy. As a result, the life expectancy prediction system can predict the user's life expectancy and use it as a reference for health management and life planning. Furthermore, it is possible to improve the accuracy of the life expectancy prediction model while protecting data privacy. For example, by proposing health support, preventive medicine, insurance, life plans, travel plans, healthy diets, etc. based on the life expectancy prediction, it is possible to improve the quality of life of the user.

[0064] The life expectancy prediction system according to this embodiment comprises a data collection unit, a prediction unit, a provision unit, and a learning unit. The data collection unit collects data. The data collection unit collects data such as genetic information, lifestyle habits, medical data, and environmental factors. The data collection unit collects data such as family medical history and genetic information, smoking and drinking habits, exercise habits, diet, medical history, test results, treatment history, living environment, and occupation. The prediction unit analyzes the data collected by the data collection unit and predicts life expectancy. The prediction unit predicts the user's life expectancy by considering, for example, genetic information, lifestyle habits, medical data, and environmental factors. The prediction unit predicts life expectancy using, for example, a machine learning algorithm. The provision unit provides products based on the diagnostic results obtained by the prediction unit. The provision unit proposes, for example, health support, preventive medicine, insurance, life plans, travel plans, and healthy eating based on the life expectancy prediction. The provision unit proposes a health support program based on the user's life expectancy prediction result. The learning unit performs federated learning when it is difficult to collect data from each company. The learning unit improves learning accuracy using only gradient information while keeping the data confidential. For example, the learning unit learns by sharing only gradient information while keeping the data held by each company confidential. As a result, the lifespan prediction system according to the embodiment can collect data, predict lifespan, and provide products based on the diagnostic results.

[0065] The data collection unit collects data such as genetic information, lifestyle data, medical data, and environmental factors. Specifically, for genetic information, it collects the user's DNA sequencing data and information on specific gene mutations. This involves using saliva or blood samples provided by the user and having them analyzed at a specialized genetic analysis institution. For lifestyle data, it collects information about the user's daily actions and habits. For example, it records the user's steps, heart rate, sleep patterns, and exercise level using smartphones or wearable devices. It also collects information such as the user's diet, smoking, and drinking frequency. For medical data, it collects detailed medical information such as the user's medical history, test results, and treatment history. This includes data provided by electronic medical record systems and medical institutions. For environmental factor data, it collects information about the user's living and working environment. For example, it collects information on air pollution levels, water quality, and noise levels in the area where the user lives, and for the working environment, it records the type of occupation, working conditions, and stress levels. This allows the data collection unit to centrally collect a wide range of data, enabling a comprehensive understanding of users' health status and living environment. Furthermore, the data collection unit securely manages this data and encrypts or anonymizes it to protect privacy. This ensures that highly accurate data collection is achieved while protecting users' personal information.

[0066] The prediction unit analyzes the data collected by the collection unit and predicts lifespan. The prediction unit predicts the user's lifespan by considering factors such as genetic information, lifestyle, medical data, and environmental factors. Specifically, it uses machine learning algorithms to predict lifespan. For example, it analyzes the impact of specific gene mutations on lifespan from genetic information, and evaluates the risk that lack of exercise and unhealthy eating habits pose to lifespan from lifestyle data. From medical data, it analyzes how past medical history and treatment history affect future health risks, and from environmental factor data, it evaluates the impact of living and working environments on health. By integrating this data and using machine learning algorithms, the prediction unit predicts the user's lifespan with high accuracy. Specifically, it uses algorithms such as deep learning, random forest, and support vector machines to analyze features obtained from multiple data sources and construct a lifespan prediction model. Furthermore, the prediction unit continuously improves the accuracy of the prediction model by utilizing past data and statistical information. For example, it optimizes the model parameters using past user data and updates the model each time new data is collected. This allows the prediction unit to always provide highly accurate lifespan predictions based on the latest information.

[0067] The service provider offers products based on the diagnostic results obtained by the prediction service provider. Specifically, based on life expectancy predictions, they propose health support, preventive medicine, insurance, life plans, travel plans, and healthy eating. For example, based on the user's life expectancy prediction, they propose a health support program. This includes individual exercise plans, meal plans, and stress management programs. For preventive medicine, they propose regular health checkups, vaccinations for specific diseases, and testing programs for early detection. For insurance products, they propose life insurance, medical insurance, and long-term care insurance tailored to the user's health risks. For life plans, based on the user's life expectancy prediction, they propose future asset management, pension plans, and plans for home purchase and renovation. For travel plans, based on the user's health status and life expectancy prediction, they propose appropriate travel destinations, travel duration, and health management plans during travel. For healthy eating, they propose meal plans, recipes, and food selection methods tailored to the user's nutritional status and health risks. In this way, the service provider can provide products tailored to the user's individual needs based on the user's life expectancy prediction results, supporting the user's health maintenance and improvement of quality of life. Furthermore, the service department can collect feedback from users and continuously improve the accuracy and effectiveness of its proposals. This allows the service department to provide users with the most suitable products and services, thereby increasing their satisfaction.

[0068] The learning unit performs federated learning when it is difficult to collect data from each company. Specifically, it improves learning accuracy using only gradient information while keeping the data confidential. For example, it shares only gradient information while keeping the data held by each company confidential and performs learning. This allows for the construction of a highly accurate model while protecting data privacy. In the federated learning process, each company's data is held locally and is not sent to the central server. The model is trained locally using each company's data, and only its gradient information is sent to the central server. The central server integrates the gradient information sent from each company and updates the overall model. The updated model is then distributed to each company again, and local learning continues. By repeating this process, a highly accurate model can be built while keeping each company's data confidential. Furthermore, to improve the accuracy of federated learning, the learning unit can use anomaly detection algorithms to detect and remove fraudulent data and abnormal gradient information. This allows the learning unit to build a highly accurate lifetime prediction model and improve the overall system performance while protecting data privacy.

[0069] The data collection unit can collect data such as genetic information, lifestyle habits, medical data, and environmental factors. For example, the data collection unit can collect DNA sequence data and gene markers as genetic information. For example, the data collection unit can collect diet, exercise, and sleep patterns as lifestyle habits. For example, the data collection unit can collect medical records, test results, and prescription information as medical data. For example, the data collection unit can collect temperature, humidity, and air quality as environmental factors. By collecting diverse data in this way, the accuracy of lifespan prediction is improved. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect data using gene analysis AI to obtain genetic information.

[0070] The prediction unit can analyze collected data and predict the lifespan of individual users. For example, the prediction unit analyzes collected genetic information, lifestyle habits, medical data, and environmental factors. The prediction unit predicts lifespan using, for example, machine learning algorithms. The prediction unit can also predict lifespan using, for example, statistical analysis. The prediction unit can also predict lifespan using, for example, neural networks. This allows for the provision of individualized life plans by predicting the lifespan of each user. Some or all of the above-described processes in the prediction unit may be performed using, for example, AI, or without AI. For example, the prediction unit can predict lifespan using an AI model that takes collected data as input and outputs a lifespan prediction.

[0071] The service provider can propose health support, preventive medicine, insurance, life plans, travel plans, healthy meals, etc., based on life expectancy predictions. For example, the service provider can propose health support programs based on life expectancy prediction results. For example, the service provider can propose vaccinations and health checkups as preventive medicine. For example, the service provider can propose life insurance and medical insurance as insurance products. For example, the service provider can propose asset management plans and retirement plans as life plans. For example, the service provider can propose travel destinations and travel insurance as travel plans. For example, the service provider can propose nutritionally balanced meal menus and ingredients as healthy meals. In this way, the service provider can provide the user with the most suitable products based on life expectancy predictions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can propose products using an AI model that takes life expectancy prediction results as input and outputs the most suitable products.

[0072] The learning unit can improve learning accuracy using only gradient information while keeping the data confidential. For example, the learning unit can learn by sharing only gradient information while keeping the data held by each company confidential. For example, the learning unit can improve learning accuracy while protecting data privacy by applying federative learning. For example, the learning unit can learn using confidential data after anonymizing or encrypting it. For example, the learning unit can learn using gradient descent or gradient boosting. This makes it possible to improve learning accuracy while protecting data privacy. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can take confidential data as input and perform learning using an AI model that improves learning accuracy.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily suspend data collection and resume it when the user is relaxed. For example, if the user is relaxed, the data collection unit can actively collect detailed data. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This allows for the collection of more appropriate data by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can take user emotion data as input and perform data collection using an AI model that adjusts the timing of data collection.

[0074] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting the types of data that the user has frequently provided in the past. For example, the data collection unit selects the most efficient collection timing from the user's past data collection history. For example, the data collection unit analyzes the accuracy of the data the user has provided in the past and proposes the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can take the user's past data collection history as input and perform data collection using an AI model that selects the optimal collection method.

[0075] The data collection unit can filter data based on the user's current health status and lifestyle. For example, if the user is in poor health, the data collection unit can reduce the burden by limiting data collection. For example, if the user is in good health, the data collection unit can collect more detailed data to improve accuracy. For example, the data collection unit can adjust the frequency and timing of data collection according to the user's lifestyle. This reduces the burden on the user by adjusting data collection according to their health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's health status and lifestyle as input and filters the data collection.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of highly important data. For example, if the user is in a hurry, the data collection unit will prioritize the collection of only the minimum necessary data. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can take user emotion data as input and perform data collection using an AI model that determines the priority of the data.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit will prioritize the collection of data related to the destination region. For example, if the user is staying in a specific location for an extended period, the data collection unit will prioritize the collection of data related to that location. This enables highly accurate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's geographical location information as input and prioritizes the collection of highly relevant data to perform data collection.

[0078] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. For example, the data collection unit can prioritize collecting data related to the user's interests from the user's social media activity. For example, the data collection unit can collect relevant data based on information about accounts followed by the user on social media. In this way, data related to the user's interests can be collected by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data collection using an AI model that takes the user's social media activity data as input and collects relevant data.

[0079] The prediction unit can estimate the user's emotions and adjust the way the lifespan prediction is presented based on the estimated emotions. For example, if the user is stressed, the prediction unit will use concise and positive language. If the user is relaxed, the prediction unit will use language that includes detailed information. If the user is in a hurry, the prediction unit will use concise language that gets straight to the point. By adjusting the way the lifespan prediction is presented according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can take user emotion data as input and perform lifespan prediction using an AI model that adjusts the way the lifespan prediction is presented.

[0080] The prediction unit can adjust the level of detail of its predictions based on the importance of the data. For example, the prediction unit can make detailed predictions based on high-importance data. For example, the prediction unit can make simplified predictions based on low-importance data. For example, the prediction unit can adjust the level of detail of its predictions in stages according to the importance of the data. This allows for efficient predictions by adjusting the level of detail of the predictions according to the importance of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes data importance as input and adjusts the level of detail of the predictions.

[0081] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a gene analysis algorithm for predictions based on genetic information. For example, the prediction unit applies a behavioral analysis algorithm for predictions based on lifestyle data. For example, the prediction unit applies a medical statistics algorithm for predictions based on medical data. By applying an appropriate prediction algorithm according to the data category, prediction accuracy is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data category as input and applies an appropriate prediction algorithm.

[0082] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit provides a display method that gets straight to the point. By adjusting the display method of the prediction results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes user emotion data as input and adjusts the display method of the prediction results.

[0083] The prediction unit can determine the priority of predictions based on the data collection timing during the prediction process. For example, the prediction unit may prioritize predictions based on the latest data. For example, the prediction unit may lower the priority of predictions based on older data. For example, the prediction unit may adjust the prediction priority in stages according to the data collection timing. This enables efficient prediction by determining the prediction priority based on the data collection timing. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data collection timing as input and determines the prediction priority.

[0084] The prediction unit can adjust the order of predictions based on the relevance of the data during the prediction process. For example, the prediction unit may prioritize predictions based on highly relevant data. For example, the prediction unit may postpone predictions based on less relevant data. For example, the prediction unit may adjust the order of predictions in stages according to the relevance of the data. This allows for efficient prediction by adjusting the order of predictions based on the relevance of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the relevance of the data as input and adjusts the order of predictions.

[0085] The service provider can estimate the user's emotions and adjust the way the product is presented based on the estimated emotions. For example, if the user is stressed, the service provider will use simple and positive language. If the user is relaxed, the service provider will use language that includes detailed information. If the user is in a hurry, the service provider will use concise language that gets straight to the point. By adjusting the way the product is presented according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes user emotion data as input and adjusts the way the product is presented.

[0086] The service provider can analyze a user's past purchase history to select the most suitable product when providing products. For example, the service provider can suggest related products based on products the user has purchased in the past. For example, the service provider can select the most suitable product from the user's past purchase history. For example, the service provider can suggest the most suitable product based on the user's evaluation of products they have purchased in the past. In this way, by analyzing past purchase history, the service provider can provide the user with the most suitable product. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes the user's past purchase history as input and selects the most suitable product.

[0087] The service provider can customize the products offered based on the user's current living situation when providing products. For example, if the user is in poor health, the service provider will prioritize providing health support products. For example, if the user is planning a trip, the service provider will suggest a travel plan. For example, if the user is experiencing a specific life event, the service provider will provide products related to that event. By customizing products according to the user's living situation, more appropriate products can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can use an AI model that takes the user's living situation data as input and customizes the products offered to provide products.

[0088] The service provider can estimate the user's emotions and determine the priority of products to offer based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize offering products with a relaxing effect. For example, if the user is relaxed, the service provider will prioritize offering products that support health. For example, if the user is in a hurry, the service provider will prioritize offering products that can be used quickly. This allows the service provider to offer more appropriate products by prioritizing products according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes user emotion data as input and determines the priority of products.

[0089] The service provider can provide the most suitable products by considering the user's geographical location information when providing products. For example, if the user is in a specific region, the service provider can provide products related to that region. For example, if the user is on the move, the service provider can provide products related to the destination region. For example, if the user is staying in a specific location for an extended period, the service provider can provide detailed information related to that location. This enables the provision of more appropriate information by providing the most suitable products based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes the user's geographical location information as input and provides the most suitable products.

[0090] The service provider can analyze a user's social media activity and suggest relevant products when providing products. For example, the service provider can provide relevant products based on information shared by the user on social media. For example, the service provider can prioritize providing products related to the user's interests based on their social media activity. For example, the service provider can provide relevant products based on information about accounts followed by the user on social media. In this way, by analyzing social media activity, the service provider can provide products related to the user's interests. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can provide products using an AI model that takes user social media activity data as input and suggests relevant products.

[0091] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning data with a relaxing effect. For example, if the user is relaxed, the learning unit will prioritize learning data related to health support. For example, if the user is in a hurry, the learning unit will prioritize selecting data that can be learned quickly. This allows for more effective learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that takes user emotion data as input and selects training data.

[0092] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can analyze past learning data and select the most effective algorithm. For example, the learning unit can adjust parameters to improve learning accuracy based on past learning data. For example, the learning unit can identify areas for improvement in the learning algorithm based on past learning data and perform optimization. In this way, the learning algorithm can be optimized by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can take past learning data as input and perform learning using an AI model that optimizes the learning algorithm.

[0093] The learning unit can apply different learning methods during training depending on the data's level of anonymity. For example, the learning unit can apply associative learning to data with a high level of anonymity. For example, the learning unit can apply a learning method that involves partial data sharing to data with a moderate level of anonymity. For example, the learning unit can apply a conventional centralized learning method to data with a low level of anonymity. This improves learning accuracy by applying the appropriate learning method according to the data's level of anonymity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that takes the data's level of anonymity as input and applies an appropriate learning method.

[0094] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will lower the learning frequency. For example, if the user is relaxed, the learning unit will increase the learning frequency. For example, if the user is in a hurry, the learning unit will optimize the learning frequency. This allows for more effective learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can take user emotion data as input and perform learning using an AI model that adjusts the learning frequency.

[0095] The learning unit can weight the training data based on the data collection timing during training. For example, the learning unit can assign higher weights to the most recent data, or lower weights to older data. The learning unit can also adjust the weighting of the training data in stages according to the data collection timing. This improves training accuracy by weighting the training data based on the data collection timing. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that takes the data collection timing as input and weights the training data.

[0096] The learning unit can select training data based on the relevance of the data during training. For example, the learning unit may prioritize learning highly relevant data. For example, the learning unit may postpone learning less relevant data. For example, the learning unit may adjust the selection of training data in stages according to the relevance of the data. This improves the learning accuracy by selecting training data based on the relevance of the data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that takes data relevance as input and selects training data.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily suspend data collection and resume it when the user is relaxed. For example, if the user is relaxed, the data collection unit can actively collect detailed data. For example, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. This allows for the collection of more appropriate data by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can take user emotion data as input and perform data collection using an AI model that adjusts the timing of data collection.

[0099] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit prioritizes collecting the types of data that the user has frequently provided in the past. For example, the data collection unit selects the most efficient collection timing from the user's past data collection history. For example, the data collection unit analyzes the accuracy of the data the user has provided in the past and proposes the optimal collection method. In this way, the optimal collection method can be selected by analyzing the past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can take the user's past data collection history as input and perform data collection using an AI model that selects the optimal collection method.

[0100] The data collection unit can filter data based on the user's current health status and lifestyle. For example, if the user is in poor health, the data collection unit can reduce the burden by limiting data collection. For example, if the user is in good health, the data collection unit can collect more detailed data to improve accuracy. For example, the data collection unit can adjust the frequency and timing of data collection according to the user's lifestyle. This reduces the burden on the user by adjusting data collection according to their health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's health status and lifestyle as input and filters the data collection.

[0101] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of highly important data. For example, if the user is in a hurry, the data collection unit will prioritize the collection of only the minimum necessary data. This enables efficient data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can take user emotion data as input and perform data collection using an AI model that determines the priority of the data.

[0102] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is on the move, the data collection unit will prioritize the collection of data related to the destination region. For example, if the user is staying in a specific location for an extended period, the data collection unit will prioritize the collection of data related to that location. This enables highly accurate data collection by collecting highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use an AI model that takes the user's geographical location information as input and prioritizes the collection of highly relevant data to perform data collection.

[0103] The prediction unit can estimate the user's emotions and adjust the way the lifespan prediction is presented based on the estimated emotions. For example, if the user is stressed, the prediction unit will use concise and positive language. If the user is relaxed, the prediction unit will use language that includes detailed information. If the user is in a hurry, the prediction unit will use concise language that gets straight to the point. By adjusting the way the lifespan prediction is presented according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can take user emotion data as input and perform lifespan prediction using an AI model that adjusts the way the lifespan prediction is presented.

[0104] The prediction unit can adjust the level of detail of its predictions based on the importance of the data. For example, the prediction unit can make detailed predictions based on high-importance data. For example, the prediction unit can make simplified predictions based on low-importance data. For example, the prediction unit can adjust the level of detail of its predictions in stages according to the importance of the data. This allows for efficient predictions by adjusting the level of detail of the predictions according to the importance of the data. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes data importance as input and adjusts the level of detail of the predictions.

[0105] The prediction unit can apply different prediction algorithms depending on the data category during prediction. For example, the prediction unit applies a gene analysis algorithm for predictions based on genetic information. For example, the prediction unit applies a behavioral analysis algorithm for predictions based on lifestyle data. For example, the prediction unit applies a medical statistics algorithm for predictions based on medical data. By applying an appropriate prediction algorithm according to the data category, prediction accuracy is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data category as input and applies an appropriate prediction algorithm.

[0106] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. For example, if the user is relaxed, the prediction unit provides a display method that includes detailed information. For example, if the user is in a hurry, the prediction unit provides a display method that gets straight to the point. By adjusting the display method of the prediction results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can make predictions using an AI model that takes user emotion data as input and adjusts the display method of the prediction results.

[0107] The prediction unit can determine the priority of predictions based on the data collection timing during the prediction process. For example, the prediction unit may prioritize predictions based on the latest data. For example, the prediction unit may lower the priority of predictions based on older data. For example, the prediction unit may adjust the prediction priority in stages according to the data collection timing. This enables efficient prediction by determining the prediction priority based on the data collection timing. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can perform predictions using an AI model that takes the data collection timing as input and determines the prediction priority.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects data. The data collection unit collects data such as genetic information, lifestyle habits, medical data, and environmental factors. Specifically, it collects data such as family medical history and genetic information, smoking and drinking habits, exercise habits, diet, medical history, test results, treatment history, living environment, and occupation. Step 2: The prediction unit analyzes the data collected by the collection unit and predicts lifespan. The prediction unit predicts the user's lifespan by considering factors such as genetic information, lifestyle, medical data, and environmental factors. The prediction unit uses machine learning algorithms to predict lifespan. Step 3: The service provider offers products based on the diagnostic results obtained by the prediction service provider. For example, the service provider proposes health support, preventive medicine, insurance, life plans, travel plans, healthy eating, etc., based on the life expectancy prediction. Specifically, it proposes a health support program based on the user's life expectancy prediction results. Step 4: The learning unit performs federated learning when it is difficult to collect data from each company. For example, the learning unit improves learning accuracy using only gradient information while keeping the data confidential. Specifically, it performs learning by sharing only the gradient information while keeping the data held by each company confidential.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, prediction unit, provision unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects genetic information and lifestyle data using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts lifespan by analyzing the collected data. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes health support and life plans based on the prediction results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and improves learning accuracy while protecting data privacy by performing federated learning. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, prediction unit, provision unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects genetic information and lifestyle data using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and predicts lifespan by analyzing the collected data. The provision unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and proposes health support and life plans based on the prediction results. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and improves learning accuracy while protecting data privacy by performing federated learning. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the collection unit, prediction unit, provision unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects genetic information and lifestyle data using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts lifespan by analyzing the collected data. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes health support and life plans based on the prediction results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and improves learning accuracy while protecting data privacy by performing federated learning. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the collection unit, prediction unit, provision unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects genetic information and lifestyle data using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 via the control unit 46A. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts lifespan by analyzing the collected data. The provision unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes health support and life plans based on the prediction results. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12 and improves learning accuracy while protecting data privacy by performing federated learning. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A data collection unit that collects data, A prediction unit analyzes the data collected by the aforementioned collection unit and predicts the lifespan, A supply unit that provides products based on the diagnostic results obtained by the prediction unit, It includes a learning unit that performs federated learning when it is difficult to collect data from each company. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect data such as genetic information, lifestyle, medical data, and environmental factors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The prediction unit, The collected data is analyzed to predict the lifespan of individual users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on life expectancy predictions, we propose health support, preventive medicine, insurance, life planning, travel planning, and healthy eating. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, Improving learning accuracy using only gradient information while keeping it confidential The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The prediction unit, The system estimates the user's emotions and adjusts the representation of lifespan predictions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, When making predictions, adjust the level of detail in the predictions based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, When making predictions, different prediction algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, When making predictions, prioritize predictions based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, During prediction, the order of predictions is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust the way we present our products and services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing products, we analyze the user's past purchase history to select the most suitable product. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing products, customize the products offered based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the products offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing products, we will provide the most suitable products by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing products, we analyze the user's social media activity and suggest related products. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, different learning methods are applied depending on the level of data anonymity. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, training data is selected based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, A prediction unit analyzes the data collected by the aforementioned collection unit and predicts the lifespan, A supply unit that provides products based on the diagnostic results obtained by the prediction unit, It includes a learning unit that performs federated learning when it is difficult to collect data from each company. A system characterized by the following features.

2. The aforementioned collection unit is Collect data such as genetic information, lifestyle, medical data, and environmental factors. The system according to feature 1.

3. The prediction unit, The collected data is analyzed to predict the lifespan of individual users. The system according to feature 1.

4. The aforementioned supply unit is, Based on life expectancy predictions, we propose health support, preventive medicine, insurance, life planning, travel planning, and healthy eating. The system according to feature 1.

5. The aforementioned learning unit, Improving learning accuracy using only gradient information while keeping it confidential The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

Images