system

Abstract

The system according to the embodiment aims to analyze the biometric information of the user, predict demand in real time, and provide appropriate recommendations. [Solution] A system according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires biometric information of a user. The analysis unit analyzes the biometric information acquired by the acquisition unit and predicts demand. The provision unit provides appropriate recommendations to the user based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies perform segmentation prediction based on user attribute information, but there is room for improvement in terms of providing real-time demand prediction and appropriate recommendations.

[0005] The system according to the embodiment aims to analyze the biometric information of the user, predict demand in real time, and provide appropriate recommendations. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires biometric information of a user. The analysis unit analyzes the biometric information acquired by the acquisition unit and predicts demand. The provision unit provides appropriate recommendations to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the biometric information of the user, predict demand in real time, and provide appropriate recommendations. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A demand forecasting platform according to an embodiment of the present invention is a system that acquires user biometric information and predicts demand in real time. This system collects user attribute information and behavioral data and acquires user biometric information (e.g., heart rate, blood pressure, stress level, etc.) in real time. By combining this data, AI predicts demand and provides optimal recommendations to users. For example, if the system detects that the user has just eaten, it recommends foods that are easy to digest and activities that promote relaxation. If the system detects that the user has just exercised, it recommends foods that are suitable for replenishment and activities that help with recovery. Furthermore, if the user's stress level is high, it recommends relaxing environments and activities that help relieve stress. This platform enables personalized recommendations tailored to user needs, improving user satisfaction. It also enables companies to more accurately forecast demand and develop efficient marketing strategies. Furthermore, specific sensors and devices (e.g., smartwatches and fitness trackers) used to acquire user biometric information are used to clarify AI analysis methods (e.g., deep learning and machine learning algorithms). To protect user privacy, the system also encrypts and anonymizes data and requires user consent, enabling the demand forecasting platform to predict demand in real time based on users' biometric information and provide optimal recommendations.

[0029] A demand forecasting platform according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires biometric information of a user. The biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. The acquisition unit acquires the biometric information using a device such as a smartwatch or a fitness tracker. The acquisition unit can acquire the biometric information of the user in real time. For example, the acquisition unit can monitor heart rate in real time and collect data. The analysis unit analyzes the biometric information acquired by the acquisition unit to predict demand. For example, historical data analysis and machine learning algorithms can be used for demand forecasting, but are not limited to these examples. The analysis unit can analyze the biometric information using, for example, deep learning to predict demand. The analysis unit can also analyze a combination of user attribute information and behavioral data. For example, the analysis unit predicts demand based on the user's past behavioral data. The provision unit provides optimal recommendations to the user based on the analysis results obtained by the analysis unit. Recommendations can include, but are not limited to, food and activities, for example. For example, if the providing unit detects that the user has just eaten, it recommends foods that are easy to digest. Furthermore, if the providing unit detects that the user has just exercised, it can also recommend foods that are suitable for nutritional supplementation. Furthermore, if the user's stress level is high, the providing unit can also recommend a relaxing environment. This allows the demand prediction platform according to the embodiment to predict demand in real time based on the user's biometric information and provide optimal recommendations.

[0030] The demand forecasting platform includes a collection unit that collects attribute information or behavioral data of users. The collection unit collects attribute information and behavioral data of users. The attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The collection unit can collect, for example, the age and gender of users. The collection unit can also collect behavioral data of users. The behavioral data includes, for example, website browsing history, purchase history, etc., but is not limited to these examples. The collection unit can collect, for example, the website browsing history of users. The collection unit can also collect the purchase history of users. In this way, by collecting user attribute information and behavioral data, the collection unit can perform more accurate demand forecasting.

[0031] The demand forecasting platform includes an integrating unit that combines and analyzes collected data. The integrating unit combines and analyzes the collected data. Data combinations include, but are not limited to, user attribute information, behavioral data, and biometric information, for example. The integrating unit can, for example, combine and analyze user attribute information and behavioral data. The integrating unit can also combine and analyze user biometric information. For example, the integrating unit can combine and analyze user heart rate data and behavioral data. The integrating unit can also combine and analyze user blood pressure data and attribute information. In this way, the integrating unit can integrate and analyze the collected data, enabling more accurate demand forecasting.

[0032] The demand forecasting platform includes an encryption unit for protecting user privacy. The encryption unit encrypts data to protect user privacy. Examples of encryption include, but are not limited to, data encryption and access control. The encryption unit can encrypt, for example, the user's biometric information. The encryption unit can also encrypt the user's behavioral data. For example, the encryption unit can encrypt the user's heart rate data. The encryption unit can also encrypt the user's website browsing history. In this way, the encryption unit protects the user's privacy, allowing the system to be used with peace of mind.

[0033] The demand forecasting platform includes a consent acquisition unit for obtaining user consent. The consent acquisition unit executes a process for obtaining user consent. The consent acquisition includes, but is not limited to, for example, the format of a consent form and the timing of obtaining consent. For example, the consent acquisition unit can present a consent form to the user and obtain consent. The consent acquisition unit can also adjust the timing for obtaining user consent. For example, the consent acquisition unit can obtain consent when the user uses the system for the first time. The consent acquisition unit can also obtain consent when the user uses a specific function. In this way, the consent acquisition unit strengthens privacy protection by obtaining user consent.

[0034] The acquisition unit can analyze the user's past biometric information history and select an appropriate acquisition method. For example, the acquisition unit can analyze the user's past heart rate data and acquire biometric information during times when the heart rate is stable. The acquisition unit can also acquire data focusing on times when blood pressure fluctuates most based on the user's past blood pressure data. Furthermore, the acquisition unit can analyze the user's past stress level and acquire biometric information during times when stress is low. This allows the optimal acquisition method to be selected by analyzing the past biometric information history. Past measurement data and trend analysis, etc., are used to analyze the past biometric information history. The type of sensor, the frequency of data acquisition, etc. are taken into consideration when selecting an appropriate acquisition method. This allows the acquisition unit to analyze the user's past biometric information history and select the optimal acquisition method.

[0035] The acquisition unit can perform filtering based on the user's current activity status and environment when acquiring the biometric information. For example, when the user is exercising, the acquisition unit filters heart rate data according to the exercise intensity. Furthermore, when the user is eating, the acquisition unit can also filter blood glucose level data according to the type of meal. Furthermore, when the user is in a relaxing environment, the acquisition unit can filter the biometric information taking into account the influence of environmental sounds and lighting. In this way, more accurate data can be acquired by filtering data based on the user's activity status and environment. The filtering of the activity status and environment takes into account specific situations such as exercising, resting, indoors, and outdoors. In this way, the acquisition unit can perform filtering based on the user's current activity status and environment when acquiring the biometric information.

[0036] The acquisition unit can prioritize acquisition of highly relevant information based on the user's geographical location information when acquiring biometric information. For example, when the user is at high altitude, the acquisition unit prioritizes acquisition of oxygen saturation. Furthermore, when the user is in an urban area, the acquisition unit can prioritize acquisition of heart rate and stress level. Furthermore, when the user is in a natural environment, the acquisition unit can prioritize acquisition of relaxation level and respiratory rate. This allows highly relevant information to be acquired preferentially by taking the user's geographical location information into consideration. GPS data, location information services, etc. are used to acquire the geographical location information. Information based on the user's current location, past behavior history, etc. are taken into consideration when acquiring highly relevant information. This allows the acquisition unit to prioritize acquisition of highly relevant information based on the user's geographical location information when acquiring biometric information.

[0037] The acquisition unit can analyze the user's social media activity when acquiring the biometric information and acquire related information. For example, if the user posts on social media that they are feeling stressed, the acquisition unit can acquire the user's stress level and heart rate. Furthermore, if the user posts about exercise, the acquisition unit can also acquire the user's heart rate and oxygen saturation after exercise. Furthermore, if the user posts about eating, the acquisition unit can also acquire the user's blood sugar level and digestive state after eating. This makes it possible to acquire related information by analyzing the user's social media activity. The content of the post, the number of likes, the number of followers, etc. are used in analyzing the social media activity. This makes it possible for the acquisition unit to analyze the user's social media activity when acquiring the biometric information and acquire related information.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during analysis. For example, if the heart rate indicates an abnormal value, the analysis unit performs a detailed analysis. Furthermore, if the blood pressure is within the normal range, the analysis unit can also perform a simple analysis. Furthermore, if the stress level is high, the analysis unit can perform a detailed analysis to identify the cause of the stress. Thus, by adjusting the level of detail of the analysis based on the importance of the biological information, efficient analysis is possible. Health risks, urgency, etc. are taken into consideration when evaluating the importance of the biological information. The depth of the analysis, data granularity, etc. are taken into consideration when adjusting the level of detail of the analysis. Thus, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the category of biological information during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. The analysis unit can also apply a stress analysis algorithm to stress level data. By applying different analysis algorithms depending on the category of biological information, more accurate analysis is possible. Heart rate, blood pressure, body temperature, etc. are taken into consideration when classifying the category of biological information. A machine learning algorithm, statistical analysis, etc. are used to apply the analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of biological information during analysis.

[0040] The analysis unit can determine the priority of analysis based on the acquisition time of the biological information during analysis. For example, the analysis unit prioritizes analysis of recently acquired biological information. The analysis unit can also prioritize analysis of biological information acquired during a specific time period. Furthermore, the analysis unit can also prioritize analysis of biological information acquired at a specific timing depending on the user's activity status. This enables efficient analysis by determining the priority of analysis based on the acquisition time of the biological information. Criteria for the acquisition time include periodic acquisition and event-based acquisition. The priority is determined based on the importance score, the use of real-time data, and the like. This allows the analysis unit to determine the priority of analysis based on the acquisition time of the biological information during analysis.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the biological information during analysis. For example, the analysis unit can adjust the order of analysis by emphasizing the relevance between heart rate and stress level. The analysis unit can also adjust the order of analysis by emphasizing the relevance between blood pressure and relaxation level. Furthermore, the analysis unit can adjust the order of analysis by emphasizing the relevance between heart rate and oxygen saturation after exercise. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the biological information. The evaluation of the relevance takes into account data correlation and co-occurrence frequency, etc. The adjustment of the order takes into account priority scores, the use of real-time data, etc. This allows the analysis unit to adjust the order of analysis based on the relevance of the biological information during analysis.

[0042] The providing unit can select the optimal recommendation method by analyzing the user's past responses when providing a recommendation. For example, the providing unit prioritizes recommendation methods to which the user has responded favorably in the past. The providing unit can also avoid recommendation methods that the user has rejected in the past. Furthermore, the providing unit can select the optimal recommendation method based on the user's past response data. In this way, the optimal recommendation method can be selected by analyzing the user's past responses. Click-through rate, purchase history, etc. are used in the analysis of past responses. The selection of the optimal recommendation method takes into account the selection of an algorithm, the degree of personalization, etc. In this way, the providing unit can select the optimal recommendation method by analyzing the user's past responses when providing a recommendation.

[0043] The providing unit can customize the recommendation content based on the user's current situation when providing a recommendation. For example, if the user has just eaten, the providing unit can recommend foods that are easy to digest. Furthermore, if the user has just exercised, the providing unit can recommend activities that are useful for recovery. Furthermore, if the user is feeling stressed, the providing unit can recommend a relaxing environment. This allows for more appropriate recommendations by customizing the recommendation content based on the user's current situation. The user's current activity, environment, time of day, etc. are taken into consideration when acquiring the current situation. The user's preferences, past behavioral history, etc. are taken into consideration when customizing the recommendation content. This allows the providing unit to customize the recommendation content based on the user's current situation when providing a recommendation.

[0044] The providing unit can provide optimal recommendations by taking into account the user's geographical location information when providing recommendations. For example, if the user is in an urban area, the providing unit can recommend a nearby cafe where the user can relax. Furthermore, if the user is in a natural environment, the providing unit can also recommend a relaxing walking course. Furthermore, if the user is at home, the providing unit can also recommend a relaxing activity that can be done at home. In this way, optimal recommendations can be provided by taking into account the user's geographical location information. GPS data, location information services, etc. are used to acquire geographical location information. Information based on the user's current location, past behavior history, etc. are taken into account when providing optimal recommendations. In this way, the providing unit can provide optimal recommendations by taking into account the user's geographical location information when providing recommendations.

[0045] The providing unit can analyze the user's social media activity and adjust the recommendation content when providing recommendations. For example, if the user posts on social media that they are feeling stressed, the providing unit can recommend activities that are useful for relieving stress. Furthermore, if the user posts about exercise, the providing unit can recommend activities that are useful for recovering after exercise. Furthermore, if the user posts about eating, the providing unit can recommend activities for relaxing after eating. In this way, by analyzing the user's social media activity, related recommendation content can be provided. The analysis of social media activity uses the post content, the number of likes, the number of followers, etc. The user's preferences, past behavioral history, etc. are taken into consideration when adjusting the recommendation content. In this way, the providing unit can analyze the user's social media activity and adjust the recommendation content when providing recommendations.

[0046] The collection unit can analyze the user's past attribute information and behavioral data and select the optimal collection method. For example, the collection unit analyzes the user's past behavioral data and determines the timing of data collection based on the behavioral pattern. The collection unit can also prioritize collecting data related to specific attributes based on the user's past attribute information. Furthermore, the collection unit can analyze the user's past behavioral data and collect data related to specific events or activities. This allows the optimal collection method to be selected by analyzing past data. Past measurement data and trend analysis, etc., are used to analyze the past attribute information and behavioral data. The type of sensor, the frequency of data acquisition, etc. are taken into consideration when selecting the optimal collection method. This allows the collection unit to analyze the user's past attribute information and behavioral data and select the optimal collection method.

[0047] When collecting attribute information and behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in an urban area, the collection unit prioritizes collecting behavioral data in the urban area. Furthermore, when the user is in a natural environment, the collection unit can also prioritize collecting behavioral data in the natural environment. Furthermore, when the user is at home, the collection unit can also prioritize collecting behavioral data at home. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. GPS data, location information services, etc. are used to acquire the geographical location information. In acquiring highly relevant data, information based on the user's current location, past behavioral history, etc. are taken into account. In this way, when collecting attribute information and behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0048] The integration unit can adjust the level of detail of integration based on the importance of the data when integrating data. For example, the integration unit integrates data with high importance in detail and integrates data with low importance in a simplified manner. The integration unit can also prioritize integration of data with high importance and postpone integration of data with low importance. Furthermore, the integration unit can analyze data with high importance in detail and analyze data with low importance in a simplified manner. In this way, efficient data integration is possible by adjusting the level of detail of integration based on the importance of the data. Health risks, urgency, etc. are taken into consideration when evaluating the importance of data. The depth of integration, granularity of data, etc. are taken into consideration when adjusting the level of detail of integration. In this way, the integration unit can adjust the level of detail of integration based on the importance of the data when integrating data.

[0049] The integration unit can determine the priority of integration based on the time of data acquisition when integrating data. For example, the integration unit prioritizes integration of recently acquired data. The integration unit can also prioritize integration of data acquired during a specific time period. Furthermore, the integration unit can also prioritize integration of data acquired at a specific timing depending on the user's activity status. This enables efficient data integration by determining the priority of integration based on the time of data acquisition. Criteria for the time of acquisition take into account periodic acquisition, event-based acquisition, etc. The priority is determined based on the importance score, the use of real-time data, etc. This allows the integration unit to determine the priority of integration based on the time of data acquisition when integrating data.

[0050] The encryption unit can adjust the encryption detail level based on the importance of the data during encryption. For example, the encryption unit encrypts highly important data in detail and less important data in simple encryption. The encryption unit can also prioritize encrypting highly important data and postpone encrypting less important data. Furthermore, the encryption unit can encrypt highly important data in detail and less important data in simple encryption. This allows for efficient encryption by adjusting the encryption detail level based on the importance of the data. Health risks, urgency, etc. are taken into consideration when evaluating the importance of data. The encryption depth, data granularity, etc. are taken into consideration when adjusting the encryption detail level. This allows the encryption unit to adjust the encryption detail level based on the importance of the data during encryption.

[0051] The encryption unit can determine the encryption priority based on the time of data acquisition during encryption. For example, the encryption unit prioritizes encryption of recently acquired data. The encryption unit can also prioritize encryption of data acquired during a specific time period. Furthermore, the encryption unit can also prioritize encryption of data acquired at a specific timing according to the user's activity status. This enables efficient encryption by determining the encryption priority based on the time of data acquisition. Criteria for the time of acquisition include periodic acquisition and event-based acquisition. The priority is determined based on the importance score, the use of real-time data, and the like. This allows the encryption unit to determine the encryption priority based on the time of data acquisition during encryption.

[0052] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history. For example, the consent acquisition unit preferentially suggests methods to which the user has previously consented. The consent acquisition unit can also avoid methods that the user has previously rejected. Furthermore, the consent acquisition unit can select the optimal consent acquisition method based on the user's past consent history. This makes it possible to select the optimal consent acquisition method by referring to the user's past consent history. The content of past consents and the frequency of consent are used to analyze the past consent history. The format of the consent form and the timing of consent acquisition are taken into consideration when selecting the optimal consent acquisition method. This makes it possible for the consent acquisition unit to select the optimal consent acquisition method by referring to the user's past consent history when obtaining consent.

[0053] The consent acquisition unit can select the optimal consent acquisition method by taking into account the user's device information when acquiring consent. For example, if the user is using a smartphone, the consent acquisition unit can provide a consent acquisition method that matches the screen size. Furthermore, if the user is using a tablet, the consent acquisition unit can also provide a consent acquisition method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the consent acquisition unit can also provide a simple and highly visible consent acquisition method. This allows the optimal consent acquisition method to be selected by taking into account the user's device information. The device type and OS version, etc. are taken into account when acquiring device information. The format of the consent form and the timing of consent acquisition, etc. are taken into account when selecting the optimal consent acquisition method. This allows the consent acquisition unit to select the optimal consent acquisition method by taking into account the user's device information when acquiring consent.

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

[0055] The acquisition unit can not only acquire the user's biometric information, but also acquire environmental information surrounding the user. For example, the acquisition unit can acquire environmental data such as the temperature, humidity, and noise level surrounding the user. The acquisition unit can also acquire environmental information surrounding the user in real time based on the user's location information. This enables more accurate demand forecasting by combining and analyzing the user's biometric information and environmental information. Furthermore, the acquisition unit can recommend an environment in which the user can be comfortable based on the user's environmental information. For example, if the user is in a hot and humid environment, the acquisition unit can recommend a cool place or cooling products. Furthermore, if the user is in a noisy environment, the acquisition unit can recommend a quiet place or noise-canceling headphones. This enables personalized recommendations that take into account the user's environmental information.

[0056] The collection unit can not only collect user attribute information and behavioral data, but also collect the user's social media activities. For example, the collection unit can collect the content of the user's social media posts, the number of likes, the number of followers, etc. The collection unit can also collect the time and frequency of the user's social media activities. This enables demand forecasting that takes the user's social media activities into account. Furthermore, the collection unit can infer the user's interests and concerns based on the user's social media activities. For example, the collection unit can analyze the topics and hashtags that the user frequently posts to infer the user's interests. The collection unit can also infer the user's interests based on the accounts the user follows and the groups the user participates in. This enables personalized recommendations that take the user's social media activities into account.

[0057] The integration unit not only combines and analyzes the collected data, but can also evaluate the reliability of the data. For example, the integration unit can evaluate the reliability of the data based on the source and method of data acquisition. The integration unit can also check the consistency and integrity of the data and exclude unreliable data. This makes it possible to perform demand forecasts based on highly reliable data. Furthermore, by evaluating the reliability of the data, the integration unit can improve the quality of the data. For example, the integration unit can provide feedback on unreliable data and improve the data acquisition method. The integration unit can also perform data quality control by evaluating the reliability of the data. This makes it possible to perform high-quality demand forecasts that take data reliability into account.

[0058] The encryption unit can not only encrypt data to protect user privacy, but also anonymize the data. For example, the encryption unit can anonymize a user's personal information so that a specific individual cannot be identified. The encryption unit can also further strengthen the user's privacy by anonymizing the data. This makes it possible to use data while protecting the user's privacy. Furthermore, the encryption unit can also facilitate data sharing and analysis by anonymizing the data. For example, the encryption unit can share anonymized data with a third party and perform joint data analysis. The encryption unit can also perform data analysis and demand forecasting based on the anonymized data. This makes it possible to use data safely while taking into account the anonymization of the data.

[0059] The consent acquisition unit not only executes the process for obtaining user consent, but also supports the withdrawal or modification of consent. For example, the consent acquisition unit can provide an interface that allows a user to easily withdraw consent if the user wishes to withdraw consent. The consent acquisition unit can also provide an option to change the consent content if the user wishes to change the consent content. This strengthens the protection of user privacy and earns the user's trust. Furthermore, the consent acquisition unit can reflect the withdrawal or modification of consent in real time. For example, the consent acquisition unit can immediately stop the collection and use of data if the user withdraws consent. The consent acquisition unit can also immediately reflect the changes if the user changes the consent content. This enables flexible data use based on the user's consent.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The acquisition unit acquires the user's biometric information. The biometric information includes heart rate, blood pressure, body temperature, etc. The acquisition unit acquires the biometric information in real time using a device such as a smartwatch or fitness tracker. For example, the acquisition unit monitors the heart rate in real time and collects data. Step 2: The analysis unit analyzes the biometric information acquired by the acquisition unit and predicts demand. Past data analysis and machine learning algorithms are used for demand prediction. For example, deep learning can be used to analyze the biometric information, and the analysis can be performed in combination with user attribute information and behavioral data. This allows demand to be predicted based on the user's past behavioral data. Step 3: The provider provides optimal recommendations to the user based on the analysis results obtained by the analyzer. Recommendations include foods and activities. For example, if it detects that the user has just eaten, it will recommend foods that are easy to digest, and if it detects that the user has just exercised, it will recommend foods that are suitable for replenishing nutrients. It can also recommend relaxing environments if the user's stress level is high.

[0062] (Example 2) A demand forecasting platform according to an embodiment of the present invention is a system that acquires user biometric information and predicts demand in real time. This system collects user attribute information and behavioral data and acquires user biometric information (e.g., heart rate, blood pressure, stress level, etc.) in real time. By combining this data, AI predicts demand and provides optimal recommendations to users. For example, if the system detects that the user has just eaten, it recommends foods that are easy to digest and activities that promote relaxation. If the system detects that the user has just exercised, it recommends foods that are suitable for replenishment and activities that help with recovery. Furthermore, if the user's stress level is high, it recommends relaxing environments and activities that help relieve stress. This platform enables personalized recommendations tailored to user needs, improving user satisfaction. It also enables companies to more accurately forecast demand and develop efficient marketing strategies. Furthermore, specific sensors and devices (e.g., smartwatches and fitness trackers) used to acquire user biometric information are used to clarify AI analysis methods (e.g., deep learning and machine learning algorithms). To protect user privacy, the system also encrypts and anonymizes data and requires user consent, enabling the demand forecasting platform to predict demand in real time based on users' biometric information and provide optimal recommendations.

[0063] A demand forecasting platform according to an embodiment includes an acquisition unit, an analysis unit, and a provision unit. The acquisition unit acquires biometric information of a user. The biometric information includes, but is not limited to, heart rate, blood pressure, and body temperature. The acquisition unit acquires the biometric information using a device such as a smartwatch or a fitness tracker. The acquisition unit can acquire the biometric information of the user in real time. For example, the acquisition unit can monitor heart rate in real time and collect data. The analysis unit analyzes the biometric information acquired by the acquisition unit to predict demand. For example, historical data analysis and machine learning algorithms can be used for demand forecasting, but are not limited to these examples. The analysis unit can analyze the biometric information using, for example, deep learning to predict demand. The analysis unit can also analyze a combination of user attribute information and behavioral data. For example, the analysis unit predicts demand based on the user's past behavioral data. The provision unit provides optimal recommendations to the user based on the analysis results obtained by the analysis unit. Recommendations can include, but are not limited to, food and activities, for example. For example, if the providing unit detects that the user has just eaten, it recommends foods that are easy to digest. Furthermore, if the providing unit detects that the user has just exercised, it can also recommend foods that are suitable for nutritional supplementation. Furthermore, if the user's stress level is high, the providing unit can also recommend a relaxing environment. This allows the demand prediction platform according to the embodiment to predict demand in real time based on the user's biometric information and provide optimal recommendations.

[0064] The demand forecasting platform includes a collection unit that collects attribute information or behavioral data of users. The collection unit collects attribute information and behavioral data of users. The attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples. The collection unit can collect, for example, the age and gender of users. The collection unit can also collect behavioral data of users. The behavioral data includes, for example, website browsing history, purchase history, etc., but is not limited to these examples. The collection unit can collect, for example, the website browsing history of users. The collection unit can also collect the purchase history of users. In this way, by collecting user attribute information and behavioral data, the collection unit can perform more accurate demand forecasting.

[0065] The demand forecasting platform includes an integrating unit that combines and analyzes collected data. The integrating unit combines and analyzes the collected data. Data combinations include, but are not limited to, user attribute information, behavioral data, and biometric information, for example. The integrating unit can, for example, combine and analyze user attribute information and behavioral data. The integrating unit can also combine and analyze user biometric information. For example, the integrating unit can combine and analyze user heart rate data and behavioral data. The integrating unit can also combine and analyze user blood pressure data and attribute information. In this way, the integrating unit can integrate and analyze the collected data, enabling more accurate demand forecasting.

[0066] The demand forecasting platform includes an encryption unit for protecting user privacy. The encryption unit encrypts data to protect user privacy. Examples of encryption include, but are not limited to, data encryption and access control. The encryption unit can encrypt, for example, the user's biometric information. The encryption unit can also encrypt the user's behavioral data. For example, the encryption unit can encrypt the user's heart rate data. The encryption unit can also encrypt the user's website browsing history. In this way, the encryption unit protects the user's privacy, allowing the system to be used with peace of mind.

[0067] The demand forecasting platform includes a consent acquisition unit for obtaining user consent. The consent acquisition unit executes a process for obtaining user consent. The consent acquisition includes, but is not limited to, for example, the format of a consent form and the timing of obtaining consent. For example, the consent acquisition unit can present a consent form to the user and obtain consent. The consent acquisition unit can also adjust the timing for obtaining user consent. For example, the consent acquisition unit can obtain consent when the user uses the system for the first time. The consent acquisition unit can also obtain consent when the user uses a specific function. In this way, the consent acquisition unit strengthens privacy protection by obtaining user consent.

[0068] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring biometric information based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit acquires the heart rate and blood pressure when the stress level reaches its peak. Furthermore, if the user is relaxed, the acquisition unit can acquire biometric information when the relaxed state continues, thereby collecting stable data. Furthermore, if the user has just exercised, the acquisition unit can acquire biometric information immediately after exercise and after a certain period of time has passed, thereby monitoring the recovery process. This allows for more accurate data collection by adjusting the timing of acquiring biometric information according to the user's emotions. The estimation of emotions is performed using, for example, techniques such as facial expression recognition and voice analysis. The adjustment of the acquisition timing is performed using real-time data or based on specific events. This allows the acquisition unit to adjust the timing of acquiring biometric information based on the user's emotions.

[0069] The acquisition unit can analyze the user's past biometric information history and select an appropriate acquisition method. For example, the acquisition unit can analyze the user's past heart rate data and acquire biometric information during times when the heart rate is stable. The acquisition unit can also acquire data focusing on times when blood pressure fluctuates most based on the user's past blood pressure data. Furthermore, the acquisition unit can analyze the user's past stress level and acquire biometric information during times when stress is low. This allows the optimal acquisition method to be selected by analyzing the past biometric information history. Past measurement data and trend analysis, etc., are used to analyze the past biometric information history. The type of sensor, the frequency of data acquisition, etc. are taken into consideration when selecting an appropriate acquisition method. This allows the acquisition unit to analyze the user's past biometric information history and select the optimal acquisition method.

[0070] The acquisition unit can perform filtering based on the user's current activity status and environment when acquiring the biometric information. For example, when the user is exercising, the acquisition unit filters heart rate data according to the exercise intensity. Furthermore, when the user is eating, the acquisition unit can also filter blood glucose level data according to the type of meal. Furthermore, when the user is in a relaxing environment, the acquisition unit can filter the biometric information taking into account the influence of environmental sounds and lighting. In this way, more accurate data can be acquired by filtering data based on the user's activity status and environment. The filtering of the activity status and environment takes into account specific situations such as exercising, resting, indoors, and outdoors. In this way, the acquisition unit can perform filtering based on the user's current activity status and environment when acquiring the biometric information.

[0071] The acquisition unit can estimate the user's emotions and determine the priority of the biometric information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring the stress level and heart rate. Furthermore, when the user is relaxed, the acquisition unit can prioritize acquiring the blood pressure and respiratory rate. Furthermore, when the user has just exercised, the acquisition unit can prioritize acquiring the heart rate and oxygen saturation. In this way, by determining the priority of the biometric information based on the user's emotions, important data can be acquired preferentially. The emotion estimation is performed using techniques such as facial expression recognition and voice analysis. The priority is determined based on an importance score or the use of real-time data. In this way, the acquisition unit can determine the priority of the biometric information to be acquired based on the user's emotions.

[0072] The acquisition unit can prioritize acquisition of highly relevant information based on the user's geographical location information when acquiring biometric information. For example, when the user is at high altitude, the acquisition unit prioritizes acquisition of oxygen saturation. Furthermore, when the user is in an urban area, the acquisition unit can prioritize acquisition of heart rate and stress level. Furthermore, when the user is in a natural environment, the acquisition unit can prioritize acquisition of relaxation level and respiratory rate. This allows highly relevant information to be acquired preferentially by taking the user's geographical location information into consideration. GPS data, location information services, etc. are used to acquire the geographical location information. Information based on the user's current location, past behavior history, etc. are taken into consideration when acquiring highly relevant information. This allows the acquisition unit to prioritize acquisition of highly relevant information based on the user's geographical location information when acquiring biometric information.

[0073] The acquisition unit can analyze the user's social media activity when acquiring the biometric information and acquire related information. For example, if the user posts on social media that they are feeling stressed, the acquisition unit can acquire the user's stress level and heart rate. Furthermore, if the user posts about exercise, the acquisition unit can also acquire the user's heart rate and oxygen saturation after exercise. Furthermore, if the user posts about eating, the acquisition unit can also acquire the user's blood sugar level and digestive state after eating. This makes it possible to acquire related information by analyzing the user's social media activity. The content of the post, the number of likes, the number of followers, etc. are used in analyzing the social media activity. This makes it possible for the acquisition unit to analyze the user's social media activity when acquiring the biometric information and acquire related information.

[0074] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can apply an analysis algorithm that emphasizes the stress level. Furthermore, if the user is relaxed, the analysis unit can apply an analysis algorithm that emphasizes the degree of relaxation. Furthermore, if the user has just exercised, the analysis unit can apply an analysis algorithm that emphasizes the recovery state after exercise. This allows for more accurate analysis by adjusting the analysis algorithm based on the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Adjusting the analysis algorithm takes into account parameter optimization, algorithm selection, and the like. This allows the analysis unit to adjust the analysis algorithm based on the user's emotions.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during analysis. For example, if the heart rate indicates an abnormal value, the analysis unit performs a detailed analysis. Furthermore, if the blood pressure is within the normal range, the analysis unit can also perform a simple analysis. Furthermore, if the stress level is high, the analysis unit can perform a detailed analysis to identify the cause of the stress. Thus, by adjusting the level of detail of the analysis based on the importance of the biological information, efficient analysis is possible. Health risks, urgency, etc. are taken into consideration when evaluating the importance of the biological information. The depth of the analysis, data granularity, etc. are taken into consideration when adjusting the level of detail of the analysis. Thus, the analysis unit can adjust the level of detail of the analysis based on the importance of the biological information during analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of biological information during analysis. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a blood pressure variability analysis algorithm to blood pressure data. The analysis unit can also apply a stress analysis algorithm to stress level data. By applying different analysis algorithms depending on the category of biological information, more accurate analysis is possible. Heart rate, blood pressure, body temperature, etc. are taken into consideration when classifying the category of biological information. A machine learning algorithm, statistical analysis, etc. are used to apply the analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of biological information during analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to view. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The adjustment of the display method takes into account the user interface design and information priority, etc. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions.

[0078] The analysis unit can determine the priority of analysis based on the acquisition time of the biological information during analysis. For example, the analysis unit prioritizes analysis of recently acquired biological information. The analysis unit can also prioritize analysis of biological information acquired during a specific time period. Furthermore, the analysis unit can also prioritize analysis of biological information acquired at a specific timing depending on the user's activity status. This enables efficient analysis by determining the priority of analysis based on the acquisition time of the biological information. Criteria for the acquisition time include periodic acquisition and event-based acquisition. The priority is determined based on the importance score, the use of real-time data, and the like. This allows the analysis unit to determine the priority of analysis based on the acquisition time of the biological information during analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the biological information during analysis. For example, the analysis unit can adjust the order of analysis by emphasizing the relevance between heart rate and stress level. The analysis unit can also adjust the order of analysis by emphasizing the relevance between blood pressure and relaxation level. Furthermore, the analysis unit can adjust the order of analysis by emphasizing the relevance between heart rate and oxygen saturation after exercise. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the biological information. The evaluation of the relevance takes into account data correlation and co-occurrence frequency, etc. The adjustment of the order takes into account priority scores, the use of real-time data, etc. This allows the analysis unit to adjust the order of analysis based on the relevance of the biological information during analysis.

[0080] The providing unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can recommend relaxing activities in gentle words. Furthermore, if the user is relaxed, the providing unit can also suggest activities to maintain relaxation. Furthermore, if the user has just exercised, the providing unit can specifically recommend foods suitable for nutritional supplementation. In this way, by adjusting the way recommendations are expressed based on the user's emotions, it is possible to provide recommendations that are easy for the user to accept. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The adjustment of the way recommendations are expressed takes into account wording, visual design, and the like. In this way, the providing unit can adjust the way recommendations are expressed based on the user's emotions.

[0081] The providing unit can select the optimal recommendation method by analyzing the user's past responses when providing a recommendation. For example, the providing unit prioritizes recommendation methods to which the user has responded favorably in the past. The providing unit can also avoid recommendation methods that the user has rejected in the past. Furthermore, the providing unit can select the optimal recommendation method based on the user's past response data. In this way, the optimal recommendation method can be selected by analyzing the user's past responses. Click-through rate, purchase history, etc. are used in the analysis of past responses. The selection of the optimal recommendation method takes into account the selection of an algorithm, the degree of personalization, etc. In this way, the providing unit can select the optimal recommendation method by analyzing the user's past responses when providing a recommendation.

[0082] The providing unit can customize the recommendation content based on the user's current situation when providing a recommendation. For example, if the user has just eaten, the providing unit can recommend foods that are easy to digest. Furthermore, if the user has just exercised, the providing unit can recommend activities that are useful for recovery. Furthermore, if the user is feeling stressed, the providing unit can recommend a relaxing environment. This allows for more appropriate recommendations by customizing the recommendation content based on the user's current situation. The user's current activity, environment, time of day, etc. are taken into consideration when acquiring the current situation. The user's preferences, past behavioral history, etc. are taken into consideration when customizing the recommendation content. This allows the providing unit to customize the recommendation content based on the user's current situation when providing a recommendation.

[0083] The providing unit can estimate the user's emotions and determine the priority of recommendations based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize recommendations that are useful for relieving stress. Furthermore, if the user is relaxed, the providing unit can prioritize recommendations to maintain relaxation. Furthermore, if the user has just exercised, the providing unit can prioritize recommendations that are suitable for nutritional supplementation. In this way, by determining the priority of recommendations based on the user's emotions, important recommendations can be provided preferentially. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The priority is determined taking into account importance scores and the use of real-time data, etc. This allows the providing unit to determine the priority of recommendations based on the user's emotions.

[0084] The providing unit can provide optimal recommendations by taking into account the user's geographical location information when providing recommendations. For example, if the user is in an urban area, the providing unit can recommend a nearby cafe where the user can relax. Furthermore, if the user is in a natural environment, the providing unit can also recommend a relaxing walking course. Furthermore, if the user is at home, the providing unit can also recommend a relaxing activity that can be done at home. In this way, optimal recommendations can be provided by taking into account the user's geographical location information. GPS data, location information services, etc. are used to acquire geographical location information. Information based on the user's current location, past behavior history, etc. are taken into account when providing optimal recommendations. In this way, the providing unit can provide optimal recommendations by taking into account the user's geographical location information when providing recommendations.

[0085] The providing unit can analyze the user's social media activity and adjust the recommendation content when providing recommendations. For example, if the user posts on social media that they are feeling stressed, the providing unit can recommend activities that are useful for relieving stress. Furthermore, if the user posts about exercise, the providing unit can recommend activities that are useful for recovering after exercise. Furthermore, if the user posts about eating, the providing unit can recommend activities for relaxing after eating. In this way, by analyzing the user's social media activity, related recommendation content can be provided. The analysis of social media activity uses the post content, the number of likes, the number of followers, etc. The user's preferences, past behavioral history, etc. are taken into consideration when adjusting the recommendation content. In this way, the providing unit can analyze the user's social media activity and adjust the recommendation content when providing recommendations.

[0086] The collection unit can estimate the user's emotions and adjust the timing of collecting attribute information and behavioral data based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit collects behavioral data when the stress level reaches its peak. Furthermore, if the user is relaxed, the collection unit can also collect attribute information when the relaxed state continues. Furthermore, if the user has just exercised, the collection unit can collect behavioral data immediately after exercise and after a certain period of time has elapsed. This allows for more accurate data collection by adjusting the timing of data collection based on the user's emotions. Emotions are estimated using techniques such as facial expression recognition and voice analysis. Adjusting the timing of collection can take into account the use of real-time data or adjustment based on specific events. This allows the collection unit to estimate the user's emotions and adjust the timing of collecting attribute information and behavioral data based on the estimated user's emotions.

[0087] The collection unit can analyze the user's past attribute information and behavioral data and select the optimal collection method. For example, the collection unit analyzes the user's past behavioral data and determines the timing of data collection based on the behavioral pattern. The collection unit can also prioritize collecting data related to specific attributes based on the user's past attribute information. Furthermore, the collection unit can analyze the user's past behavioral data and collect data related to specific events or activities. This allows the optimal collection method to be selected by analyzing past data. Past measurement data and trend analysis, etc., are used to analyze the past attribute information and behavioral data. The type of sensor, the frequency of data acquisition, etc. are taken into consideration when selecting the optimal collection method. This allows the collection unit to analyze the user's past attribute information and behavioral data and select the optimal collection method.

[0088] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to the stress level. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to the degree of relaxation. Furthermore, if the user has just exercised, the collection unit can prioritize collecting data related to the recovery state after exercise. In this way, by determining the priority of data based on the user's emotions, important data can be collected preferentially. Emotion estimation is performed using techniques such as facial expression recognition and voice analysis. The priority determination takes into account importance scores and the use of real-time data, etc. In this way, the collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions.

[0089] When collecting attribute information and behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in an urban area, the collection unit prioritizes collecting behavioral data in the urban area. Furthermore, when the user is in a natural environment, the collection unit can also prioritize collecting behavioral data in the natural environment. Furthermore, when the user is at home, the collection unit can also prioritize collecting behavioral data at home. In this way, highly relevant data can be collected preferentially by taking into account the user's geographical location information. GPS data, location information services, etc. are used to acquire the geographical location information. In acquiring highly relevant data, information based on the user's current location, past behavioral history, etc. are taken into account. In this way, when collecting attribute information and behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0090] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user's emotions. For example, if the user is feeling stressed, the integration unit can prioritize and integrate data related to the stress level. Furthermore, if the user is relaxed, the integration unit can prioritize and integrate data related to the degree of relaxation. Furthermore, if the user has just exercised, the integration unit can prioritize and integrate data related to the recovery state after exercise. This allows for more accurate data integration by adjusting the data integration method based on the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Adjusting the data integration method takes into account factors such as data weighting and selection of an integration algorithm. This allows the integration unit to estimate the user's emotions and adjust the data integration method based on the estimated user's emotions.

[0091] The integration unit can adjust the level of detail of integration based on the importance of the data when integrating data. For example, the integration unit integrates data with high importance in detail and integrates data with low importance in a simplified manner. The integration unit can also prioritize integration of data with high importance and postpone integration of data with low importance. Furthermore, the integration unit can analyze data with high importance in detail and analyze data with low importance in a simplified manner. In this way, efficient data integration is possible by adjusting the level of detail of integration based on the importance of the data. Health risks, urgency, etc. are taken into consideration when evaluating the importance of data. The depth of integration, granularity of data, etc. are taken into consideration when adjusting the level of detail of integration. In this way, the integration unit can adjust the level of detail of integration based on the importance of the data when integrating data.

[0092] The integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is nervous, the integration unit can provide a simple, highly visible display method. If the user is relaxed, the integration unit can also provide a display method including detailed information. If the user is in a hurry, the integration unit can also provide a display method that focuses on the main points. This allows the integrated data display method to be adjusted based on the user's emotions, making it easier for the user to view. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The adjustment of the display method takes into account factors such as the user interface design and information priority. This allows the integration unit to estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions.

[0093] The integration unit can determine the priority of integration based on the time of data acquisition when integrating data. For example, the integration unit prioritizes integration of recently acquired data. The integration unit can also prioritize integration of data acquired during a specific time period. Furthermore, the integration unit can also prioritize integration of data acquired at a specific timing depending on the user's activity status. This enables efficient data integration by determining the priority of integration based on the time of data acquisition. Criteria for the time of acquisition take into account periodic acquisition, event-based acquisition, etc. The priority is determined based on the importance score, the use of real-time data, etc. This allows the integration unit to determine the priority of integration based on the time of data acquisition when integrating data.

[0094] The encryption unit can estimate the user's emotion and adjust the encryption strength based on the estimated user's emotion. For example, the encryption unit applies high-strength encryption when the user is stressed. The encryption unit can also apply standard encryption when the user is relaxed. Furthermore, the encryption unit can also apply high-strength encryption when the user has just exercised. This allows for appropriate encryption by adjusting the encryption strength based on the user's emotion. Emotion estimation is performed using techniques such as facial expression recognition and voice analysis. The adjustment of encryption strength takes into account the selection of an encryption algorithm, the length of the encryption key, and the like. This allows the encryption unit to estimate the user's emotion and adjust the encryption strength based on the estimated user's emotion.

[0095] The encryption unit can adjust the encryption detail level based on the importance of the data during encryption. For example, the encryption unit encrypts highly important data in detail and less important data in simple encryption. The encryption unit can also prioritize encrypting highly important data and postpone encrypting less important data. Furthermore, the encryption unit can encrypt highly important data in detail and less important data in simple encryption. This allows for efficient encryption by adjusting the encryption detail level based on the importance of the data. Health risks, urgency, etc. are taken into consideration when evaluating the importance of data. The encryption depth, data granularity, etc. are taken into consideration when adjusting the encryption detail level. This allows the encryption unit to adjust the encryption detail level based on the importance of the data during encryption.

[0096] The encryption unit can estimate the user's emotions and adjust the display method of the encrypted data based on the estimated user emotions. For example, if the user is nervous, the encryption unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the encryption unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the encryption unit can provide a display method that focuses on the main points. In this way, by adjusting the display method of the encrypted data based on the user's emotions, it is possible to provide a display that is easy for the user to view. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The adjustment of the display method takes into account the user interface design, information priority, and the like. In this way, the encryption unit can estimate the user's emotions and adjust the display method of the encrypted data based on the estimated user emotions.

[0097] The encryption unit can determine the encryption priority based on the time of data acquisition during encryption. For example, the encryption unit prioritizes encryption of recently acquired data. The encryption unit can also prioritize encryption of data acquired during a specific time period. Furthermore, the encryption unit can also prioritize encryption of data acquired at a specific timing according to the user's activity status. This enables efficient encryption by determining the encryption priority based on the time of data acquisition. Criteria for the time of acquisition include periodic acquisition and event-based acquisition. The priority is determined based on the importance score, the use of real-time data, and the like. This allows the encryption unit to determine the encryption priority based on the time of data acquisition during encryption.

[0098] The consent acquisition unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user's emotions. For example, if the user is feeling stressed, the consent acquisition unit can provide a simple and highly visible consent acquisition method. Furthermore, if the user is relaxed, the consent acquisition unit can also provide a consent acquisition method that includes detailed information. Furthermore, if the user is in a hurry, the consent acquisition unit can also provide a consent acquisition method that focuses on the main points. In this way, by adjusting the consent acquisition method based on the user's emotions, consent acquisition that is easy for the user to accept can be achieved. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The adjustment of the consent acquisition method takes into account the format of the consent form, the timing of consent acquisition, and the like. In this way, the consent acquisition unit can estimate the user's emotions and adjust the consent acquisition method based on the estimated user's emotions.

[0099] When obtaining consent, the consent acquisition unit can select the optimal consent acquisition method by referring to the user's past consent history. For example, the consent acquisition unit preferentially suggests methods to which the user has previously consented. The consent acquisition unit can also avoid methods that the user has previously rejected. Furthermore, the consent acquisition unit can select the optimal consent acquisition method based on the user's past consent history. This makes it possible to select the optimal consent acquisition method by referring to the user's past consent history. The content of past consents and the frequency of consent are used to analyze the past consent history. The format of the consent form and the timing of consent acquisition are taken into consideration when selecting the optimal consent acquisition method. This makes it possible for the consent acquisition unit to select the optimal consent acquisition method by referring to the user's past consent history when obtaining consent.

[0100] The consent acquisition unit can estimate the user's emotions and determine the priority of consent acquisition based on the estimated user's emotions. For example, if the user is feeling stressed, the consent acquisition unit can prioritize consent acquisition that helps relieve stress. Furthermore, if the user is relaxed, the consent acquisition unit can also prioritize consent acquisition to maintain relaxation. Furthermore, if the user has just exercised, the consent acquisition unit can also prioritize consent acquisition that is suitable for nutritional supplementation. In this way, by determining the priority of consent acquisition based on the user's emotions, important consent acquisition can be prioritized. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The priority is determined taking into account importance scores and the use of real-time data, etc. In this way, the consent acquisition unit can estimate the user's emotions and determine the priority of consent acquisition based on the estimated user's emotions.

[0101] The consent acquisition unit can select the optimal consent acquisition method by taking into account the user's device information when acquiring consent. For example, if the user is using a smartphone, the consent acquisition unit can provide a consent acquisition method that matches the screen size. Furthermore, if the user is using a tablet, the consent acquisition unit can also provide a consent acquisition method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the consent acquisition unit can also provide a simple and highly visible consent acquisition method. This allows the optimal consent acquisition method to be selected by taking into account the user's device information. The device type and OS version, etc. are taken into account when acquiring device information. The format of the consent form and the timing of consent acquisition, etc. are taken into account when selecting the optimal consent acquisition method. This allows the consent acquisition unit to select the optimal consent acquisition method by taking into account the user's device information when acquiring consent. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, integration unit, encryption unit, and consent acquisition unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires biometric information of a user using a sensor of the smart device 14. The analysis unit analyzes the biometric information using the specific processing unit 290 of the data processing device 12 and predicts demand. The provision unit provides optimal recommendations to the user via the control unit 46A of the smart device 14. The collection unit collects user attribute information and behavioral data via the communication I / F 44 of the smart device 14. The integration unit integrates and analyzes the data collected by the specific processing unit 290 of the data processing device 12. The encryption unit encrypts the user's data using the specific processing unit 290 of the data processing device 12. The consent acquisition unit acquires user consent using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, integration unit, encryption unit, and consent acquisition unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires biometric information of the user using a sensor of the smart glasses 214. The analysis unit analyzes the biometric information and predicts demand using the specific processing unit 290 of the data processing device 12. The provision unit provides optimal recommendations to the user using the control unit 46A of the smart glasses 214. The collection unit collects user attribute information and behavioral data via the communication I / F 44 of the smart glasses 214. The integration unit integrates and analyzes the data collected by the specific processing unit 290 of the data processing device 12. The encryption unit encrypts the user's data using the specific processing unit 290 of the data processing device 12. The consent acquisition unit acquires user consent using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, integration unit, encryption unit, and consent acquisition unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires biometric information of the user using a sensor of the headset type terminal 314. The analysis unit analyzes the biometric information and predicts demand using the specific processing unit 290 of the data processing device 12. The provision unit provides optimal recommendations to the user using the control unit 46A of the headset type terminal 314. The collection unit collects user attribute information and behavioral data via the communication I / F 44 of the headset type terminal 314. The integration unit integrates and analyzes the data collected by the specific processing unit 290 of the data processing device 12. The encryption unit encrypts the user's data using the specific processing unit 290 of the data processing device 12. The consent acquisition unit acquires user consent using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, analysis unit, provision unit, collection unit, integration unit, encryption unit, and consent acquisition unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires biometric information of a user using a sensor of the robot 414. The analysis unit analyzes the biometric information and predicts demand using the specific processing unit 290 of the data processing device 12. The provision unit provides optimal recommendations to the user using the control unit 46A of the robot 414. The collection unit collects user attribute information and behavioral data via the communication I / F 44 of the robot 414. The integration unit integrates and analyzes the data collected by the specific processing unit 290 of the data processing device 12. The encryption unit encrypts the user data using the specific processing unit 290 of the data processing device 12. The consent acquisition unit acquires user consent using the control unit 46A of the robot 414.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The acquisition unit can not only acquire the user's biometric information, but also acquire environmental information surrounding the user. For example, the acquisition unit can acquire environmental data such as the temperature, humidity, and noise level surrounding the user. The acquisition unit can also acquire environmental information surrounding the user in real time based on the user's location information. This enables more accurate demand forecasting by combining and analyzing the user's biometric information and environmental information. Furthermore, the acquisition unit can recommend an environment in which the user can be comfortable based on the user's environmental information. For example, if the user is in a hot and humid environment, the acquisition unit can recommend a cool place or cooling products. Furthermore, if the user is in a noisy environment, the acquisition unit can recommend a quiet place or noise-canceling headphones. This enables personalized recommendations that take into account the user's environmental information.

[0104] The collection unit can not only collect user attribute information and behavioral data, but also collect the user's social media activities. For example, the collection unit can collect the content of the user's social media posts, the number of likes, the number of followers, etc. The collection unit can also collect the time and frequency of the user's social media activities. This enables demand forecasting that takes the user's social media activities into account. Furthermore, the collection unit can infer the user's interests and concerns based on the user's social media activities. For example, the collection unit can analyze the topics and hashtags that the user frequently posts to infer the user's interests. The collection unit can also infer the user's interests based on the accounts the user follows and the groups the user participates in. This enables personalized recommendations that take the user's social media activities into account.

[0105] The integration unit not only combines and analyzes the collected data, but can also evaluate the reliability of the data. For example, the integration unit can evaluate the reliability of the data based on the source and method of data acquisition. The integration unit can also check the consistency and integrity of the data and exclude unreliable data. This makes it possible to perform demand forecasts based on highly reliable data. Furthermore, by evaluating the reliability of the data, the integration unit can improve the quality of the data. For example, the integration unit can provide feedback on unreliable data and improve the data acquisition method. The integration unit can also perform data quality control by evaluating the reliability of the data. This makes it possible to perform high-quality demand forecasts that take data reliability into account.

[0106] The encryption unit can not only encrypt data to protect user privacy, but also anonymize the data. For example, the encryption unit can anonymize a user's personal information so that a specific individual cannot be identified. The encryption unit can also further strengthen the user's privacy by anonymizing the data. This makes it possible to use data while protecting the user's privacy. Furthermore, the encryption unit can also facilitate data sharing and analysis by anonymizing the data. For example, the encryption unit can share anonymized data with a third party and perform joint data analysis. The encryption unit can also perform data analysis and demand forecasting based on the anonymized data. This makes it possible to use data safely while taking into account the anonymization of the data.

[0107] The consent acquisition unit not only executes the process for obtaining user consent, but also supports the withdrawal or modification of consent. For example, the consent acquisition unit can provide an interface that allows a user to easily withdraw consent if the user wishes to withdraw consent. The consent acquisition unit can also provide an option to change the consent content if the user wishes to change the consent content. This strengthens the protection of user privacy and earns the user's trust. Furthermore, the consent acquisition unit can reflect the withdrawal or modification of consent in real time. For example, the consent acquisition unit can immediately stop the collection and use of data if the user withdraws consent. The consent acquisition unit can also immediately reflect the changes if the user changes the consent content. This enables flexible data use based on the user's consent.

[0108] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring biometric information based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit acquires the heart rate and blood pressure when the stress level reaches its peak. Furthermore, if the user is relaxed, the acquisition unit can acquire biometric information when the relaxed state continues, thereby collecting stable data. Furthermore, if the user has just exercised, the acquisition unit can acquire biometric information immediately after exercise and after a certain period of time has passed, thereby monitoring the recovery process. This allows for more accurate data collection by adjusting the timing of acquiring biometric information according to the user's emotions. The estimation of emotions is performed using, for example, techniques such as facial expression recognition and voice analysis. The adjustment of the acquisition timing is performed using real-time data or based on specific events. This allows the acquisition unit to adjust the timing of acquiring biometric information based on the user's emotions.

[0109] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can apply an analysis algorithm that emphasizes the stress level. Furthermore, if the user is relaxed, the analysis unit can apply an analysis algorithm that emphasizes the degree of relaxation. Furthermore, if the user has just exercised, the analysis unit can apply an analysis algorithm that emphasizes the recovery state after exercise. This allows for more accurate analysis by adjusting the analysis algorithm based on the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Adjusting the analysis algorithm takes into account parameter optimization, algorithm selection, and the like. This allows the analysis unit to adjust the analysis algorithm based on the user's emotions.

[0110] The providing unit can estimate the user's emotions and adjust the way recommendations are expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can recommend relaxing activities in gentle words. If the user is relaxed, the providing unit can also suggest activities to maintain relaxation. Furthermore, if the user has just exercised, the providing unit can specifically recommend foods suitable for replenishing nutrients. By adjusting the way recommendations are expressed based on the user's emotions, it is possible to provide recommendations that are easy for the user to accept. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The adjustment of the way recommendations are expressed takes into account wording, visual design, and the like. This allows the providing unit to adjust the way recommendations are expressed based on the user's emotions.

[0111] The providing unit can estimate the user's emotions and determine the priority of recommendations based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize recommendations that are useful for relieving stress. Furthermore, if the user is relaxed, the providing unit can prioritize recommendations to maintain relaxation. Furthermore, if the user has just exercised, the providing unit can prioritize recommendations that are suitable for nutritional supplementation. In this way, by determining the priority of recommendations based on the user's emotions, important recommendations can be provided preferentially. Emotions are estimated using technologies such as facial expression recognition and voice analysis. The priority is determined taking into account importance scores and the use of real-time data. In this way, the providing unit can determine the priority of recommendations based on the user's emotions.

[0112] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user's emotions. For example, if the user is feeling stressed, the integration unit can prioritize and integrate data related to the stress level. Furthermore, if the user is relaxed, the integration unit can prioritize and integrate data related to the degree of relaxation. Furthermore, if the user has just exercised, the integration unit can prioritize and integrate data related to the recovery state after exercise. This allows for more accurate data integration by adjusting the data integration method based on the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis. Adjusting the data integration method takes into account factors such as data weighting and selection of an integration algorithm. This allows the integration unit to estimate the user's emotions and adjust the data integration method based on the estimated user's emotions.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The acquisition unit acquires the user's biometric information. The biometric information includes heart rate, blood pressure, body temperature, etc. The acquisition unit acquires the biometric information in real time using a device such as a smartwatch or fitness tracker. For example, the acquisition unit monitors the heart rate in real time and collects data. Step 2: The analysis unit analyzes the biometric information acquired by the acquisition unit and predicts demand. Past data analysis and machine learning algorithms are used for demand prediction. For example, deep learning can be used to analyze the biometric information, and the analysis can be performed in combination with user attribute information and behavioral data. This allows demand to be predicted based on the user's past behavioral data. Step 3: The provider provides optimal recommendations to the user based on the analysis results obtained by the analyzer. Recommendations include foods and activities. For example, if it detects that the user has just eaten, it will recommend foods that are easy to digest, and if it detects that the user has just exercised, it will recommend foods that are suitable for replenishing nutrients. It can also recommend relaxing environments if the user's stress level is high.

[0115] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0116] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0117] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0133] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 7, a 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.

[0153] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0159] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0166] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0170] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0171] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0172] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0176] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0177] 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.

[0178] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0179] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0180] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0181] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0183] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0184] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0185] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires biometric information of a user; an analysis unit that analyzes the biometric information acquired by the acquisition unit and predicts demand; a providing unit that provides an appropriate recommendation to a user based on the analysis result obtained by the analyzing unit; Equipped with A system characterized by:

2. A collection unit is provided for collecting user attribute information or behavioral data.

2. The system of claim 1.

3. Equipped with an integration section that combines and analyzes collected data 2. The system of claim 1.

4. Equipped with an encryption section to protect user privacy 2. The system of claim 1.

5. Equipped with a consent acquisition unit for obtaining user consent 2. The system of claim 1.

6. The acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring biometric information based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Analyze the user's past biometric information history and select the appropriate acquisition method 2. The system of claim 1.

8. The acquisition unit When collecting biometric information, filtering is performed based on the user's current activity status and environment.

2. The system of claim 1.

9. The acquisition unit The user's emotions are estimated, and the priority of the biometric information to be acquired is determined based on the estimated user's emotions.

2. The system of claim 1.

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