System

Abstract

An object of the system according to the embodiment is to analyze personal data and provide personalized advice.SOLUTION: A system includes a data collection unit, an analysis unit, an advice provision unit, and a record update unit. The data collection unit collects personal data accumulated in the smartphone. The analysis unit analyzes the data collected by the data collection unit. The advice providing unit provides personalized advice to the user on the basis of a result analyzed by the analysis unit. The record updating section continuously records and updates daily life data of the user.SELECTED DRAWING: Figure 1

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 technology has not fully realized a system that utilizes personal data to provide personalized advice, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze personal data and provide personalized advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, an advice provision unit, and a record update unit. The data collection unit collects personal data stored in a smartphone. The analysis unit analyzes the data collected by the data collection unit. The advice provision unit provides personalized advice to the user based on the results of the analysis by the analysis unit. The record update unit continuously records and updates the user's daily life data. [Effects of the Invention]

[0007] The system according to the embodiment can analyze an individual's data and provide personalized advice. [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 touch of 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The AI ​​advisor system according to the embodiment of the present invention utilizes personal data stored in a smartphone to provide optimal advice to each individual user. This enables the AI ​​advisor system to provide personalized advice to enrich the user's life.

[0029] The AI ​​advisor system according to the embodiment includes a data collection unit, an analysis unit, an advice provision unit, and a record update unit. The data collection unit collects personal data stored in a smartphone. For example, the data collection unit collects location information of the user. The data collection unit can also collect health data of the user. The data collection unit can also collect app usage history of the user. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining technology. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using statistical analysis. The advice provision unit provides personalized advice to the user based on the results of the analysis by the analysis unit. For example, the advice provision unit can provide health advice. The advice provision unit can also provide lifestyle improvement advice. The advice provision unit can also provide stress reduction advice. The record update unit continuously records and updates daily life data of the user. For example, the record update unit can record sleep data of the user. The record update unit can also record exercise data of the user. The record update unit can also record the user's dietary data, which allows the AI ​​advisor system according to the embodiment to provide personalized advice to enrich the user's life.

[0030] The data collection unit can analyze a user's social media activity and add online behavior patterns and interests to the data. The data collection unit, for example, analyzes a user's social media activity and adds online behavior patterns to the data. For example, it collects content that the user frequently posts and topics that interest them. The data collection unit also analyzes the content of social media posts and reflects the user's interests and concerns in the data. For example, it collects information on accounts that the user follows and groups that the user joins. The data collection unit also monitors the user's social media activity and analyzes online behavior patterns to add them to the data. For example, it collects the content of articles and comments that the user shares. This allows the user's online behavior patterns and interests to be reflected in the data.

[0031] The data collection unit can also integrate data from smart home devices and wearable devices to collect more comprehensive lifestyle data. The data collection unit, for example, integrates data from smart home devices to collect lifestyle data of the user. For example, it collects data from smart lighting and temperature sensors to understand the user's lifestyle environment. The data collection unit also integrates data from wearable devices to record the user's health condition and activity level in detail. For example, it collects heart rate, step count, and sleep data. The data collection unit also integrates data from smart home devices and wearable devices to comprehensively collect lifestyle data of the user. For example, it collects smart speaker usage history and fitness tracker data. In this way, comprehensive lifestyle data can be collected by integrating data from smart home devices and wearable devices.

[0032] The data collection unit can introduce a data sharing function between different users, enabling users with similar lifestyle patterns to mutually refer to advice. The data collection unit, for example, introduces a data sharing function between different users, enabling users with similar lifestyle patterns to mutually refer to advice. For example, it promotes advice sharing between users with the same hobbies. Furthermore, the data collection unit uses the data sharing function to build a system that enables users with similar lifestyle patterns to mutually refer to advice. For example, it allows users with the same health goals to share advice. Furthermore, the data collection unit realizes mutual advice referencing between users with similar lifestyle patterns through data sharing between different users. For example, it allows users who are following the same meal plan to share advice. This enables users with similar lifestyle patterns to mutually refer to advice.

[0033] The advice providing unit can analyze the user's past advice history, evaluate the effectiveness of the advice, and provide more effective advice. The advice providing unit, for example, analyzes the user's past advice history and evaluates the effectiveness of the advice. For example, it analyzes the effectiveness of exercise plans proposed in the past and proposes a more effective plan. The advice providing unit also identifies the most effective advice for the user based on the advice history. For example, it evaluates the effectiveness of past meal plans and proposes an optimal meal plan. The advice providing unit also analyzes the user's past advice history, evaluates the effectiveness of the advice, and provides more effective advice. For example, it analyzes the effectiveness of past stress countermeasures and proposes an optimal relaxation method. This allows more effective advice to be provided based on the past advice history.

[0034] The advice providing unit can provide the user's advice through a voice assistant or a chatbot, thereby realizing a more interactive experience. The advice providing unit can, for example, provide the user's advice through a voice assistant, thereby realizing a more interactive experience. For example, the advice providing unit can provide advice through a voice dialogue. The advice providing unit can also provide personalized advice to the user using a chatbot. For example, the advice can be provided through a text message dialogue. The advice providing unit can also provide interactive advice to the user through the voice assistant or chatbot. For example, the advice can be provided by responding to the user's questions in real time. This makes it possible to provide interactive advice through the voice assistant or chatbot.

[0035] The advice providing unit can collect and analyze region-specific data to provide localized advice to users of different cultures and regions. The advice providing unit collects and analyzes region-specific data to provide localized advice to users of different cultures and regions. For example, advice based on the food culture and climate of the region is provided. The advice providing unit also collects region-specific data to provide personalized advice to users of different cultures and regions. For example, advice based on the health habits and lifestyle of the region is provided. The advice providing unit also analyzes region-specific data to provide localized advice to users of different cultures and regions. For example, advice based on local events and festivals is provided. This makes it possible to provide localized advice that is tailored to different cultures and regions.

[0036] The record updating unit can analyze the user's lifestyle data in time series and identify long-term trends and patterns. The record updating unit, for example, analyzes the user's lifestyle data in time series and identifies long-term trends and patterns. For example, it analyzes changes in the user's health condition and increases or decreases in activity level. The record updating unit also uses time series analysis to identify long-term trends from the user's lifestyle data. For example, it identifies changes in the user's eating patterns and exercise habits. The record updating unit also analyzes the user's lifestyle data in time series and identifies long-term trends and patterns. For example, it analyzes fluctuations in the user's sleep patterns and stress levels. In this way, it is possible to analyze the user's lifestyle data in time series and identify long-term trends and patterns.

[0037] The record updating unit may introduce a sharing function for sharing the user's lifestyle data with family and friends to promote collaborative lifestyle improvements. The record updating unit may, for example, introduce a sharing function for sharing the user's lifestyle data with family and friends to promote collaborative lifestyle improvements. For example, health data of all family members may be shared to jointly manage health. The record updating unit may also use the lifestyle data sharing function to build a system for improving lifestyles together with friends. For example, exercise data may be shared with friends to encourage each other. The record updating unit may also share the user's lifestyle data with family and friends to promote collaborative lifestyle improvements. For example, dietary data of all family members may be shared to create a balanced meal plan. This allows the user's lifestyle data to be shared with family and friends to promote collaborative lifestyle improvements.

[0038] The record update unit can enhance the data synchronization function between different devices, allowing users to record and update data from any device. The record update unit, for example, enhances the data synchronization function between different devices, allowing users to record and update data from any device. For example, data synchronization between a smartphone, a tablet, and a smartwatch is realized. The record update unit also uses the data synchronization function to build a system that allows users to record and update data from multiple devices. For example, data recorded on a smartphone can be checked on a tablet. The record update unit also enhances the data synchronization between different devices, allowing users to record and update data from any device. For example, exercise data recorded on a smartwatch can be checked on a smartphone. This enhances the data synchronization between different devices, allowing users to record and update data from any device.

[0039] The advice providing unit can introduce a sharing function that allows the user to share their preferences with family and friends and suggest joint entertainment and activities. The advice providing unit, for example, introduces a sharing function that allows the user to share their preferences with family and friends and suggest joint entertainment and activities. For example, it can suggest movies and music that the whole family can enjoy. The advice providing unit also uses the preference sharing function to build a system that suggests activities that can be enjoyed together with friends. For example, it can suggest events and activities that can be participated in together with friends. The advice providing unit also shares the user's preferences with family and friends and suggests joint entertainment and activities. For example, it can suggest leisure activities and travel plans that the whole family can enjoy. This allows the user's preferences to be shared with family and friends and suggests joint entertainment and activities.

[0040] The advice providing unit can enhance the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device. The advice providing unit, for example, enhances the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device. For example, data synchronization is realized between a smartphone, a tablet, and a smart speaker. The advice providing unit also uses the preference reflection function to build a system that allows the user to receive advice based on the preferences from multiple devices. For example, preferences set on a smartphone are reflected on a tablet. The advice providing unit also enhances the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device. For example, preferences set on a smart speaker are reflected on a smartphone. This enhances the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The data collection unit can analyze a user's purchasing history and add consumption patterns and preferences to the data. For example, it can collect products and services that the user frequently purchases. The data collection unit can also analyze the user's purchasing history and reflect preferences for specific brands and categories in the data. For example, it can identify the user's favorite brands and product categories. The data collection unit can also monitor the user's purchasing history and analyze consumption patterns to add to the data. For example, it can collect products that the user purchases during specific seasons or events. This makes it possible to collect data based on the user's purchasing history.

[0043] The data collection unit can analyze the user's exercise data and add exercise patterns and health status to the data. For example, it collects the user's daily exercise and fitness activities. The data collection unit also analyzes the user's exercise data and reflects specific exercise habits and health status in the data. For example, it identifies the type and frequency of exercise preferred by the user. The data collection unit also monitors the user's exercise data and analyzes exercise patterns to add to the data. For example, it collects exercises performed by the user during specific time periods. This makes it possible to collect data based on the user's exercise data.

[0044] The data collection unit can analyze a user's travel history and add travel patterns and preferences to the data. For example, it collects data on places and accommodations visited by the user. The data collection unit also analyzes the user's travel history and reflects preferences for specific travel destinations and activities in the data. For example, it identifies the user's preferred travel destinations and activities. The data collection unit also monitors the user's travel history and analyzes travel patterns to add to the data. For example, it collects information on the user's tendency to travel during specific seasons or events. This makes it possible to collect data based on the user's travel history.

[0045] The data collection unit can analyze a user's reading history and add reading patterns and preferences to the data. For example, it collects data on books and articles read by the user. The data collection unit also analyzes the user's reading history and reflects preferences for specific genres and authors in the data. For example, it identifies the user's favorite genres and authors. The data collection unit also monitors the user's reading history, analyzes reading patterns, and adds them to the data. For example, it collects books that the user reads during specific time periods. This makes it possible to collect data based on the user's reading history.

[0046] The advice providing unit can analyze the user's past advice history, evaluate the effectiveness of the advice, and provide more effective advice. For example, it analyzes the effectiveness of exercise plans proposed in the past and proposes a more effective plan. The advice providing unit also identifies the most effective advice for the user based on the advice history. For example, it evaluates the effectiveness of past meal plans and proposes an optimal meal plan. The advice providing unit also analyzes the user's past advice history, evaluates the effectiveness of the advice, and provides more effective advice. For example, it analyzes the effectiveness of past stress countermeasures and proposes an optimal relaxation method. This allows more effective advice to be provided based on the past advice history.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The data collection unit collects personal data stored on the smartphone. For example, the data collection unit can collect the user's location information, health data, and app usage history. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining techniques, machine learning algorithms, and statistical analysis. Step 3: The advice providing unit provides personalized advice to the user based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide health advice, lifestyle improvement advice, and stress reduction advice. Step 4: The record update unit continuously records and updates the user's daily life data. For example, the record update unit can record the user's sleep data, exercise data, and diet data.

[0049] (Example 2) The AI ​​advisor system according to the embodiment of the present invention utilizes personal data stored in a smartphone to provide optimal advice to each individual user. This enables the AI ​​advisor system to provide personalized advice to enrich the user's life.

[0050] The AI ​​advisor system according to the embodiment includes a data collection unit, an analysis unit, an advice provision unit, and a record update unit. The data collection unit collects personal data stored in a smartphone. For example, the data collection unit collects location information of the user. The data collection unit can also collect health data of the user. The data collection unit can also collect app usage history of the user. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining technology. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using statistical analysis. The advice provision unit provides personalized advice to the user based on the results of the analysis by the analysis unit. For example, the advice provision unit can provide health advice. The advice provision unit can also provide lifestyle improvement advice. The advice provision unit can also provide stress reduction advice. The record update unit continuously records and updates daily life data of the user. For example, the record update unit can record sleep data of the user. The record update unit can also record exercise data of the user. The record update unit can also record the user's dietary data, which allows the AI ​​advisor system according to the embodiment to provide personalized advice to enrich the user's life.

[0051] The data collection unit can analyze the user's emotional state in real time and dynamically adjust the frequency and type of data collection according to changes in the emotional state. For example, the data collection unit analyzes the user's emotional state in real time and dynamically adjusts the frequency of data collection according to emotional changes. For example, if the user is feeling stressed, more detailed health data is collected. The data collection unit also uses emotion analysis to change the type of data to collect according to the user's emotional changes. For example, if the user is happy, it focuses on collecting positive activity data. The data collection unit also monitors the user's emotional state in real time and optimizes the data collection method according to emotional changes. For example, if the user is tired, it collects data related to rest. This makes it possible to collect data according to the user's emotional state.

[0052] The data collection unit can analyze a user's social media activity and add online behavior patterns and interests to the data. The data collection unit, for example, analyzes a user's social media activity and adds online behavior patterns to the data. For example, it collects content that the user frequently posts and topics that interest them. The data collection unit also analyzes the content of social media posts and reflects the user's interests and concerns in the data. For example, it collects information on accounts that the user follows and groups that the user joins. The data collection unit also monitors the user's social media activity and analyzes online behavior patterns to add them to the data. For example, it collects the content of articles and comments that the user shares. This allows the user's online behavior patterns and interests to be reflected in the data.

[0053] The data collection unit can use the emotion estimation function to analyze the emotions felt by the user at a specific event or situation and collect data based on those emotions. The data collection unit, for example, uses the emotion estimation function to analyze the emotions felt by the user at a specific event and collect data based on those emotions. For example, the joy and excitement felt by the user when participating in an event is added to the data. The data collection unit also analyzes the emotions felt by the user in a specific situation and collects data based on those emotions. For example, it identifies situations in which the user feels stressed and collects data on that. The data collection unit also uses the emotion estimation function to analyze the emotions felt by the user at a specific event or situation in real time and collects data based on those emotions. For example, it collects data when the user is relaxed. This makes it possible to collect data based on the user's emotions.

[0054] The data collection unit can also integrate data from smart home devices and wearable devices to collect more comprehensive lifestyle data. The data collection unit, for example, integrates data from smart home devices to collect lifestyle data of the user. For example, it collects data from smart lighting and temperature sensors to understand the user's lifestyle environment. The data collection unit also integrates data from wearable devices to record the user's health condition and activity level in detail. For example, it collects heart rate, step count, and sleep data. The data collection unit also integrates data from smart home devices and wearable devices to comprehensively collect lifestyle data of the user. For example, it collects smart speaker usage history and fitness tracker data. In this way, comprehensive lifestyle data can be collected by integrating data from smart home devices and wearable devices.

[0055] The data collection unit can introduce a data sharing function between different users, enabling users with similar lifestyle patterns to mutually refer to advice. The data collection unit, for example, introduces a data sharing function between different users, enabling users with similar lifestyle patterns to mutually refer to advice. For example, it promotes advice sharing between users with the same hobbies. Furthermore, the data collection unit uses the data sharing function to build a system that enables users with similar lifestyle patterns to mutually refer to advice. For example, it allows users with the same health goals to share advice. Furthermore, the data collection unit realizes mutual advice referencing between users with similar lifestyle patterns through data sharing between different users. For example, it allows users who are following the same meal plan to share advice. This enables users with similar lifestyle patterns to mutually refer to advice.

[0056] The data collection unit can use the emotion estimation function to analyze how the user feels about specific data collection and optimize the collection method. The data collection unit, for example, uses the emotion estimation function to analyze how the user feels about specific data collection and optimize the collection method. For example, data collection that causes the user stress is avoided. The data collection unit also analyzes the user's emotional response and understands how the user feels about specific data collection. For example, it prioritizes data collection methods that cause the user positive emotions. The data collection unit also uses the emotion estimation function to analyze in real time how the user feels about specific data collection and optimize the collection method. For example, it collects data when the user is relaxed. This allows the data collection method to be optimized based on the user's emotions.

[0057] The advice providing unit can analyze the emotional state of the user and provide advice according to the emotional state. For example, the advice providing unit analyzes the emotional state of the user and suggests ways to relax if the user is feeling stressed. For example, it provides advice on deep breathing or meditation. The advice providing unit also uses emotion analysis to suggest activities that will give the user positive emotions. For example, it suggests hobbies or activities that bring joy to the user. The advice providing unit also analyzes the emotional state of the user in real time and provides advice according to the emotions. For example, if the user is tired, it suggests ways to rest. This makes it possible to provide advice according to the emotional state of the user.

[0058] The advice providing unit can analyze the user's past advice history, evaluate the effectiveness of the advice, and provide more effective advice. The advice providing unit, for example, analyzes the user's past advice history and evaluates the effectiveness of the advice. For example, it analyzes the effectiveness of exercise plans proposed in the past and proposes a more effective plan. The advice providing unit also identifies the most effective advice for the user based on the advice history. For example, it evaluates the effectiveness of past meal plans and proposes an optimal meal plan. The advice providing unit also analyzes the user's past advice history, evaluates the effectiveness of the advice, and provides more effective advice. For example, it analyzes the effectiveness of past stress countermeasures and proposes an optimal relaxation method. This allows more effective advice to be provided based on the past advice history.

[0059] The advice providing unit can provide the user's advice through a voice assistant or a chatbot, thereby realizing a more interactive experience. The advice providing unit can, for example, provide the user's advice through a voice assistant, thereby realizing a more interactive experience. For example, the advice providing unit can provide advice through a voice dialogue. The advice providing unit can also provide personalized advice to the user using a chatbot. For example, the advice can be provided through a text message dialogue. The advice providing unit can also provide interactive advice to the user through the voice assistant or chatbot. For example, the advice can be provided by responding to the user's questions in real time. This makes it possible to provide interactive advice through the voice assistant or chatbot.

[0060] The advice providing unit can collect and analyze region-specific data to provide localized advice to users of different cultures and regions. The advice providing unit collects and analyzes region-specific data to provide localized advice to users of different cultures and regions. For example, advice based on the food culture and climate of the region is provided. The advice providing unit also collects region-specific data to provide personalized advice to users of different cultures and regions. For example, advice based on the health habits and lifestyle of the region is provided. The advice providing unit also analyzes region-specific data to provide localized advice to users of different cultures and regions. For example, advice based on local events and festivals is provided. This makes it possible to provide localized advice that is tailored to different cultures and regions.

[0061] The advice providing unit can use the emotion estimation function to identify the timing when the user is likely to accept advice and provide the advice at that timing. The advice providing unit, for example, uses the emotion estimation function to identify the timing when the user is likely to accept advice and provide the advice at that timing. For example, the advice providing unit provides advice when the user is relaxed. The advice providing unit also analyzes the emotional state of the user in real time to identify the timing when the user is likely to accept advice. For example, the advice providing unit provides advice when the user is feeling positive. The advice providing unit also uses the emotion estimation function to identify the timing when the user is likely to accept advice and provide the advice at that timing. For example, the advice providing unit provides advice when the user is concentrating. This makes it possible to provide advice at a timing when the user is likely to accept advice.

[0062] The record update unit can analyze the user's emotional state and dynamically adjust the data recording method and frequency according to changes in the emotional state. For example, the record update unit analyzes the user's emotional state and dynamically adjusts the data recording method according to changes in the emotional state. For example, if the user is feeling stressed, detailed health data is recorded. The record update unit also uses emotion analysis to change the data recording frequency according to changes in the user's emotional state. For example, when the user has positive emotions, activity data is recorded frequently. The record update unit also monitors the user's emotional state in real time and optimizes the data recording method according to changes in the emotional state. For example, if the user is tired, detailed data related to rest is recorded. This makes it possible to adjust the data recording method and frequency according to the user's emotional state.

[0063] The record updating unit can analyze the user's lifestyle data in time series and identify long-term trends and patterns. The record updating unit, for example, analyzes the user's lifestyle data in time series and identifies long-term trends and patterns. For example, it analyzes changes in the user's health condition and increases or decreases in activity level. The record updating unit also uses time series analysis to identify long-term trends from the user's lifestyle data. For example, it identifies changes in the user's eating patterns and exercise habits. The record updating unit also analyzes the user's lifestyle data in time series and identifies long-term trends and patterns. For example, it analyzes fluctuations in the user's sleep patterns and stress levels. In this way, it is possible to analyze the user's lifestyle data in time series and identify long-term trends and patterns.

[0064] The record update unit uses the emotion estimation function to analyze the emotion a user has when recording specific data, thereby improving the accuracy of recording. The record update unit, for example, uses the emotion estimation function to analyze the emotion a user has when recording specific data, thereby improving the accuracy of recording. For example, it preferentially records data when the user has positive emotions. The record update unit also analyzes the user's emotional response and understands the emotion when recording specific data. For example, it avoids recording data that makes the user feel stressed. The record update unit also uses the emotion estimation function to analyze the emotion a user has when recording specific data in real time, thereby improving the accuracy of recording. For example, it records data in detail when the user is relaxed. This makes it possible to improve the accuracy of data recording based on the user's emotions.

[0065] The record updating unit may introduce a sharing function for sharing the user's lifestyle data with family and friends to promote collaborative lifestyle improvements. The record updating unit may, for example, introduce a sharing function for sharing the user's lifestyle data with family and friends to promote collaborative lifestyle improvements. For example, health data of all family members may be shared to jointly manage health. The record updating unit may also use the lifestyle data sharing function to build a system for improving lifestyles together with friends. For example, exercise data may be shared with friends to encourage each other. The record updating unit may also share the user's lifestyle data with family and friends to promote collaborative lifestyle improvements. For example, dietary data of all family members may be shared to create a balanced meal plan. This allows the user's lifestyle data to be shared with family and friends to promote collaborative lifestyle improvements.

[0066] The record update unit can enhance the data synchronization function between different devices, allowing users to record and update data from any device. The record update unit, for example, enhances the data synchronization function between different devices, allowing users to record and update data from any device. For example, data synchronization between a smartphone, a tablet, and a smartwatch is realized. The record update unit also uses the data synchronization function to build a system that allows users to record and update data from multiple devices. For example, data recorded on a smartphone can be checked on a tablet. The record update unit also enhances the data synchronization between different devices, allowing users to record and update data from any device. For example, exercise data recorded on a smartwatch can be checked on a smartphone. This enhances the data synchronization between different devices, allowing users to record and update data from any device.

[0067] The record updating unit uses the emotion estimation function to analyze the emotion of the user when recording data, thereby improving the motivation to record. The record updating unit, for example, uses the emotion estimation function to analyze the emotion of the user when recording data, thereby improving the motivation to record. For example, the record updating unit encourages the user to record data when they have positive emotions. The record updating unit also analyzes the user's emotional response, thereby improving the motivation to record data. For example, the record updating unit avoids recording data that makes the user feel stressed. The record updating unit also uses the emotion estimation function to analyze the emotion of the user when recording data in real time, thereby improving the motivation to record. For example, the record updating unit encourages the user to record data when they are relaxed. This makes it possible to improve the motivation to record data based on the user's emotions.

[0068] The advice providing unit can provide advice that reflects the user's preferences. For example, the advice providing unit analyzes the user's emotional state and provides advice that reflects preferences based on the emotion. For example, if the user is sad, the advice providing unit suggests uplifting music. The advice providing unit also uses emotion analysis to suggest activities that will give the user positive emotions. For example, it suggests hobbies or activities that bring joy to the user. The advice providing unit also analyzes the user's emotional state in real time and provides advice that reflects preferences based on the emotion. For example, if the user is tired, it suggests relaxing music. This makes it possible to provide advice based on the user's preferences.

[0069] The advice providing unit uses the emotion estimation function to analyze how the user feels about a specific preference, thereby improving the accuracy of preference-based advice. The advice providing unit, for example, uses the emotion estimation function to analyze how the user feels about a specific preference, thereby improving the accuracy of preference-based advice. For example, preferences that the user feels positive about are prioritized. The advice providing unit also analyzes the user's emotional response to understand how the user feels about the specific preference. For example, preferences that cause the user stress are avoided. The advice providing unit also uses the emotion estimation function to analyze in real time how the user feels about a specific preference, thereby improving the accuracy of preference-based advice. For example, preferences that make the user feel relaxed are prioritized. This makes it possible to improve the accuracy of preference-based advice based on the user's emotions.

[0070] The advice providing unit can introduce a sharing function that allows the user to share their preferences with family and friends and suggest joint entertainment and activities. The advice providing unit, for example, introduces a sharing function that allows the user to share their preferences with family and friends and suggest joint entertainment and activities. For example, it can suggest movies and music that the whole family can enjoy. The advice providing unit also uses the preference sharing function to build a system that suggests activities that can be enjoyed together with friends. For example, it can suggest events and activities that can be participated in together with friends. The advice providing unit also shares the user's preferences with family and friends and suggests joint entertainment and activities. For example, it can suggest leisure activities and travel plans that the whole family can enjoy. This allows the user's preferences to be shared with family and friends and suggests joint entertainment and activities.

[0071] The advice providing unit can enhance the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device. The advice providing unit, for example, enhances the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device. For example, data synchronization is realized between a smartphone, a tablet, and a smart speaker. The advice providing unit also uses the preference reflection function to build a system that allows the user to receive advice based on the preferences from multiple devices. For example, preferences set on a smartphone are reflected on a tablet. The advice providing unit also enhances the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device. For example, preferences set on a smart speaker are reflected on a smartphone. This enhances the function of reflecting preferences across different devices and platforms, allowing the user to receive advice based on the preferences from any device.

[0072] The advice providing unit uses the emotion estimation function to analyze the emotion of the user when discovering a new preference, thereby improving the accuracy of suggesting new preferences. The advice providing unit, for example, uses the emotion estimation function to analyze the emotion of the user when discovering a new preference, thereby improving the accuracy of suggesting new preferences. For example, it prioritizes new preferences in which the user has positive emotions. The advice providing unit also analyzes the user's emotional response to understand the emotion of the user when discovering a new preference. For example, it avoids new preferences that make the user feel stressed. The advice providing unit also uses the emotion estimation function to analyze the emotion of the user when discovering a new preference in real time, thereby improving the accuracy of suggesting new preferences. For example, it prioritizes new preferences that make the user feel relaxed. This improves the accuracy of suggesting new preferences based on the user's emotions.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The data collection unit can analyze a user's purchasing history and add consumption patterns and preferences to the data. For example, it can collect products and services that the user frequently purchases. The data collection unit can also analyze the user's purchasing history and reflect preferences for specific brands and categories in the data. For example, it can identify the user's favorite brands and product categories. The data collection unit can also monitor the user's purchasing history and analyze consumption patterns to add to the data. For example, it can collect products that the user purchases during specific seasons or events. This makes it possible to collect data based on the user's purchasing history.

[0075] The data collection unit can analyze the user's emotional state and provide music and entertainment content that matches the emotional state. For example, if the user is feeling stressed, it can suggest relaxing music. The data collection unit also uses emotion analysis to provide content that the user will have positive emotions with. For example, it can suggest movies or TV shows that will make the user feel happy. The data collection unit also analyzes the user's emotional state in real time and provides entertainment content that matches the emotion. For example, if the user is tired, it can suggest a relaxing podcast. In this way, entertainment content that matches the user's emotional state can be provided.

[0076] The data collection unit can analyze the user's exercise data and add exercise patterns and health status to the data. For example, it collects the user's daily exercise and fitness activities. The data collection unit also analyzes the user's exercise data and reflects specific exercise habits and health status in the data. For example, it identifies the type and frequency of exercise preferred by the user. The data collection unit also monitors the user's exercise data and analyzes exercise patterns to add to the data. For example, it collects exercises performed by the user during specific time periods. This makes it possible to collect data based on the user's exercise data.

[0077] The data collection unit can use the emotion estimation function to analyze how a user feels about a particular meal or drink, and collect data based on that emotion. For example, if a user enjoys a particular meal, it collects data about that meal. The data collection unit also uses emotion analysis to analyze how the user feels about a particular drink and collects that data. For example, it identifies drinks that help the user relax. The data collection unit also analyzes the user's emotional state in real time and collects food and drink data based on the emotion. For example, it collects foods that the user prefers when they are feeling stressed. This makes it possible to collect food and drink data based on the user's emotion.

[0078] The data collection unit can analyze a user's travel history and add travel patterns and preferences to the data. For example, it collects data on places and accommodations visited by the user. The data collection unit also analyzes the user's travel history and reflects preferences for specific travel destinations and activities in the data. For example, it identifies the user's preferred travel destinations and activities. The data collection unit also monitors the user's travel history and analyzes travel patterns to add to the data. For example, it collects information on the user's tendency to travel during specific seasons or events. This makes it possible to collect data based on the user's travel history.

[0079] The data collection unit can analyze the user's emotional state and provide an exercise or fitness plan that matches the emotional state. For example, if the user is feeling stressed, it can suggest an exercise that will help them relax. The data collection unit also uses emotion analysis to provide an exercise that will make the user feel positive. For example, it can suggest a fitness activity that the user can enjoy. The data collection unit also analyzes the user's emotional state in real time and provides an exercise or fitness plan that matches the emotion. For example, if the user is tired, it can suggest some light stretching. This makes it possible to provide an exercise or fitness plan that matches the user's emotional state.

[0080] The data collection unit can analyze a user's reading history and add reading patterns and preferences to the data. For example, it collects data on books and articles read by the user. The data collection unit also analyzes the user's reading history and reflects preferences for specific genres and authors in the data. For example, it identifies the user's favorite genres and authors. The data collection unit also monitors the user's reading history, analyzes reading patterns, and adds them to the data. For example, it collects books that the user reads during specific time periods. This makes it possible to collect data based on the user's reading history.

[0081] The advice providing unit can analyze the user's emotional state and provide dietary and nutritional advice according to the emotional state. For example, if the user is feeling stressed, it will suggest a meal that will help them relax. The advice providing unit also uses emotion analysis to provide meals that will make the user feel positive. For example, it will suggest recipes and ingredients that the user will enjoy. The advice providing unit also analyzes the user's emotional state in real time and provides dietary and nutritional advice according to the emotion. For example, if the user is tired, it will suggest a meal that will replenish energy. In this way, it is possible to provide dietary and nutritional advice according to the user's emotional state.

[0082] The advice providing unit can analyze the user's past advice history, evaluate the effectiveness of the advice, and provide more effective advice. For example, it analyzes the effectiveness of exercise plans proposed in the past and proposes a more effective plan. The advice providing unit also identifies the most effective advice for the user based on the advice history. For example, it evaluates the effectiveness of past meal plans and proposes an optimal meal plan. The advice providing unit also analyzes the user's past advice history, evaluates the effectiveness of the advice, and provides more effective advice. For example, it analyzes the effectiveness of past stress countermeasures and proposes an optimal relaxation method. This allows more effective advice to be provided based on the past advice history.

[0083] The advice providing unit can analyze the emotional state of the user and provide travel and leisure advice according to the emotional state. For example, if the user is feeling stressed, it can suggest travel destinations where the user can relax. The advice providing unit also uses emotion analysis to provide travel destinations where the user has positive emotions. For example, it can suggest tourist spots and activities that the user can enjoy. The advice providing unit also analyzes the emotional state of the user in real time and provides travel and leisure advice according to the emotions. For example, if the user is tired, it can suggest resorts where the user can relax. In this way, it is possible to provide travel and leisure advice according to the emotional state of the user.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The data collection unit collects personal data stored on the smartphone. For example, the data collection unit can collect the user's location information, health data, and app usage history. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze the data using data mining techniques, machine learning algorithms, and statistical analysis. Step 3: The advice providing unit provides personalized advice to the user based on the results of the analysis by the analysis unit. For example, the advice providing unit can provide health advice, lifestyle improvement advice, and stress reduction advice. Step 4: The record update unit continuously records and updates the user's daily life data. For example, the record update unit can record the user's sleep data, exercise data, and diet data.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0127] 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.

[0128] 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.

[0129] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] 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.

[0132] 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.

[0133] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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, to avoid confusion and 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.

[0152] 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. [Explanation of symbols]

[0153] 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. a data collection unit that collects personal data stored in the smartphone; an analysis unit that analyzes the data collected by the data collection unit; an advice providing unit that provides personalized advice to the user based on the results of the analysis by the analysis unit; a record update unit that continuously records and updates the user's daily life data. A system characterized by:

2. The data collection unit Analyzing the user's emotional state in real time and dynamically adjusting the frequency and type of data collection in response to fluctuations in the emotional state.

2. The system of claim 1.

3. The data collection unit Integrate data from smart home devices and wearable devices to collect more comprehensive lifestyle data.

2. The system of claim 1.

4. The advice providing unit Analyzing the emotional state of the user and providing advice according to the emotional state 2. The system of claim 1.

5. The record update unit Analyzing the user's emotional state and dynamically adjusting the method and frequency of recording the data in response to fluctuations in the emotional state.

2. The system of claim 1.

6. The advice providing unit Analyzing the emotional reaction of the user when receiving the advice, and generating the advice that elicits a positive reaction.

2. The system of claim 1.

7. The record update unit Analyzing the emotions of the user when recording the specific data, and improving the accuracy of the recording.

2. The system of claim 1.

8. The advice providing unit Analyzing how the user feels about a particular preference and improving the accuracy of the advice based on the preference.

2. The system of claim 1.

Images