System

Abstract

An object of a system according to an embodiment is to support management of diet and health improvement that can be easily continued by a user.SOLUTION: A system according to an embodiment includes a generation unit, a reception unit, an aggregation unit, and a transmission unit. The generation unit generates a question. The reception unit receives an answer of the user based on the question generated by the generation unit. The aggregation unit aggregates the answers received by the reception unit. The transmission unit transmits the performance aggregated by the aggregation unit.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] With conventional technology, there was a problem with apps for managing diets and health improvements, where users found the input work tedious and found it difficult to continue using the app.

[0005] The system according to the embodiment aims to support the user in managing a diet and improving their health in a way that is easy to continue. [Means for solving the problem]

[0006] The system according to the embodiment includes a generating unit, a receiving unit, a counting unit, and a transmitting unit. The generating unit generates a question. The receiving unit receives an answer from a user based on the question generated by the generating unit. The counting unit counts the answers received by the receiving unit. The transmitting unit transmits the results counted by the counting unit. [Effects of the Invention]

[0007] The system according to the embodiment can support the user in managing a diet and improving their health in a way that is easy for them to continue. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A management system according to an embodiment of the present invention is designed to prevent users from giving up after three days. This management system provides a function that asks users about what they did and ate each day via their smartphone. The questions are answered with a simple yes or no response, and voice input is also possible. Next, a function is provided that sends daily and monthly performance data via voice and email. For example, the management system automatically launches when a user opens the app and asks questions such as, "Did you exercise today?" and "Did you eat vegetables today?" The user simply responds with a yes or no. Voice input is also possible, with input completed by answering, for example, "yes" or "no." This allows users to effortlessly record their activities. Next, the management system automatically tallys up performance data at a time set by the user and sends it via voice message or email. For example, it may send information such as, "You exercised 10 times this month" or "You ate 5 vegetables this week." This allows users to easily understand their progress and maintain their motivation. This allows users to easily record their daily activities and understand their progress, making it less likely that they will give up after three days. To keep users motivated to continue exercising, the management system allows users to enter their daily exercise records by voice and receive their results by email at the end of the week. It also allows users to record their meals in the same way, providing support for maintaining a healthy diet.

[0029] The management system according to the embodiment includes a generating unit, a receiving unit, a counting unit, and a transmitting unit. The generating unit generates questions. For example, the generating unit generates questions with two choices, "Yes" or "No," to make it easier for the user to answer. The generating unit may also have a function for customizing the content of the medical interview questions. For example, the generating unit may adjust the difficulty and level of detail of the questions depending on the user's health condition and goals. The generating unit may also estimate the user's emotions and adjust the content and timing of the questions based on the estimated user's emotions. The receiving unit accepts the user's answers based on the questions generated by the generating unit. The receiving unit may, for example, accept voice input. The receiving unit may also estimate the user's emotions and adjust the answer acceptance interface based on the estimated user's emotions. The receiving unit may also analyze the user's past answer patterns and select an optimal acceptance method. The counting unit counts the answers accepted by the receiving unit. For example, the counting unit may optimize a counting algorithm by referring to the user's past answer data. The counting unit may also adjust the level of detail of the counting based on the user's health goals and progress. Furthermore, the tallying unit can estimate the user's emotions and adjust the tallying method based on the estimated user's emotions. The sending unit transmits the results tallied by the tallying unit. The sending unit, for example, transmits the daily and monthly results as a voice message. The sending unit can also transmit the daily and monthly results by email. Furthermore, the sending unit can estimate the user's emotions and adjust the timing and method of transmitting the results based on the estimated user's emotions. This allows the management system according to the embodiment to efficiently collect, tally, and transmit user responses.

[0030] The generation unit can generate questions with two choices, Yes or No, to make it easier for the user to answer. Examples of questions with two choices, Yes or No, include, but are not limited to, questions such as "Did you exercise today?" and "Did you eat vegetables today?" The generation unit can generate questions with two choices, Yes or No, to make it easier for the user to answer. The generation unit can also adjust the difficulty and level of detail of the questions according to the user's health condition and goals. For example, if the user is in good health, it can generate detailed questions. The generation unit can also estimate the user's emotions and adjust the content and timing of the questions based on the estimated user's emotions. For example, if the user is feeling stressed, it can generate simple questions to reduce the burden of answering. This makes it easier for the user to answer.

[0031] The reception unit can receive voice input. Examples of voice input include, but are not limited to, recording a user's voice using a microphone and converting the voice into text data using voice recognition technology. The reception unit receives voice input by, for example, the user responding "yes" or "no." The reception unit can also estimate the user's emotions and adjust the response reception interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to reduce the burden of answering. Furthermore, the reception unit can analyze the user's past response patterns and select the optimal response method. For example, the reception unit can prioritize the response methods that the user has frequently used in the past. This allows the user to easily respond by voice.

[0032] The transmission unit can transmit daily and monthly achievements as voice messages. Examples of daily and monthly achievements include, but are not limited to, the number of responses per day and monthly aggregate results. The transmission unit can transmit voice messages such as, for example, "You exercised 10 times this month" or "You ate 5 vegetables this week." The transmission unit can also estimate the user's emotions and adjust the timing and method of transmitting achievements based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of transmitting achievements can be delayed. This allows the user to check their achievements through voice messages.

[0033] The sending unit can send daily and monthly results by email. Daily and monthly results include, but are not limited to, the number of responses per day and monthly aggregate results. For example, the sending unit can send emails with content such as, "You exercised 10 times this month" or "You ate 5 vegetables this week." The sending unit can also estimate the user's emotions and adjust the timing and method of sending the results based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of sending the results can be delayed. This allows the user to check their results by email.

[0034] The generation unit may have a function that allows the user to customize the content of questions in the medical interview. The content of questions in the medical interview may include, but is not limited to, questions about health status and lifestyle habits. The generation unit may have a function that allows the user to customize the content of questions according to, for example, the user's health status and goals. The generation unit may also estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions. For example, if the user is feeling stressed, a simple question may be generated to reduce the burden of answering. This allows the user to customize the content of questions.

[0035] The tallying unit may have a function that allows the user to select a method for tallying the results. Examples of tallying methods include, but are not limited to, calculating an average value and analyzing a distribution. The tallying unit may have a function that allows the user to select a tallying method according to, for example, their health goals and progress. The tallying unit may also estimate the user's emotions and adjust the tallying method based on the estimated user emotions. For example, if the user is feeling stressed, a simple tallying method can be provided to reduce the burden on the user. This allows the user to select a tallying method.

[0036] The generation unit can analyze the user's past answer history and generate optimal questions. For example, the generation unit generates related questions based on questions that the user has frequently answered in the past. The generation unit can also analyze the user's past answer patterns and generate questions that are easy to answer. Furthermore, the generation unit can generate questions suitable for a specific time period from the user's past answer history. This makes it possible to generate optimal questions based on the user's past answer history.

[0037] When generating questions, the generation unit can customize the content of the questions based on the user's lifestyle rhythm and activity patterns. For example, if the user has a habit of exercising in the morning, the generation unit can generate questions about morning exercise. Also, if the user has a habit of eating in the evening, the generation unit can generate questions about eating in the evening. Furthermore, the generation unit can generate questions suitable for weekends based on the user's weekend activity patterns. This makes it possible to generate questions that match the user's lifestyle rhythm and activity patterns.

[0038] When generating a question, the generation unit can adjust the difficulty and level of detail of the question according to the user's health condition and goals. For example, if the user is in good health, the generation unit generates a detailed question. Alternatively, if the user is in poor health, the generation unit can generate a simple question. Furthermore, the generation unit can adjust the difficulty of the question according to the user's health goal. This allows questions to be generated that are appropriate for the user's health condition and goals.

[0039] When generating questions, the generation unit can generate highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate questions related to that area. If the user is traveling, the generation unit can also generate questions related to the user's travel destination. Furthermore, if the user is at home, the generation unit can also generate questions related to activities at home. This makes it possible to generate questions based on the user's geographical location information.

[0040] When generating a question, the generation unit can analyze the user's social media activity and generate a related question. For example, the generation unit generates a question based on the meal details shared by the user on social media. The generation unit can also generate a question based on the user's exercise record on social media. Furthermore, the generation unit can analyze the content posted by the user on social media and generate a related question. This makes it possible to generate a question based on the user's social media activity.

[0041] When generating a question, the generation unit can customize the content of the question by reflecting the user's past feedback. For example, the generation unit generates a related question based on questions that the user found easy to answer in the past. The generation unit can also analyze the user's past feedback and adjust the difficulty level of the question. Furthermore, the generation unit can improve the content of the question by reflecting the user's past feedback. This makes it possible to generate questions based on the user's past feedback.

[0042] When accepting an answer, the acceptance unit can analyze the user's past answer patterns and select the most suitable acceptance method. For example, the acceptance unit can preferentially provide an answer method that the user has frequently used in the past. The acceptance unit can also analyze the user's past answer patterns and suggest an easy answer method. Furthermore, the acceptance unit can select an acceptance method that is suitable for a specific time period from the user's past answer history. This makes it possible to provide an acceptance method based on the user's past answer patterns.

[0043] When accepting an answer, the accepting unit can select the optimal accepting means depending on the user's input method. For example, if the user prefers voice input, the accepting unit can provide voice input preferentially. Furthermore, if the user prefers text input, the accepting unit can also provide text input preferentially. Furthermore, if the user prefers image input, the accepting unit can also provide image input preferentially. This makes it possible to provide an accepting means depending on the user's input method.

[0044] The reception unit can customize the reception method based on the user's current activity status when receiving a response. For example, when the user is exercising, the reception unit provides voice input with priority. Also, when the user is eating, the reception unit can provide simple text input. Furthermore, when the user is taking a break, the reception unit can provide a detailed input method. In this way, a reception method according to the user's current activity status can be provided.

[0045] When accepting answers, the accepting unit can preferentially accept highly relevant answers taking into account the user's geographical location information. For example, when the user is in a specific area, the accepting unit preferentially accepts answers related to that area. Furthermore, when the user is traveling, the accepting unit can also preferentially accept answers related to the user's travel destination. Furthermore, when the user is at home, the accepting unit can also preferentially accept answers related to activities at home. This allows answers based on the user's geographical location information to be preferentially accepted.

[0046] When accepting a response, the accepting unit can analyze the user's social media activity and accept related responses. For example, the accepting unit accepts responses based on the meal details shared by the user on social media. The accepting unit can also accept responses based on the user's exercise records on social media. Furthermore, the accepting unit can analyze the content posted by the user on social media and accept related responses. This makes it possible to accept responses based on the user's social media activity.

[0047] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an answer. For example, the reception unit provides a related reception method based on a method that the user found easiest to use in answering in the past. The reception unit can also analyze the user's past feedback and improve the reception method. Furthermore, the reception unit can customize the reception method by reflecting the user's past feedback. This makes it possible to provide a reception method based on the user's past feedback.

[0048] The tallying unit can optimize the tallying algorithm by referring to the user's past response data when tallying. For example, the tallying unit selects the optimal tallying algorithm based on the user's past response data. The tallying unit can also analyze the user's past response patterns to optimize the tallying algorithm. Furthermore, the tallying unit can select a tallying method suitable for a specific time period based on the user's past response history. This makes it possible to provide the optimal tallying algorithm based on the user's past response data.

[0049] The aggregation unit can adjust the level of detail of the aggregation based on the user's health goals and progress during aggregation. The aggregation unit provides a detailed aggregation according to the user's health goals, for example. The aggregation unit can also provide a simple aggregation based on the user's progress. Furthermore, the aggregation unit can adjust the level of detail of the aggregation taking into account the user's health goals and progress. This makes it possible to provide a level of detail of the aggregation according to the user's health goals and progress.

[0050] The aggregation unit can improve the aggregation method by reflecting user feedback when aggregating. For example, the aggregation unit improves the aggregation method based on user feedback. The aggregation unit can also analyze past user feedback and optimize the aggregation method. Furthermore, the aggregation unit can adjust the level of detail of the aggregation by reflecting user feedback. This makes it possible to provide an aggregation method based on user feedback.

[0051] The aggregation unit can customize the aggregation results by taking into account the user's geographical location information when aggregating data. For example, if the user is in a specific area, the aggregation unit can provide aggregation results related to that area. If the user is traveling, the aggregation unit can also provide aggregation results related to the user's travel destination. Furthermore, if the user is at home, the aggregation unit can also provide aggregation results related to activities at home. This makes it possible to provide aggregation results based on the user's geographical location information.

[0052] The tallying unit can analyze the user's social media activities when tallying the data and reflect related data in the tally. For example, the tallying unit can reflect the meal details the user shared on social media in the tally. The tallying unit can also reflect the user's exercise records on social media in the tally. Furthermore, the tallying unit can analyze the content posted by the user on social media and reflect related data in the tally. This makes it possible to provide tally results based on the user's social media activities.

[0053] The tallying unit can customize the tallying method by reflecting the user's past feedback when tallying. For example, the tallying unit provides a relevant tallying method based on a method that the user found easy to use in the past. The tallying unit can also analyze the user's past feedback and improve the tallying method. Furthermore, the tallying unit can customize the tallying method by reflecting the user's past feedback. This makes it possible to provide a tallying method based on the user's past feedback.

[0054] When transmitting performance data, the transmission unit can optimize the transmission content by referring to the user's past performance data. The transmission unit selects the optimal transmission content based on the user's past performance data, for example. The transmission unit can also analyze the user's past performance patterns and optimize the transmission content. Furthermore, the transmission unit can select transmission content suitable for a specific time period based on the user's past performance history. This makes it possible to provide optimal transmission content based on the user's past performance data.

[0055] When transmitting the achievements, the transmission unit can customize the transmission content based on the user's health goals and progress. For example, the transmission unit provides detailed transmission content according to the user's health goals. The transmission unit can also provide simple transmission content based on the user's progress. Furthermore, the transmission unit can also customize the transmission content taking into account the user's health goals and progress. This makes it possible to provide transmission content according to the user's health goals and progress.

[0056] The transmission unit can improve the transmission method by reflecting user feedback when transmitting actual results. The transmission unit improves the transmission method based on user feedback, for example. The transmission unit can also analyze past user feedback and optimize the transmission method. Furthermore, the transmission unit can customize the transmission method by reflecting user feedback. This makes it possible to provide a transmission method based on user feedback.

[0057] The transmission unit can customize the transmission content by taking into account the user's geographical location information when transmitting the achievements. For example, if the user is in a specific area, the transmission unit can transmit achievements related to that area. Also, if the user is traveling, the transmission unit can transmit achievements related to the travel destination. Furthermore, if the user is at home, the transmission unit can transmit achievements related to activities at home. This makes it possible to provide transmission content based on the user's geographical location information.

[0058] When transmitting the achievements, the transmission unit can analyze the user's social media activities and transmit related achievements. For example, the transmission unit can transmit achievements based on the meal details shared by the user on social media. The transmission unit can also transmit achievements based on the user's exercise records on social media. Furthermore, the transmission unit can analyze the content posted by the user on social media and transmit related achievements. This makes it possible to transmit achievements based on the user's social media activities.

[0059] The transmission unit can customize the transmission method by reflecting the user's past feedback when transmitting the actual results. For example, the transmission unit provides a relevant transmission method based on a method that the user found easy to receive in the past. The transmission unit can also analyze the user's past feedback and improve the transmission method. Furthermore, the transmission unit can customize the transmission method by reflecting the user's past feedback. This makes it possible to provide a transmission method based on the user's past feedback.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The management system can further include a data analysis unit. The data analysis unit has the function of analyzing the user's activity data and performance data in detail and providing the user with useful information. For example, it can analyze the user's exercise patterns and dietary habits and suggest ways to improve their health. The data analysis unit can also predict future health risks based on the user's past data. This allows the user to understand their health condition in detail and take appropriate measures.

[0062] The management system may further include a goal setting unit. The goal setting unit has a function that allows the user to set their own health goals and lifestyle goals. For example, the user can set exercise goals and dietary goals and manage their progress. The goal setting unit can also suggest realistic and achievable goals based on the user's past data. This allows the user to act with specific goals in mind.

[0063] The management system may further include a notification unit. The notification unit has the function of notifying the user of important information and reminders. For example, the notification unit may notify the user of the progress of goals set by the user or important health information. The notification unit may also send notifications at optimal times based on the user's past behavioral patterns. This allows the user to receive important information in a timely manner.

[0064] The management system can further include a calendar section. The calendar section has the function of managing the user's activities and schedules in a calendar format. For example, the user can enter exercise and meal plans into the calendar and visually check their progress. The calendar section can also suggest an optimal schedule based on the user's past data. This allows the user to act in a planned manner.

[0065] The management system can further include a reminder unit. The reminder unit has a function of reminding the user so that they do not forget the goals and tasks they have set. For example, if the user sets a time for exercise, the reminder unit can send a notification encouraging them to exercise at the set time. The reminder unit can also send reminders at the optimal timing based on the user's past behavioral patterns. This allows the user to take planned actions toward achieving their goals.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The generation unit generates a question. For example, the generation unit generates a question with two choices, "Yes" or "No," to make it easier for the user to answer. The generation unit may also have a function that allows the content of the medical interview questions to be customized. For example, the generation unit may adjust the difficulty and level of detail of the questions depending on the user's health condition and goals. Furthermore, the generation unit may estimate the user's emotions and adjust the content and timing of the questions based on the estimated user's emotions. Step 2: The reception unit receives the user's answer based on the question generated by the generation unit. The reception unit receives, for example, a voice input. The reception unit can also estimate the user's emotions and adjust the answer reception interface based on the estimated user emotions. Furthermore, the reception unit can analyze the user's past answer patterns and select the optimal reception method. Step 3: The aggregation unit aggregates the responses received by the reception unit. The aggregation unit, for example, optimizes the aggregation algorithm by referring to the user's past response data. The aggregation unit can also adjust the level of detail of the aggregation based on the user's health goals and progress. Furthermore, the aggregation unit can estimate the user's emotions and adjust the aggregation method based on the estimated user's emotions. Step 4: The transmission unit transmits the results tallied by the aggregation unit. For example, the transmission unit transmits the results by day and month as a voice message. The transmission unit can also transmit the results by day and month by email. Furthermore, the transmission unit can estimate the user's emotions and adjust the timing and method of transmitting the results based on the estimated user's emotions.

[0068] (Example 2) A management system according to an embodiment of the present invention is designed to prevent users from giving up after three days. This management system provides a function that asks users about what they did and ate each day via their smartphone. The questions are answered with a simple yes or no response, and voice input is also possible. Next, a function is provided that sends daily and monthly performance data via voice and email. For example, the management system automatically launches when a user opens the app and asks questions such as, "Did you exercise today?" and "Did you eat vegetables today?" The user simply responds with a yes or no. Voice input is also possible, with input completed by answering, for example, "yes" or "no." This allows users to effortlessly record their activities. Next, the management system automatically tallys up performance data at a time set by the user and sends it via voice message or email. For example, it may send information such as, "You exercised 10 times this month" or "You ate 5 vegetables this week." This allows users to easily understand their progress and maintain their motivation. This allows users to easily record their daily activities and understand their progress, making it less likely that they will give up after three days. To keep users motivated to continue exercising, the management system allows users to enter their daily exercise records by voice and receive their results by email at the end of the week. It also allows users to record their meals in the same way, providing support for maintaining a healthy diet.

[0069] The management system according to the embodiment includes a generating unit, a receiving unit, a counting unit, and a transmitting unit. The generating unit generates questions. For example, the generating unit generates questions with two choices, "Yes" or "No," to make it easier for the user to answer. The generating unit may also have a function for customizing the content of the medical interview questions. For example, the generating unit may adjust the difficulty and level of detail of the questions depending on the user's health condition and goals. The generating unit may also estimate the user's emotions and adjust the content and timing of the questions based on the estimated user's emotions. The receiving unit accepts the user's answers based on the questions generated by the generating unit. The receiving unit may, for example, accept voice input. The receiving unit may also estimate the user's emotions and adjust the answer acceptance interface based on the estimated user's emotions. The receiving unit may also analyze the user's past answer patterns and select an optimal acceptance method. The counting unit counts the answers accepted by the receiving unit. For example, the counting unit may optimize a counting algorithm by referring to the user's past answer data. The counting unit may also adjust the level of detail of the counting based on the user's health goals and progress. Furthermore, the tallying unit can estimate the user's emotions and adjust the tallying method based on the estimated user's emotions. The sending unit transmits the results tallied by the tallying unit. The sending unit, for example, transmits the daily and monthly results as a voice message. The sending unit can also transmit the daily and monthly results by email. Furthermore, the sending unit can estimate the user's emotions and adjust the timing and method of transmitting the results based on the estimated user's emotions. This allows the management system according to the embodiment to efficiently collect, tally, and transmit user responses.

[0070] The generation unit can generate questions with two choices, Yes or No, to make it easier for the user to answer. Examples of questions with two choices, Yes or No, include, but are not limited to, questions such as "Did you exercise today?" and "Did you eat vegetables today?" The generation unit can generate questions with two choices, Yes or No, to make it easier for the user to answer. The generation unit can also adjust the difficulty and level of detail of the questions according to the user's health condition and goals. For example, if the user is in good health, it can generate detailed questions. The generation unit can also estimate the user's emotions and adjust the content and timing of the questions based on the estimated user's emotions. For example, if the user is feeling stressed, it can generate simple questions to reduce the burden of answering. This makes it easier for the user to answer.

[0071] The reception unit can receive voice input. Examples of voice input include, but are not limited to, recording a user's voice using a microphone and converting the voice into text data using voice recognition technology. The reception unit receives voice input by, for example, the user responding "yes" or "no." The reception unit can also estimate the user's emotions and adjust the response reception interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to reduce the burden of answering. Furthermore, the reception unit can analyze the user's past response patterns and select the optimal response method. For example, the reception unit can prioritize the response methods that the user has frequently used in the past. This allows the user to easily respond by voice.

[0072] The transmission unit can transmit daily and monthly achievements as voice messages. Examples of daily and monthly achievements include, but are not limited to, the number of responses per day and monthly aggregate results. The transmission unit can transmit voice messages such as, for example, "You exercised 10 times this month" or "You ate 5 vegetables this week." The transmission unit can also estimate the user's emotions and adjust the timing and method of transmitting achievements based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of transmitting achievements can be delayed. This allows the user to check their achievements through voice messages.

[0073] The sending unit can send daily and monthly results by email. Daily and monthly results include, but are not limited to, the number of responses per day and monthly aggregate results. For example, the sending unit can send emails with content such as, "You exercised 10 times this month" or "You ate 5 vegetables this week." The sending unit can also estimate the user's emotions and adjust the timing and method of sending the results based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of sending the results can be delayed. This allows the user to check their results by email.

[0074] The generation unit may have a function that allows the user to customize the content of questions in the medical interview. The content of questions in the medical interview may include, but is not limited to, questions about health status and lifestyle habits. The generation unit may have a function that allows the user to customize the content of questions according to, for example, the user's health status and goals. The generation unit may also estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions. For example, if the user is feeling stressed, a simple question may be generated to reduce the burden of answering. This allows the user to customize the content of questions.

[0075] The tallying unit may have a function that allows the user to select a method for tallying the results. Examples of tallying methods include, but are not limited to, calculating an average value and analyzing a distribution. The tallying unit may have a function that allows the user to select a tallying method according to, for example, their health goals and progress. The tallying unit may also estimate the user's emotions and adjust the tallying method based on the estimated user emotions. For example, if the user is feeling stressed, a simple tallying method can be provided to reduce the burden on the user. This allows the user to select a tallying method.

[0076] The generation unit can estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates simple questions to reduce the burden of answering. The generation unit can also generate detailed questions to collect more specific information if the user is relaxed. Furthermore, if the user is in a hurry, the generation unit can generate questions that can be answered in a short time to encourage quick input. This makes it possible to generate questions that correspond to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0077] The generation unit can analyze the user's past answer history and generate optimal questions. For example, the generation unit generates related questions based on questions that the user has frequently answered in the past. The generation unit can also analyze the user's past answer patterns and generate questions that are easy to answer. Furthermore, the generation unit can generate questions suitable for a specific time period from the user's past answer history. This makes it possible to generate optimal questions based on the user's past answer history.

[0078] When generating questions, the generation unit can customize the content of the questions based on the user's lifestyle rhythm and activity patterns. For example, if the user has a habit of exercising in the morning, the generation unit can generate questions about morning exercise. Also, if the user has a habit of eating in the evening, the generation unit can generate questions about eating in the evening. Furthermore, the generation unit can generate questions suitable for weekends based on the user's weekend activity patterns. This makes it possible to generate questions that match the user's lifestyle rhythm and activity patterns.

[0079] When generating a question, the generation unit can adjust the difficulty and level of detail of the question according to the user's health condition and goals. For example, if the user is in good health, the generation unit generates a detailed question. Alternatively, if the user is in poor health, the generation unit can generate a simple question. Furthermore, the generation unit can adjust the difficulty of the question according to the user's health goal. This allows questions to be generated that are appropriate for the user's health condition and goals.

[0080] The generation unit can estimate the user's emotions and adjust the order of questions based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can start with simple questions. Also, if the user is relaxed, the generation unit can start with detailed questions. Furthermore, if the user is in a hurry, the generation unit can prioritize important questions. This makes it possible to adjust the order of questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0081] When generating questions, the generation unit can generate highly relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the generation unit can generate questions related to that area. If the user is traveling, the generation unit can also generate questions related to the user's travel destination. Furthermore, if the user is at home, the generation unit can also generate questions related to activities at home. This makes it possible to generate questions based on the user's geographical location information.

[0082] When generating a question, the generation unit can analyze the user's social media activity and generate a related question. For example, the generation unit generates a question based on the meal details shared by the user on social media. The generation unit can also generate a question based on the user's exercise record on social media. Furthermore, the generation unit can analyze the content posted by the user on social media and generate a related question. This makes it possible to generate a question based on the user's social media activity.

[0083] When generating a question, the generation unit can customize the content of the question by reflecting the user's past feedback. For example, the generation unit generates a related question based on questions that the user found easy to answer in the past. The generation unit can also analyze the user's past feedback and adjust the difficulty level of the question. Furthermore, the generation unit can improve the content of the question by reflecting the user's past feedback. This makes it possible to generate questions based on the user's past feedback.

[0084] The reception unit can estimate the user's emotions and adjust the answer reception interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface to reduce the burden of answering. Furthermore, if the user is relaxed, the reception unit can provide a detailed interface to collect more specific information. Furthermore, if the user is in a hurry, the reception unit can provide an interface that allows the user to answer in a short time and encourage quick input. This makes it possible to provide an interface that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] When accepting an answer, the acceptance unit can analyze the user's past answer patterns and select the most suitable acceptance method. For example, the acceptance unit can preferentially provide an answer method that the user has frequently used in the past. The acceptance unit can also analyze the user's past answer patterns and suggest an easy answer method. Furthermore, the acceptance unit can select an acceptance method that is suitable for a specific time period from the user's past answer history. This makes it possible to provide an acceptance method based on the user's past answer patterns.

[0086] When accepting an answer, the accepting unit can select the optimal accepting means depending on the user's input method. For example, if the user prefers voice input, the accepting unit can provide voice input preferentially. Furthermore, if the user prefers text input, the accepting unit can also provide text input preferentially. Furthermore, if the user prefers image input, the accepting unit can also provide image input preferentially. This makes it possible to provide an accepting means depending on the user's input method.

[0087] The reception unit can customize the reception method based on the user's current activity status when receiving a response. For example, when the user is exercising, the reception unit provides voice input with priority. Also, when the user is eating, the reception unit can provide simple text input. Furthermore, when the user is taking a break, the reception unit can provide a detailed input method. In this way, a reception method according to the user's current activity status can be provided.

[0088] The reception unit can estimate the user's emotions and determine the priority of answer reception based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving simple questions. Furthermore, when the user is relaxed, the reception unit can also prioritize receiving detailed questions. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving important questions. This allows answers to be received in a priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0089] When accepting answers, the accepting unit can preferentially accept highly relevant answers taking into account the user's geographical location information. For example, when the user is in a specific area, the accepting unit preferentially accepts answers related to that area. Furthermore, when the user is traveling, the accepting unit can also preferentially accept answers related to the user's travel destination. Furthermore, when the user is at home, the accepting unit can also preferentially accept answers related to activities at home. This allows answers based on the user's geographical location information to be preferentially accepted.

[0090] When accepting a response, the accepting unit can analyze the user's social media activity and accept related responses. For example, the accepting unit accepts responses based on the meal details shared by the user on social media. The accepting unit can also accept responses based on the user's exercise records on social media. Furthermore, the accepting unit can analyze the content posted by the user on social media and accept related responses. This makes it possible to accept responses based on the user's social media activity.

[0091] The reception unit can customize the reception method by reflecting the user's past feedback when receiving an answer. For example, the reception unit provides a related reception method based on a method that the user found easiest to use in answering in the past. The reception unit can also analyze the user's past feedback and improve the reception method. Furthermore, the reception unit can customize the reception method by reflecting the user's past feedback. This makes it possible to provide a reception method based on the user's past feedback.

[0092] The aggregation unit can estimate the user's emotions and adjust the aggregation method based on the estimated user emotions. For example, if the user is feeling stressed, the aggregation unit provides a simple aggregation method to reduce the burden. Furthermore, if the user is relaxed, the aggregation unit can provide a detailed aggregation method to collect more specific information. Furthermore, if the user is in a hurry, the aggregation unit can provide a method that can aggregate data in a short time, encouraging quick aggregation. This makes it possible to provide an aggregation method that suits the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The tallying unit can optimize the tallying algorithm by referring to the user's past response data when tallying. For example, the tallying unit selects the optimal tallying algorithm based on the user's past response data. The tallying unit can also analyze the user's past response patterns to optimize the tallying algorithm. Furthermore, the tallying unit can select a tallying method suitable for a specific time period based on the user's past response history. This makes it possible to provide the optimal tallying algorithm based on the user's past response data.

[0094] The aggregation unit can adjust the level of detail of the aggregation based on the user's health goals and progress during aggregation. The aggregation unit provides a detailed aggregation according to the user's health goals, for example. The aggregation unit can also provide a simple aggregation based on the user's progress. Furthermore, the aggregation unit can adjust the level of detail of the aggregation taking into account the user's health goals and progress. This makes it possible to provide a level of detail of the aggregation according to the user's health goals and progress.

[0095] The aggregation unit can improve the aggregation method by reflecting user feedback when aggregating. For example, the aggregation unit improves the aggregation method based on user feedback. The aggregation unit can also analyze past user feedback and optimize the aggregation method. Furthermore, the aggregation unit can adjust the level of detail of the aggregation by reflecting user feedback. This makes it possible to provide an aggregation method based on user feedback.

[0096] The aggregation unit can estimate the user's emotions and adjust the display method of the aggregation results based on the estimated user emotions. For example, if the user is feeling stressed, the aggregation unit can provide a simple display method to reduce the burden on the user. Furthermore, if the user is relaxed, the aggregation unit can provide a detailed display method to collect more specific information. Furthermore, if the user is in a hurry, the aggregation unit can provide a method that can display the results in a short time to encourage quick display. This makes it possible to provide a display method of the aggregation results that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0097] The aggregation unit can customize the aggregation results by taking into account the user's geographical location information when aggregating data. For example, if the user is in a specific area, the aggregation unit can provide aggregation results related to that area. If the user is traveling, the aggregation unit can also provide aggregation results related to the user's travel destination. Furthermore, if the user is at home, the aggregation unit can also provide aggregation results related to activities at home. This makes it possible to provide aggregation results based on the user's geographical location information.

[0098] The tallying unit can analyze the user's social media activities when tallying the data and reflect related data in the tally. For example, the tallying unit can reflect the meal details the user shared on social media in the tally. The tallying unit can also reflect the user's exercise records on social media in the tally. Furthermore, the tallying unit can analyze the content posted by the user on social media and reflect related data in the tally. This makes it possible to provide tally results based on the user's social media activities.

[0099] The tallying unit can customize the tallying method by reflecting the user's past feedback when tallying. For example, the tallying unit provides a relevant tallying method based on a method that the user found easy to use in the past. The tallying unit can also analyze the user's past feedback and improve the tallying method. Furthermore, the tallying unit can customize the tallying method by reflecting the user's past feedback. This makes it possible to provide a tallying method based on the user's past feedback.

[0100] The transmission unit can estimate the user's emotions and adjust the timing and method of transmitting the achievements based on the estimated user emotions. For example, if the user is feeling stressed, the transmission unit can delay the timing of transmitting the achievements. Furthermore, if the user is relaxed, the transmission unit can also advance the timing of transmitting the achievements. Furthermore, if the user is in a hurry, the transmission unit can transmit the achievements in a concise manner. This makes it possible to provide the timing and method of transmitting the achievements according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] When transmitting performance data, the transmission unit can optimize the transmission content by referring to the user's past performance data. The transmission unit selects the optimal transmission content based on the user's past performance data, for example. The transmission unit can also analyze the user's past performance patterns and optimize the transmission content. Furthermore, the transmission unit can select transmission content suitable for a specific time period based on the user's past performance history. This makes it possible to provide optimal transmission content based on the user's past performance data.

[0102] When transmitting the achievements, the transmission unit can customize the transmission content based on the user's health goals and progress. For example, the transmission unit provides detailed transmission content according to the user's health goals. The transmission unit can also provide simple transmission content based on the user's progress. Furthermore, the transmission unit can also customize the transmission content taking into account the user's health goals and progress. This makes it possible to provide transmission content according to the user's health goals and progress.

[0103] The transmission unit can improve the transmission method by reflecting user feedback when transmitting actual results. The transmission unit improves the transmission method based on user feedback, for example. The transmission unit can also analyze past user feedback and optimize the transmission method. Furthermore, the transmission unit can customize the transmission method by reflecting user feedback. This makes it possible to provide a transmission method based on user feedback.

[0104] The transmission unit can estimate the user's emotions and determine the priority of achievement transmission based on the estimated user emotions. For example, if the user is feeling stressed, the transmission unit can prioritize sending important achievements. Furthermore, if the user is relaxed, the transmission unit can also prioritize sending detailed achievements. Furthermore, if the user is in a hurry, the transmission unit can also prioritize sending concise achievements. This allows achievements to be transmitted in a priority order according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0105] The transmission unit can customize the transmission content by taking into account the user's geographical location information when transmitting the achievements. For example, if the user is in a specific area, the transmission unit can transmit achievements related to that area. Also, if the user is traveling, the transmission unit can transmit achievements related to the travel destination. Furthermore, if the user is at home, the transmission unit can transmit achievements related to activities at home. This makes it possible to provide transmission content based on the user's geographical location information.

[0106] When transmitting the achievements, the transmission unit can analyze the user's social media activities and transmit related achievements. For example, the transmission unit can transmit achievements based on the meal details shared by the user on social media. The transmission unit can also transmit achievements based on the user's exercise records on social media. Furthermore, the transmission unit can analyze the content posted by the user on social media and transmit related achievements. This makes it possible to transmit achievements based on the user's social media activities.

[0107] The transmission unit can customize the transmission method by reflecting the user's past feedback when transmitting the actual results. For example, the transmission unit provides a relevant transmission method based on a method that the user found easy to receive in the past. The transmission unit can also analyze the user's past feedback and improve the transmission method. Furthermore, the transmission unit can customize the transmission method by reflecting the user's past feedback. This makes it possible to provide a transmission method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, reception unit, counting unit, and transmission unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the counting unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, reception unit, counting unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the counting unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, reception unit, counting unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the counting unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, reception unit, counting unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the counting unit is realized by the specific processing unit 290 of the data processing device 12. For example, the transmission unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The management system may further include a reward unit. The reward unit has a function of providing a reward when the user achieves a goal set by the user. For example, if the user continues to exercise for a certain period of time, the reward unit may provide the user with coupons or points. The reward unit may also estimate the user's emotions and adjust the type and timing of rewards based on the estimated emotions. For example, if the user is losing motivation, the reward unit may maintain the user's motivation by providing a reward early on. This allows the user to continue making efforts toward achieving their goals.

[0110] The management system may further include an advice unit. The advice unit has a function of providing advice on health and lifestyle habits based on the user's response data and performance data. For example, if the user is not getting enough exercise, the advice unit can suggest an appropriate exercise method and frequency. The advice unit can also estimate the user's emotions and adjust the content and timing of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can suggest relaxation methods and stress relief methods. This allows the user to receive specific advice on maintaining a healthy lifestyle.

[0111] The management system may further include a community unit. The community unit has a function for users to interact with each other and encourage each other. For example, a user can share exercise and meal records and receive comments and supportive messages from other users. The community unit may also estimate the user's emotions and adjust the content and timing of interactions based on the estimated emotions. For example, if a user feels lonely, the feeling of loneliness can be alleviated by promoting interactions with other users. This allows the user to work together with other users to achieve their goals.

[0112] The management system may further include a reminder unit. The reminder unit has a function of reminding the user so that they do not forget the goals and tasks they have set. For example, if the user sets a time for exercise, the reminder unit can send a notification encouraging them to exercise at the set time. The reminder unit can also estimate the user's emotions and adjust the content and timing of the reminder based on the estimated emotions. For example, if the user is tired, the reminder notification can be sent in a soft tone. This allows the user to take planned action toward achieving their goals.

[0113] The management system may further include a feedback unit. The feedback unit has a function of providing feedback on the user's activities and achievements. For example, if the user continues exercising, the feedback unit can provide feedback on the effectiveness of the exercise and areas for improvement. The feedback unit can also estimate the user's emotions and adjust the content and timing of the feedback based on the estimated emotions. For example, if the user is losing motivation, positive feedback can be provided to maintain motivation. This allows the user to receive specific feedback on their activities.

[0114] The management system can further include a data analysis unit. The data analysis unit has the function of analyzing the user's activity data and performance data in detail and providing the user with useful information. For example, it can analyze the user's exercise patterns and dietary habits and suggest ways to improve their health. The data analysis unit can also predict future health risks based on the user's past data. This allows the user to understand their health condition in detail and take appropriate measures.

[0115] The management system may further include a goal setting unit. The goal setting unit has a function that allows the user to set their own health goals and lifestyle goals. For example, the user can set exercise goals and dietary goals and manage their progress. The goal setting unit can also suggest realistic and achievable goals based on the user's past data. This allows the user to act with specific goals in mind.

[0116] The management system may further include a notification unit. The notification unit has the function of notifying the user of important information and reminders. For example, the notification unit may notify the user of the progress of goals set by the user or important health information. The notification unit may also send notifications at optimal times based on the user's past behavioral patterns. This allows the user to receive important information in a timely manner.

[0117] The management system can further include a calendar section. The calendar section has the function of managing the user's activities and schedules in a calendar format. For example, the user can enter exercise and meal plans into the calendar and visually check their progress. The calendar section can also suggest an optimal schedule based on the user's past data. This allows the user to act in a planned manner.

[0118] The management system can further include a reminder unit. The reminder unit has a function of reminding the user so that they do not forget the goals and tasks they have set. For example, if the user sets a time for exercise, the reminder unit can send a notification encouraging them to exercise at the set time. The reminder unit can also send reminders at the optimal timing based on the user's past behavioral patterns. This allows the user to take planned actions toward achieving their goals.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The generation unit generates a question. For example, the generation unit generates a question with two choices, "Yes" or "No," to make it easier for the user to answer. The generation unit may also have a function that allows the content of the medical interview questions to be customized. For example, the generation unit may adjust the difficulty and level of detail of the questions depending on the user's health condition and goals. Furthermore, the generation unit may estimate the user's emotions and adjust the content and timing of the questions based on the estimated user's emotions. Step 2: The reception unit receives the user's answer based on the question generated by the generation unit. The reception unit receives, for example, a voice input. The reception unit can also estimate the user's emotions and adjust the answer reception interface based on the estimated user emotions. Furthermore, the reception unit can analyze the user's past answer patterns and select the optimal reception method. Step 3: The aggregation unit aggregates the responses received by the reception unit. The aggregation unit, for example, optimizes the aggregation algorithm by referring to the user's past response data. The aggregation unit can also adjust the level of detail of the aggregation based on the user's health goals and progress. Furthermore, the aggregation unit can estimate the user's emotions and adjust the aggregation method based on the estimated user's emotions. Step 4: The transmission unit transmits the results tallied by the aggregation unit. For example, the transmission unit transmits the results by day and month as a voice message. The transmission unit can also transmit the results by day and month by email. Furthermore, the transmission unit can estimate the user's emotions and adjust the timing and method of transmitting the results based on the estimated user's emotions.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 generator for generating questions; a receiving unit that receives a user's answer based on the question generated by the generating unit; a counting unit that counts the answers received by the receiving unit; a transmission unit that transmits the results tallied by the tallying unit; Equipped with A system characterized by:

2. The generation unit Generate yes or no questions that are easy for users to answer 2. The system of claim 1.

3. The reception unit Accepts voice input 2. The system of claim 1.

4. The transmission unit Send daily and monthly results via voice message 2. The system of claim 1.

5. The transmission unit Send daily and monthly results by email 2. The system of claim 1.

6. The generation unit Equipped with the ability to customize the content of medical interview questions 2. The system of claim 1.

7. The aggregation unit Equipped with a function that allows users to select the method of tallying results 2. The system of claim 1.

8. The generation unit Estimate the user's emotions and adjust the content and timing of questions based on the estimated user emotions.

2. The system of claim 1.

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