system

Abstract

The system according to the embodiment aims to allow elderly people to create exercise plans using voice input and to link with other health management apps. [Solution] A system according to an embodiment includes a reception unit, a collection unit, a planning unit, a linking unit, and a providing unit. The reception unit receives voice input. The collection unit collects weather information. The planning unit creates an exercise plan based on the voice input received by the reception unit and the information collected by the collection unit. The linking unit links exercise results with other health management apps based on the exercise plan created by the planning unit. The providing unit provides advice based on the exercise results linked by the linking unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult for elderly people to create exercise plans using voice input and to link them with other health management apps.

[0005] The system according to the embodiment aims to allow elderly people to create exercise plans using voice input and to link with other health management apps. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a collection unit, a planning unit, a linking unit, and a providing unit. The reception unit receives voice input. The collection unit collects weather information. The planning unit creates an exercise plan based on the voice input received by the reception unit and the information collected by the collection unit. The linking unit links the exercise results with other health management apps based on the exercise plan created by the planning unit. The providing unit provides advice based on the exercise results linked by the linking unit. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to create exercise plans using voice input and to link with other health management apps. [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) An exercise advice system according to an embodiment of the present invention provides daily exercise advice for elderly people through a voice-based interactive system. This exercise advice system allows users to set goals via voice and develop an exercise plan based on those goals. Exercise results are automatically linked to other health management apps, providing appropriate advice and motivational information to help users achieve their goals. This information is presented as text or images based on progress and circumstances, and can be read aloud. For example, a user may vocally set a goal, such as "I want to walk 30 minutes every day." The system then collects information such as weather and climate to develop an optimal exercise plan. For example, the system suggests indoor exercises on rainy days and outdoor walking on sunny days. Exercise results are automatically linked to other health management apps. For example, exercise records are automatically sent to the health management app, allowing users to easily check their exercise history. To help users achieve their goals, the system provides appropriate advice and motivational information. For example, a message such as "You did a great job today! Keep it up tomorrow!" is displayed. This information is presented as text or images based on progress and circumstances, and can be read aloud. For example, even visually impaired users can receive advice through audio. This system allows elderly people to continue exercising at their own pace without straining themselves, which helps them maintain their health.This exercise advice system allows elderly people to continue exercising at their own pace without straining themselves, which helps them maintain their health.

[0029] An exercise advice system according to an embodiment includes a reception unit, a collection unit, a planning unit, a linking unit, and a providing unit. The reception unit receives voice input. For example, the reception unit is used when a user sets goals by voice. The voice input is performed using a microphone or the voice input function of a smartphone. The collection unit collects meteorological information. For example, the collection unit collects climate and weather information in real time from the Internet. The collected information includes temperature, humidity, wind speed, and the like. The planning unit creates an exercise plan based on the voice input received by the reception unit and the information collected by the collection unit. For example, the planning unit uses AI to create an optimal exercise plan for each individual user. The AI ​​uses technologies such as machine learning and deep learning. The linking unit links exercise results based on the exercise plan created by the planning unit with other health management apps. For example, the linking unit automatically sends exercise records to the health management apps. The providing unit provides advice based on the exercise results linked by the linking unit. For example, the providing unit presents advice in the form of text or images according to progress and status, and also supports voice reading. This allows the exercise advice system to support elderly people's exercise by creating exercise plans based on voice input and providing advice in collaboration with other apps.

[0030] The reception unit may include technology for improving the accuracy of voice recognition. Examples of technology for improving the accuracy of voice recognition include noise canceling and voice filtering. Noise canceling is a technology for removing ambient noise and improving the accuracy of voice input. For example, using a noise-canceling microphone can effectively remove ambient noise. Voice filtering is a technology for enhancing voice in a specific frequency band and improving the accuracy of voice recognition. For example, using voice filtering software can improve the quality of voice input. This improves the accuracy of voice recognition and more accurately recognizes the user's voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input voice input data to AI and have the AI ​​improve the accuracy of voice recognition.

[0031] The planning unit can use AI to create an exercise plan suitable for each individual user. AI uses technologies such as machine learning and deep learning. For example, machine learning can be used to create an optimal exercise plan based on past exercise data and the user's health condition. Deep learning can be used to analyze complex patterns and provide an optimal exercise plan for each individual user. For example, AI can analyze the user's past exercise history to create an effective exercise plan. AI can also customize the exercise plan based on the user's health condition and activity level. For example, if the user is tired, an easy exercise plan can be created, and if the user is energetic, a hard exercise plan can be created. In this way, by using AI, an optimal exercise plan can be created for the user. Some or all of the above-mentioned processing in the planning unit may be performed using AI, for example, or may be performed without using AI. For example, the planning unit can have AI create the exercise plan.

[0032] The collection unit can collect weather information instantly from the Internet. The collection unit collects climate and weather information from the Internet in real time. For example, the collection unit can obtain the latest weather information using a weather API. The collected information includes temperature, humidity, wind speed, precipitation, etc. For example, the collection unit can obtain the current temperature and humidity using the weather API and reflect the information in the exercise plan. The collection unit can also periodically update the weather information to provide the latest information. For example, the collection unit updates the weather information every hour and reflects the information in the exercise plan. In this way, climate and weather information is collected in real time, allowing an exercise plan based on the latest information to be created. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to collect weather information.

[0033] The linking unit can automatically link exercise results with other health management apps. The linking unit automatically sends exercise records to the health management apps. For example, the linking unit can send exercise results to other apps using an API. The exercise results include calories burned, exercise time, distance, etc. For example, the linking unit can automatically send exercise results to the health management app, allowing the user to easily check their exercise history. The linking unit can also periodically update the exercise results to provide the latest information. For example, the linking unit can send exercise results immediately after exercise and provide feedback to the user. This automatically links the exercise results with other health management apps, allowing the user to easily check their exercise history. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can have AI execute the linking of exercise results.

[0034] The providing unit presents advice in the form of text or images according to the progress status and can also support voice reading. The providing unit presents advice in the form of text or images according to the progress and situation. For example, the providing unit provides advice based on the user's exercise achievement level and goal progress rate. The advice may include suggestions for improving exercise or health management. For example, a message such as "You did a great job today! Keep it up tomorrow" is displayed. The providing unit also supports voice reading, so that even visually impaired users can receive advice. For example, the providing unit provides advice by voice using text-to-speech software. As a result, advice according to the progress and situation is presented in the form of text or images and can also be read aloud, allowing the user to receive appropriate advice. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without AI. For example, the providing unit may have AI perform the provision of advice.

[0035] The reception unit can analyze the user's past voice input history and select the optimal voice recognition algorithm. The reception unit analyzes the user's past voice input history and selects the optimal voice recognition algorithm. For example, the reception unit selects an algorithm that prioritizes recognition of voice commands that the user has frequently used in the past. The reception unit can also analyze the user's voice input patterns and select the optimal voice recognition algorithm. Furthermore, the reception unit can select the optimal voice recognition algorithm taking into account the user's pronunciation and accent characteristics. In this way, by analyzing the past voice input history, the optimal voice recognition algorithm is selected and the accuracy of voice recognition is improved. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice input history data to AI and have the AI ​​select the optimal voice recognition algorithm.

[0036] The reception unit can filter voice input based on the user's current health condition and activity level. The reception unit can filter voice input based on the user's current health condition and activity level. For example, if the user is tired, only simple voice commands can be accepted. On the other hand, if the user is energetic, detailed voice commands can be accepted. Furthermore, appropriate voice commands can be filtered according to the user's activity level. In this way, the voice input is filtered according to the user's health condition and activity level, and appropriate voice commands can be accepted. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition and activity level data into AI and have the AI ​​perform the filtering of voice commands.

[0037] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving voice input. The reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information when receiving voice input. For example, when the user is in a specific location, the reception unit prioritizes receiving voice inputs related to that location. Also, when the user is traveling, the reception unit can prioritize receiving voice inputs related to the user's destination. Furthermore, when the user is at home, the reception unit can prioritize receiving voice inputs related to the user's home. In this way, highly relevant voice inputs are prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to AI and have the AI ​​prioritize the voice inputs.

[0038] The reception unit can analyze the user's social media activity and receive related input when a voice input is received. The reception unit analyzes the user's social media activity and receives related input when a voice input is received. For example, the reception unit can preferentially receive related voice input based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related voice input. Furthermore, the reception unit can also receive appropriate voice input by referring to the user's social media activity history. In this way, related voice input is received based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​receive the voice input.

[0039] The collection unit can analyze past climate and weather data and select the optimal collection method. The collection unit analyzes past climate and weather data and selects the optimal collection method. For example, the optimal collection timing is determined based on past climate data. The collection unit can also analyze past weather data and select the optimal collection method. Furthermore, an efficient collection method can be selected by referring to past climate and weather patterns. In this way, by analyzing past data, the optimal collection method is selected, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past climate and weather data into AI and have the AI ​​select the optimal collection method.

[0040] The collection unit can filter the climate and weather information based on the user's current activity plan when collecting the climate and weather information. The collection unit can filter the climate and weather information based on the user's current activity plan when collecting the climate and weather information. For example, if the user is planning an outdoor activity, outdoor climate and weather information can be collected preferentially. Also, if the user is planning an indoor activity, indoor climate and weather information can be collected preferentially. Furthermore, relevant climate and weather information can be filtered according to the user's activity plan. As a result, relevant climate and weather information is filtered according to the user's activity plan, and appropriate information is collected. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's activity plan data into AI and have the AI ​​filter the climate and weather information.

[0041] When collecting climate and weather information, the collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. When collecting climate and weather information, the collection unit prioritizes collecting highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting climate and weather information related to that location. Also, when the user is traveling, the collection unit can prioritize collecting climate and weather information related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting climate and weather information related to the user's home. In this way, highly relevant climate and weather information is collected preferentially based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into AI and cause the AI ​​to collect climate and weather information.

[0042] The collection unit can analyze the user's social media activities and collect related information when collecting climate and weather information. The collection unit can analyze the user's social media activities and collect related information when collecting climate and weather information. For example, the collection unit can prioritize collecting related climate and weather information based on information shared by the user on social media. The collection unit can also analyze the user's social media posts to collect related climate and weather information. Furthermore, the collection unit can refer to the user's social media activity history to collect appropriate climate and weather information. In this way, related climate and weather information is collected based on the user's social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​collect climate and weather information.

[0043] When formulating an exercise plan, the planning unit can analyze the user's past exercise history to create an optimal plan. When formulating an exercise plan, the planning unit analyzes the user's past exercise history to create an optimal plan. For example, the planning unit may create an optimal exercise plan based on data of exercises the user has performed in the past. The planning unit may also analyze the user's exercise history to create an effective exercise plan. Furthermore, the planning unit may also refer to the user's past exercise patterns to create an efficient exercise plan. In this way, an optimal exercise plan is created by analyzing the past exercise history. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit may input the user's exercise history data into AI and have the AI ​​execute the planning of an optimal exercise plan.

[0044] When formulating an exercise plan, the planning unit can customize the plan based on the user's current health condition and activity level. When formulating an exercise plan, the planning unit customizes the plan based on the user's current health condition and activity level. For example, if the user is tired, a light exercise plan can be formulated. On the other hand, if the user is energetic, a hard exercise plan can be formulated. Furthermore, an appropriate exercise plan can be customized according to the user's health condition. As a result, the exercise plan is customized according to the user's health condition and activity level, allowing appropriate exercise to be performed. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's health condition and activity level data into AI and have the AI ​​customize the exercise plan.

[0045] The planning unit can create an optimal exercise plan by taking into account the user's geographical location information when creating an exercise plan. The planning unit creates an optimal plan by taking into account the user's geographical location information when creating an exercise plan. For example, if the user is in a specific location, the planning unit creates an exercise plan appropriate for that location. Also, if the user is traveling, the planning unit can create an exercise plan appropriate for the user's destination. Furthermore, if the user is at home, the planning unit can create an exercise plan that can be performed at home. In this way, an optimal exercise plan is created based on the user's geographical location information. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's geographical location information data into AI and have the AI ​​create the exercise plan.

[0046] The planning unit can analyze the user's social media activities and create a related plan when creating an exercise plan. The planning unit analyzes the user's social media activities and create a related plan when creating an exercise plan. For example, the planning unit can create a related exercise plan based on information shared by the user on social media. The planning unit can also analyze the user's social media posts and create a related exercise plan. Furthermore, the planning unit can create an appropriate exercise plan by referring to the user's social media activity history. In this way, a related exercise plan is created based on the user's social media activity. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's social media activity data into AI and have the AI ​​create the exercise plan.

[0047] When linking exercise results, the linking unit can analyze the user's past linking history to select the optimal linking method. When linking exercise results, the linking unit analyzes the user's past linking history to select the optimal linking method. For example, the linking unit selects the optimal linking method based on linking methods used by the user in the past. The linking unit can also analyze the user's linking history to select an effective linking method. Furthermore, the linking unit can select an efficient linking method by referring to the user's past linking patterns. In this way, the optimal linking method is selected by analyzing the past linking history, and efficient linking is performed. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's linking history data into AI and have the AI ​​select the optimal linking method.

[0048] The linking unit can filter exercise results based on the user's current health condition and activity level when linking the exercise results. The linking unit can filter exercise results based on the user's current health condition and activity level when linking the exercise results. For example, if the user is tired, only simple exercise results can be linked. Also, if the user is energetic, detailed exercise results can be linked. Furthermore, appropriate exercise results can be filtered according to the user's health condition. In this way, exercise results are filtered according to the user's health condition and activity level, and appropriate results are linked. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's health condition and activity level data into AI and have the AI ​​perform filtering of the exercise results.

[0049] When linking exercise results, the linking unit can prioritize linking highly relevant results taking into account the user's geographical location information. When linking exercise results, the linking unit prioritizes linking highly relevant results taking into account the user's geographical location information. For example, if the user is in a specific location, exercise results related to that location can be prioritized. Also, if the user is traveling, exercise results related to the user's destination can be prioritized. Furthermore, if the user is at home, exercise results related to the user's home can be prioritized. In this way, highly relevant exercise results are prioritized to be linked based on the user's geographical location information. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information data into AI and cause the AI ​​to link the exercise results.

[0050] The linking unit can analyze the user's social media activity and link related results when linking exercise results. The linking unit analyzes the user's social media activity and link related results when linking exercise results. For example, related exercise results are linked preferentially based on information shared by the user on social media. The linking unit can also analyze the content of the user's social media posts and link related exercise results. Furthermore, appropriate exercise results can be linked based on the user's social media activity history. In this way, related exercise results are linked based on the user's social media activity. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's social media activity data into AI and have the AI ​​execute the exercise result linking.

[0051] When providing advice, the providing unit can analyze the user's past exercise history and provide optimal advice. When providing advice, the providing unit can analyze the user's past exercise history and provide optimal advice. For example, optimal advice is provided based on data of exercises the user has performed in the past. The providing unit can also analyze the user's exercise history and provide effective advice. Furthermore, efficient advice can be provided by referring to the user's past exercise patterns. In this way, optimal advice is provided by analyzing the past exercise history. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's exercise history data into AI and have the AI ​​provide optimal advice.

[0052] The providing unit can customize the advice based on the user's current health condition and activity level when providing the advice. The providing unit customizes the advice based on the user's current health condition and activity level when providing the advice. For example, if the user is tired, light advice can be provided. On the other hand, if the user is energetic, hard advice can be provided. Furthermore, appropriate advice can be customized according to the user's health condition. As a result, advice is customized according to the user's health condition and activity level, and appropriate advice is provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's health condition and activity level data into AI and have the AI ​​customize the advice.

[0053] When providing advice, the providing unit can provide optimal advice by taking into account the user's geographical location information. When providing advice, the providing unit provides optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific location, advice appropriate to that location can be provided. Also, if the user is traveling, advice appropriate to the user's destination can be provided. Furthermore, if the user is at home, advice that can be done at home can be provided. In this way, optimal advice is provided based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into AI and cause the AI ​​to provide advice.

[0054] The providing unit can analyze the user's social media activity and provide relevant advice when providing advice. The providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the providing unit can provide relevant advice based on information shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide relevant advice. Furthermore, the providing unit can provide appropriate advice by referring to the user's social media activity history. In this way, relevant advice is provided based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and have the AI ​​provide the advice.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When the user inputs voice, the reception unit can analyze the user's past exercise history and select the optimal voice recognition algorithm. For example, it can select an algorithm that prioritizes recognition of voice commands that the user has frequently used in the past. It can also analyze the user's voice input pattern and select the optimal voice recognition algorithm. Furthermore, it can also select the optimal voice recognition algorithm taking into account the user's pronunciation and accent characteristics. In this way, by analyzing the past voice input history, the optimal voice recognition algorithm is selected, improving the accuracy of voice recognition.

[0057] The collection unit can collect weather information taking into account the user's geographical location information. For example, if the user is in a specific location, weather information related to that location can be collected preferentially. Also, if the user is traveling, weather information related to the user's destination can be collected preferentially. Furthermore, if the user is at home, weather information related to the user's home can be collected preferentially. In this way, weather information that is highly relevant based on the user's geographical location information can be collected preferentially.

[0058] The planning unit can analyze the user's social media activities and create a related exercise plan. For example, the planning unit can create a related exercise plan based on information shared by the user on social media. The planning unit can also analyze the content of the user's social media posts and create a related exercise plan. Furthermore, the planning unit can also create an appropriate exercise plan by referring to the user's social media activity history. In this way, a related exercise plan is created based on the user's social media activities.

[0059] When linking exercise results, the linking unit can analyze the user's past linking history to select the optimal linking method. For example, the optimal linking method is selected based on the linking methods used by the user in the past. The linking unit can also analyze the user's linking history to select an effective linking method. Furthermore, the linking unit can select an efficient linking method by referring to the user's past linking patterns. In this way, the optimal linking method is selected by analyzing the past linking history, and efficient linking is performed.

[0060] When providing advice, the providing unit can analyze the user's past exercise history and provide optimal advice. For example, optimal advice can be provided based on data on exercise the user has performed in the past. The providing unit can also analyze the user's exercise history and provide effective advice. Furthermore, efficient advice can be provided by referring to the user's past exercise patterns. In this way, optimal advice can be provided by analyzing the user's past exercise history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit receives voice input. For example, this is used when a user sets a goal by voice. Voice input can be performed using a microphone or the voice input function of a smartphone. Step 2: The collection unit collects meteorological information. For example, it collects climate and weather information in real time from the Internet. The collected information includes temperature, humidity, wind speed, etc. Step 3: The planning unit creates an exercise plan based on the voice input received by the reception unit and the information collected by the collection unit. For example, an optimal exercise plan for each individual user is created using AI. The AI ​​uses techniques such as machine learning and deep learning. Step 4: The linking unit links the exercise results with other health management apps based on the exercise plan created by the planning unit. For example, the linking unit automatically sends the exercise records to the health management apps. Step 5: The provider provides advice based on the exercise results linked by the linking unit. For example, advice can be presented in the form of text or images depending on the progress and situation, and can also be read aloud.

[0063] (Example 2) An exercise advice system according to an embodiment of the present invention provides daily exercise advice for elderly people through a voice-based interactive system. This exercise advice system allows users to set goals via voice and develop an exercise plan based on those goals. Exercise results are automatically linked to other health management apps, providing appropriate advice and motivational information to help users achieve their goals. This information is presented as text or images based on progress and circumstances, and can be read aloud. For example, a user may vocally set a goal, such as "I want to walk 30 minutes every day." The system then collects information such as weather and climate to develop an optimal exercise plan. For example, the system suggests indoor exercises on rainy days and outdoor walking on sunny days. Exercise results are automatically linked to other health management apps. For example, exercise records are automatically sent to the health management app, allowing users to easily check their exercise history. To help users achieve their goals, the system provides appropriate advice and motivational information. For example, a message such as "You did a great job today! Keep it up tomorrow!" is displayed. This information is presented as text or images based on progress and circumstances, and can be read aloud. For example, even visually impaired users can receive advice through audio. This system allows elderly people to continue exercising at their own pace without straining themselves, which helps them maintain their health.This exercise advice system allows elderly people to continue exercising at their own pace without straining themselves, which helps them maintain their health.

[0064] An exercise advice system according to an embodiment includes a reception unit, a collection unit, a planning unit, a linking unit, and a providing unit. The reception unit receives voice input. For example, the reception unit is used when a user sets goals by voice. The voice input is performed using a microphone or the voice input function of a smartphone. The collection unit collects meteorological information. For example, the collection unit collects climate and weather information in real time from the Internet. The collected information includes temperature, humidity, wind speed, and the like. The planning unit creates an exercise plan based on the voice input received by the reception unit and the information collected by the collection unit. For example, the planning unit uses AI to create an optimal exercise plan for each individual user. The AI ​​uses technologies such as machine learning and deep learning. The linking unit links exercise results based on the exercise plan created by the planning unit with other health management apps. For example, the linking unit automatically sends exercise records to the health management apps. The providing unit provides advice based on the exercise results linked by the linking unit. For example, the providing unit presents advice in the form of text or images according to progress and status, and also supports voice reading. This allows the exercise advice system to support elderly people's exercise by creating exercise plans based on voice input and providing advice in collaboration with other apps.

[0065] The reception unit may include technology for improving the accuracy of voice recognition. Examples of technology for improving the accuracy of voice recognition include noise canceling and voice filtering. Noise canceling is a technology for removing ambient noise and improving the accuracy of voice input. For example, using a noise-canceling microphone can effectively remove ambient noise. Voice filtering is a technology for enhancing voice in a specific frequency band and improving the accuracy of voice recognition. For example, using voice filtering software can improve the quality of voice input. This improves the accuracy of voice recognition and more accurately recognizes the user's voice input. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input voice input data to AI and have the AI ​​improve the accuracy of voice recognition.

[0066] The planning unit can use AI to create an exercise plan suitable for each individual user. AI uses technologies such as machine learning and deep learning. For example, machine learning can be used to create an optimal exercise plan based on past exercise data and the user's health condition. Deep learning can be used to analyze complex patterns and provide an optimal exercise plan for each individual user. For example, AI can analyze the user's past exercise history to create an effective exercise plan. AI can also customize the exercise plan based on the user's health condition and activity level. For example, if the user is tired, an easy exercise plan can be created, and if the user is energetic, a hard exercise plan can be created. In this way, by using AI, an optimal exercise plan can be created for the user. Some or all of the above-mentioned processing in the planning unit may be performed using AI, for example, or may be performed without using AI. For example, the planning unit can have AI create the exercise plan.

[0067] The collection unit can collect weather information instantly from the Internet. The collection unit collects climate and weather information from the Internet in real time. For example, the collection unit can obtain the latest weather information using a weather API. The collected information includes temperature, humidity, wind speed, precipitation, etc. For example, the collection unit can obtain the current temperature and humidity using the weather API and reflect the information in the exercise plan. The collection unit can also periodically update the weather information to provide the latest information. For example, the collection unit updates the weather information every hour and reflects the information in the exercise plan. In this way, climate and weather information is collected in real time, allowing an exercise plan based on the latest information to be created. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can cause AI to collect weather information.

[0068] The linking unit can automatically link exercise results with other health management apps. The linking unit automatically sends exercise records to the health management apps. For example, the linking unit can send exercise results to other apps using an API. The exercise results include calories burned, exercise time, distance, etc. For example, the linking unit can automatically send exercise results to the health management app, allowing the user to easily check their exercise history. The linking unit can also periodically update the exercise results to provide the latest information. For example, the linking unit can send exercise results immediately after exercise and provide feedback to the user. This automatically links the exercise results with other health management apps, allowing the user to easily check their exercise history. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can have AI execute the linking of exercise results.

[0069] The providing unit presents advice in the form of text or images according to the progress status and can also support voice reading. The providing unit presents advice in the form of text or images according to the progress and situation. For example, the providing unit provides advice based on the user's exercise achievement level and goal progress rate. The advice may include suggestions for improving exercise or health management. For example, a message such as "You did a great job today! Keep it up tomorrow" is displayed. The providing unit also supports voice reading, so that even visually impaired users can receive advice. For example, the providing unit provides advice by voice using text-to-speech software. As a result, advice according to the progress and situation is presented in the form of text or images and can also be read aloud, allowing the user to receive appropriate advice. Some or all of the above-described processing by the providing unit may be performed, for example, using AI, or may be performed without AI. For example, the providing unit may have AI perform the provision of advice.

[0070] The reception unit can estimate the user's emotion and adjust the timing of voice input reception based on the estimated user emotion. The reception unit estimates the user's emotion and adjusts the timing of voice input reception based on the estimated user emotion. For example, if the user is stressed, the reception timing of voice input is delayed to allow the user to relax. Also, if the user is relaxed, the reception timing of voice input is accelerated to allow smooth input. Furthermore, if the user is in a hurry, the reception timing of voice input is immediate to allow quick input. This allows voice input to be performed at a more appropriate timing by adjusting the reception timing of voice input according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0071] The reception unit can analyze the user's past voice input history and select the optimal voice recognition algorithm. The reception unit analyzes the user's past voice input history and selects the optimal voice recognition algorithm. For example, the reception unit selects an algorithm that prioritizes recognition of voice commands that the user has frequently used in the past. The reception unit can also analyze the user's voice input patterns and select the optimal voice recognition algorithm. Furthermore, the reception unit can select the optimal voice recognition algorithm taking into account the user's pronunciation and accent characteristics. In this way, by analyzing the past voice input history, the optimal voice recognition algorithm is selected and the accuracy of voice recognition is improved. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input voice input history data to AI and have the AI ​​select the optimal voice recognition algorithm.

[0072] The reception unit can filter voice input based on the user's current health condition and activity level. The reception unit can filter voice input based on the user's current health condition and activity level. For example, if the user is tired, only simple voice commands can be accepted. On the other hand, if the user is energetic, detailed voice commands can be accepted. Furthermore, appropriate voice commands can be filtered according to the user's activity level. In this way, the voice input is filtered according to the user's health condition and activity level, and appropriate voice commands can be accepted. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's health condition and activity level data into AI and have the AI ​​perform the filtering of voice commands.

[0073] The reception unit can estimate the user's emotion and determine the priority of voice inputs based on the estimated user emotion. The reception unit estimates the user's emotion and determines the priority of voice inputs based on the estimated user emotion. For example, if the user is stressed, important voice inputs can be received with priority. Also, if the user is relaxed, all voice inputs can be received equally. Furthermore, if the user is in a hurry, urgent voice inputs can be received with the highest priority. In this way, the priority of voice inputs is determined according to the user's emotion, and important voice inputs can be received with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to perform emotion estimation.

[0074] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving voice input. The reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information when receiving voice input. For example, when the user is in a specific location, the reception unit prioritizes receiving voice inputs related to that location. Also, when the user is traveling, the reception unit can prioritize receiving voice inputs related to the user's destination. Furthermore, when the user is at home, the reception unit can prioritize receiving voice inputs related to the user's home. In this way, highly relevant voice inputs are prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to AI and have the AI ​​prioritize the voice inputs.

[0075] The reception unit can analyze the user's social media activity and receive related input when a voice input is received. The reception unit analyzes the user's social media activity and receives related input when a voice input is received. For example, the reception unit can preferentially receive related voice input based on information shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and receive related voice input. Furthermore, the reception unit can also receive appropriate voice input by referring to the user's social media activity history. In this way, related voice input is received based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data into AI and have the AI ​​receive the voice input.

[0076] The collection unit can estimate the user's emotions and adjust the timing of collecting climate and weather information based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting climate and weather information based on the estimated user emotions. For example, if the user is stressed, the collection timing of climate and weather information can be delayed. Also, if the user is relaxed, the collection timing of climate and weather information can be accelerated. Furthermore, if the user is in a hurry, the collection timing of climate and weather information can be immediately collected. This adjusts the collection timing of climate and weather information according to the user's emotions, and information is collected at an appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0077] The collection unit can analyze past climate and weather data and select the optimal collection method. The collection unit analyzes past climate and weather data and selects the optimal collection method. For example, the optimal collection timing is determined based on past climate data. The collection unit can also analyze past weather data and select the optimal collection method. Furthermore, an efficient collection method can be selected by referring to past climate and weather patterns. In this way, by analyzing past data, the optimal collection method is selected, enabling efficient information collection. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past climate and weather data into AI and have the AI ​​select the optimal collection method.

[0078] The collection unit can filter the climate and weather information based on the user's current activity plan when collecting the climate and weather information. The collection unit can filter the climate and weather information based on the user's current activity plan when collecting the climate and weather information. For example, if the user is planning an outdoor activity, outdoor climate and weather information can be collected preferentially. Also, if the user is planning an indoor activity, indoor climate and weather information can be collected preferentially. Furthermore, relevant climate and weather information can be filtered according to the user's activity plan. As a result, relevant climate and weather information is filtered according to the user's activity plan, and appropriate information is collected. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's activity plan data into AI and have the AI ​​filter the climate and weather information.

[0079] The collection unit can estimate the user's emotions and determine the priority of the weather and climate information to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of the weather and climate information to be collected based on the estimated user emotions. For example, if the user is stressed, important weather and climate information can be collected with priority. Alternatively, if the user is relaxed, all weather and climate information can be collected equally. Furthermore, if the user is in a hurry, urgent weather and climate information can be collected with top priority. In this way, the priority of the weather and climate information is determined according to the user's emotions, and important information is collected with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0080] When collecting climate and weather information, the collection unit can prioritize collecting highly relevant information taking into account the user's geographical location information. When collecting climate and weather information, the collection unit prioritizes collecting highly relevant information taking into account the user's geographical location information. For example, when the user is in a specific location, the collection unit prioritizes collecting climate and weather information related to that location. Also, when the user is traveling, the collection unit can prioritize collecting climate and weather information related to the user's destination. Furthermore, when the user is at home, the collection unit can prioritize collecting climate and weather information related to the user's home. In this way, highly relevant climate and weather information is collected preferentially based on the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into AI and cause the AI ​​to collect climate and weather information.

[0081] The collection unit can analyze the user's social media activities and collect related information when collecting climate and weather information. The collection unit can analyze the user's social media activities and collect related information when collecting climate and weather information. For example, the collection unit can prioritize collecting related climate and weather information based on information shared by the user on social media. The collection unit can also analyze the user's social media posts to collect related climate and weather information. Furthermore, the collection unit can refer to the user's social media activity history to collect appropriate climate and weather information. In this way, related climate and weather information is collected based on the user's social media activity. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media activity data into AI and have the AI ​​collect climate and weather information.

[0082] The planning unit can estimate the user's emotions and adjust the exercise plan creation method based on the estimated user emotions. The planning unit can estimate the user's emotions and adjust the exercise plan creation method based on the estimated user emotions. For example, if the user is feeling stressed, a relaxing exercise plan can be created. Also, if the user is relaxed, a challenging exercise plan can be created. Furthermore, if the user is in a hurry, an effective exercise plan can be created in a short time. This adjusts the exercise plan creation method according to the user's emotions, resulting in a more appropriate exercise plan. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the planning unit can be performed using an AI, for example, or without an AI. For example, the planning unit can input the user's emotion data into the generation AI and have the generation AI execute emotion estimation.

[0083] When formulating an exercise plan, the planning unit can analyze the user's past exercise history to create an optimal plan. When formulating an exercise plan, the planning unit analyzes the user's past exercise history to create an optimal plan. For example, the planning unit may create an optimal exercise plan based on data of exercises the user has performed in the past. The planning unit may also analyze the user's exercise history to create an effective exercise plan. Furthermore, the planning unit may also refer to the user's past exercise patterns to create an efficient exercise plan. In this way, an optimal exercise plan is created by analyzing the past exercise history. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit may input the user's exercise history data into AI and have the AI ​​execute the planning of an optimal exercise plan.

[0084] When formulating an exercise plan, the planning unit can customize the plan based on the user's current health condition and activity level. When formulating an exercise plan, the planning unit customizes the plan based on the user's current health condition and activity level. For example, if the user is tired, a light exercise plan can be formulated. On the other hand, if the user is energetic, a hard exercise plan can be formulated. Furthermore, an appropriate exercise plan can be customized according to the user's health condition. As a result, the exercise plan is customized according to the user's health condition and activity level, allowing appropriate exercise to be performed. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's health condition and activity level data into AI and have the AI ​​customize the exercise plan.

[0085] The planning unit can estimate the user's emotions and prioritize exercise plans based on the estimated user emotions. The planning unit estimates the user's emotions and prioritizes exercise plans based on the estimated user emotions. For example, if the user is stressed, a relaxing exercise plan can be prioritized. Also, if the user is relaxed, a challenging exercise plan can be prioritized. Furthermore, if the user is in a hurry, a short, effective exercise plan can be prioritized. In this way, the priority of exercise plans is determined according to the user's emotions, and important exercise plans are prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the planning unit can be performed using, for example, an AI, or without an AI. For example, the planning unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0086] The planning unit can create an optimal exercise plan by taking into account the user's geographical location information when creating an exercise plan. The planning unit creates an optimal plan by taking into account the user's geographical location information when creating an exercise plan. For example, if the user is in a specific location, the planning unit creates an exercise plan appropriate for that location. Also, if the user is traveling, the planning unit can create an exercise plan appropriate for the user's destination. Furthermore, if the user is at home, the planning unit can create an exercise plan that can be performed at home. In this way, an optimal exercise plan is created based on the user's geographical location information. Some or all of the above-described processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's geographical location information data into AI and have the AI ​​create the exercise plan.

[0087] The planning unit can analyze the user's social media activities and create a related plan when creating an exercise plan. The planning unit analyzes the user's social media activities and create a related plan when creating an exercise plan. For example, the planning unit can create a related exercise plan based on information shared by the user on social media. The planning unit can also analyze the user's social media posts and create a related exercise plan. Furthermore, the planning unit can create an appropriate exercise plan by referring to the user's social media activity history. In this way, a related exercise plan is created based on the user's social media activity. Some or all of the above-mentioned processing in the planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the planning unit can input the user's social media activity data into AI and have the AI ​​create the exercise plan.

[0088] The linking unit can estimate the user's emotions and adjust the timing of linking the exercise results based on the estimated user emotions. The linking unit can estimate the user's emotions and adjust the timing of linking the exercise results based on the estimated user emotions. For example, if the user is stressed, the linking unit can delay the timing of linking the exercise results. Also, if the user is relaxed, the linking unit can advance the timing of linking the exercise results. Furthermore, if the user is in a hurry, the linking unit can link the exercise results immediately. This allows the linking timing of the exercise results to be adjusted according to the user's emotions, and linking is performed at an appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the linking unit can be performed using an AI, for example, or without an AI. For example, the linking unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0089] When linking exercise results, the linking unit can analyze the user's past linking history to select the optimal linking method. When linking exercise results, the linking unit analyzes the user's past linking history to select the optimal linking method. For example, the linking unit selects the optimal linking method based on linking methods used by the user in the past. The linking unit can also analyze the user's linking history to select an effective linking method. Furthermore, the linking unit can select an efficient linking method by referring to the user's past linking patterns. In this way, the optimal linking method is selected by analyzing the past linking history, and efficient linking is performed. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's linking history data into AI and have the AI ​​select the optimal linking method.

[0090] The linking unit can filter exercise results based on the user's current health condition and activity level when linking the exercise results. The linking unit can filter exercise results based on the user's current health condition and activity level when linking the exercise results. For example, if the user is tired, only simple exercise results can be linked. Also, if the user is energetic, detailed exercise results can be linked. Furthermore, appropriate exercise results can be filtered according to the user's health condition. In this way, exercise results are filtered according to the user's health condition and activity level, and appropriate results are linked. Some or all of the above-described processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's health condition and activity level data into AI and have the AI ​​perform filtering of the exercise results.

[0091] The linking unit can estimate the user's emotions and determine the priority of exercise results to be linked based on the estimated user emotions. The linking unit can estimate the user's emotions and determine the priority of exercise results to be linked based on the estimated user emotions. For example, if the user is feeling stressed, important exercise results can be linked with priority. Also, if the user is relaxed, all exercise results can be linked equally. Furthermore, if the user is in a hurry, urgent exercise results can be linked with top priority. In this way, the priority of exercise results is determined according to the user's emotions, and important results are linked with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the linking unit can be performed using, for example, an AI. For example, the linking unit can input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0092] When linking exercise results, the linking unit can prioritize linking highly relevant results taking into account the user's geographical location information. When linking exercise results, the linking unit prioritizes linking highly relevant results taking into account the user's geographical location information. For example, if the user is in a specific location, exercise results related to that location can be prioritized. Also, if the user is traveling, exercise results related to the user's destination can be prioritized. Furthermore, if the user is at home, exercise results related to the user's home can be prioritized. In this way, highly relevant exercise results are prioritized to be linked based on the user's geographical location information. Some or all of the above-described processing in the linking unit may be performed using AI, for example, or may be performed without using AI. For example, the linking unit can input the user's geographical location information data into AI and cause the AI ​​to link the exercise results.

[0093] The linking unit can analyze the user's social media activity and link related results when linking exercise results. The linking unit analyzes the user's social media activity and link related results when linking exercise results. For example, related exercise results are linked preferentially based on information shared by the user on social media. The linking unit can also analyze the content of the user's social media posts and link related exercise results. Furthermore, appropriate exercise results can be linked based on the user's social media activity history. In this way, related exercise results are linked based on the user's social media activity. Some or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's social media activity data into AI and have the AI ​​execute the exercise result linking.

[0094] The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user emotions. The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide relaxing advice. If the user is relaxed, the providing unit can provide challenging advice. Furthermore, if the user is in a hurry, the providing unit can provide effective advice in a short time. This adjusts the method of providing advice according to the user's emotions, and more appropriate advice is provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a 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. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0095] When providing advice, the providing unit can analyze the user's past exercise history and provide optimal advice. When providing advice, the providing unit can analyze the user's past exercise history and provide optimal advice. For example, optimal advice is provided based on data of exercises the user has performed in the past. The providing unit can also analyze the user's exercise history and provide effective advice. Furthermore, efficient advice can be provided by referring to the user's past exercise patterns. In this way, optimal advice is provided by analyzing the past exercise history. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's exercise history data into AI and have the AI ​​provide optimal advice.

[0096] The providing unit can customize the advice based on the user's current health condition and activity level when providing the advice. The providing unit customizes the advice based on the user's current health condition and activity level when providing the advice. For example, if the user is tired, light advice can be provided. On the other hand, if the user is energetic, hard advice can be provided. Furthermore, appropriate advice can be customized according to the user's health condition. As a result, advice is customized according to the user's health condition and activity level, and appropriate advice is provided. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's health condition and activity level data into AI and have the AI ​​customize the advice.

[0097] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize relaxing advice. Also, if the user is relaxed, it can prioritize challenging advice. Furthermore, if the user is in a hurry, it can prioritize effective advice that can be completed in a short time. In this way, the priority of advice is determined according to the user's emotions, and important advice is provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a 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. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0098] When providing advice, the providing unit can provide optimal advice by taking into account the user's geographical location information. When providing advice, the providing unit provides optimal advice by taking into account the user's geographical location information. For example, if the user is in a specific location, advice appropriate to that location can be provided. Also, if the user is traveling, advice appropriate to the user's destination can be provided. Furthermore, if the user is at home, advice that can be done at home can be provided. In this way, optimal advice is provided based on the user's geographical location information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into AI and cause the AI ​​to provide advice.

[0099] The providing unit can analyze the user's social media activity and provide relevant advice when providing advice. The providing unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the providing unit can provide relevant advice based on information shared by the user on social media. The providing unit can also analyze the content of the user's social media posts and provide relevant advice. Furthermore, the providing unit can provide appropriate advice by referring to the user's social media activity history. In this way, relevant advice is provided based on the user's social media activity. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's social media activity data into AI and have the AI ​​provide the advice. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, collection unit, planning unit, linking unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input using the microphone 38B or touch panel 38A of the smart device 14. The collection unit collects weather information from the Internet using the specific processing unit 290 of the data processing device 12. The planning unit creates an exercise plan using AI using the specific processing unit 290 of the data processing device 12. The linking unit links the exercise results with other health management apps using the specific processing unit 290 of the data processing device 12. The providing unit presents advice in the form of text or images using the control unit 46A of the smart device 14, and also supports voice reading. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, collection unit, planning unit, linking unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214. The collection unit collects weather information from the Internet using the specific processing unit 290 of the data processing device 12. The planning unit creates an exercise plan using AI using the specific processing unit 290 of the data processing device 12. The linking unit links the exercise results with other health management apps using the specific processing unit 290 of the data processing device 12. The providing unit presents advice in the form of text or images using the control unit 46A of the smart glasses 214, and also supports voice reading. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, collection unit, planning unit, linking unit, and providing unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset-type terminal 314. The collection unit collects weather information from the Internet using the specific processing unit 290 of the data processing device 12. The planning unit creates an exercise plan using AI using the specific processing unit 290 of the data processing device 12. The linking unit links the exercise results with other health management apps using the specific processing unit 290 of the data processing device 12. The providing unit presents advice in the form of text or images using the control unit 46A of the headset-type terminal 314, and also supports voice reading. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, collection unit, planning unit, linking unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414. The collection unit collects weather information from the Internet using the specific processing unit 290 of the data processing device 12. The planning unit creates an exercise plan using AI using the specific processing unit 290 of the data processing device 12. The linking unit links the exercise results with other health management apps using the specific processing unit 290 of the data processing device 12. The providing unit presents advice in the form of text or images using the control unit 46A of the robot 414, and also supports voice reading.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When the user inputs voice, the reception unit can analyze the user's past exercise history and select the optimal voice recognition algorithm. For example, it can select an algorithm that prioritizes recognition of voice commands that the user has frequently used in the past. It can also analyze the user's voice input pattern and select the optimal voice recognition algorithm. Furthermore, it can also select the optimal voice recognition algorithm taking into account the user's pronunciation and accent characteristics. In this way, by analyzing the past voice input history, the optimal voice recognition algorithm is selected, improving the accuracy of voice recognition.

[0102] The collection unit can collect weather information taking into account the user's geographical location information. For example, if the user is in a specific location, weather information related to that location can be collected preferentially. Also, if the user is traveling, weather information related to the user's destination can be collected preferentially. Furthermore, if the user is at home, weather information related to the user's home can be collected preferentially. In this way, weather information that is highly relevant based on the user's geographical location information can be collected preferentially.

[0103] The planning unit can analyze the user's social media activities and create a related exercise plan. For example, the planning unit can create a related exercise plan based on information shared by the user on social media. The planning unit can also analyze the content of the user's social media posts and create a related exercise plan. Furthermore, the planning unit can also create an appropriate exercise plan by referring to the user's social media activity history. In this way, a related exercise plan is created based on the user's social media activities.

[0104] When linking exercise results, the linking unit can analyze the user's past linking history to select the optimal linking method. For example, the optimal linking method is selected based on the linking methods used by the user in the past. The linking unit can also analyze the user's linking history to select an effective linking method. Furthermore, the linking unit can select an efficient linking method by referring to the user's past linking patterns. In this way, the optimal linking method is selected by analyzing the past linking history, and efficient linking is performed.

[0105] When providing advice, the providing unit can analyze the user's past exercise history and provide optimal advice. For example, optimal advice can be provided based on data on exercise the user has performed in the past. The providing unit can also analyze the user's exercise history and provide effective advice. Furthermore, efficient advice can be provided by referring to the user's past exercise patterns. In this way, optimal advice can be provided by analyzing the user's past exercise history.

[0106] The reception unit can estimate the user's emotions and adjust the timing of voice input reception based on the estimated user emotions. For example, if the user is feeling stressed, the timing of voice input reception can be delayed to allow the user to relax. Alternatively, if the user is relaxed, the timing of voice input reception can be accelerated to allow smooth input. Furthermore, if the user is in a hurry, the timing of voice input reception can be made immediate to allow quick input. In this way, the timing of voice input reception can be adjusted according to the user's emotions, allowing voice input to be performed at a more appropriate timing.

[0107] The collection unit can estimate the user's emotions and adjust the timing of collecting climate and weather information based on the estimated user emotions. For example, if the user is feeling stressed, the timing of collecting climate and weather information can be delayed. Alternatively, if the user is relaxed, the timing of collecting climate and weather information can be advanced. Furthermore, if the user is in a hurry, the timing of collecting climate and weather information can be collected immediately. In this way, the timing of collecting climate and weather information is adjusted according to the user's emotions, and information is collected at an appropriate time.

[0108] The planning unit can estimate the user's emotions and adjust the method for formulating an exercise plan based on the estimated user's emotions. For example, if the user is feeling stressed, a relaxing exercise plan can be formulated. Also, if the user is relaxed, a challenging exercise plan can be formulated. Furthermore, if the user is in a hurry, an effective exercise plan can be formulated in a short amount of time. In this way, the method for formulating an exercise plan can be adjusted according to the user's emotions, and a more appropriate exercise plan can be formulated.

[0109] The linking unit can estimate the user's emotions and adjust the timing of linking the exercise results based on the estimated user's emotions. For example, if the user is feeling stressed, the linking timing of the exercise results can be delayed. Also, if the user is relaxed, the linking timing of the exercise results can be advanced. Furthermore, if the user is in a hurry, the exercise results can be linked immediately. In this way, the linking timing of the exercise results is adjusted according to the user's emotions, and linking is performed at an appropriate time.

[0110] The providing unit can estimate the user's emotions and adjust the method of providing advice based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide relaxing advice. If the user is relaxed, the providing unit can provide challenging advice. Furthermore, if the user is in a hurry, the providing unit can provide effective advice in a short amount of time. In this way, the method of providing advice is adjusted according to the user's emotions, and more appropriate advice is provided.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception unit receives voice input. For example, this is used when a user sets a goal by voice. Voice input can be performed using a microphone or the voice input function of a smartphone. Step 2: The collection unit collects meteorological information. For example, it collects climate and weather information in real time from the Internet. The collected information includes temperature, humidity, wind speed, etc. Step 3: The planning unit creates an exercise plan based on the voice input received by the reception unit and the information collected by the collection unit. For example, an optimal exercise plan for each individual user is created using AI. The AI ​​uses techniques such as machine learning and deep learning. Step 4: The linking unit links the exercise results with other health management apps based on the exercise plan created by the planning unit. For example, the linking unit automatically sends the exercise records to the health management apps. Step 5: The provider provides advice based on the exercise results linked by the linking unit. For example, advice can be presented in the form of text or images depending on the progress and situation, and can also be read aloud.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 reception unit that receives voice input; a collection unit that collects meteorological information; a planning unit that creates an exercise plan based on the voice input received by the receiving unit and the information collected by the collecting unit; a linking unit that links the exercise results with other health management applications based on the exercise plan formulated by the planning unit; a providing unit that provides advice based on the exercise results linked by the linking unit. A system characterized by:

2. The reception unit Equipped with technology to improve the accuracy of voice recognition 2. The system of claim 1.

3. The planning unit Using AI to create exercise plans tailored to individual users 2. The system of claim 1.

4. The collecting unit Instant weather information collection from the Internet 2. The system of claim 1.

5. The linking unit is Automatically link your exercise results with other health management apps 2. The system of claim 1.

6. The providing unit Advice is provided in the form of text and images according to progress, and voice reading is also supported.

2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past voice input history and select the optimal voice recognition algorithm 2. The system of claim 1.

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