system

Abstract

The system according to the embodiment aims to enable users to clearly define their ideal life and provide them with specific steps to realize it. [Solution] A system according to an embodiment includes a reception unit, a visualization unit, a definition unit, and a provision unit. The reception unit receives information from a user. The visualization unit visualizes the state of the user's life based on the information received by the reception unit. The definition unit defines an ideal life for the user based on the information visualized by the visualization unit. The provision unit provides specific steps for realizing the ideal life defined by the definition unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult for users to clearly define their ideal life and find specific steps to achieve it.

[0005] The system according to the embodiment aims to enable users to clearly define their ideal life and provide them with specific steps to realize it. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a visualization unit, a definition unit, and a provision unit. The reception unit receives information from a user. The visualization unit visualizes the state of the user's life based on the information received by the reception unit. The definition unit defines an ideal life for the user based on the information visualized by the visualization unit. The provision unit provides specific steps for realizing the ideal life defined by the definition unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to clearly define his or her ideal life and provides specific steps to realize it. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention uses a generative AI to define an individual's "good life" and support its realization. This system accepts information from a user, and the generative AI visualizes the user's life status based on that information, defines what a "good life" means for the user, and provides specific steps to achieve it. For example, a user may spend just three minutes a day conversing with the generative AI. Through this conversation, the generative AI grasps the user's current emotions, goals, values, and other information and visualizes the user's "life status." Next, the generative AI defines and helps the user recognize what a "good life" means for them. Finally, the generative AI provides specific steps to develop the "good life" axis and supports the user's efforts to achieve it. This allows the system to clarify the "good life" for the user and take concrete actions toward achieving it. For example, if a user defines a "good life" as "a healthy and happy life," the generative AI provides specific steps toward achieving that goal. By following these steps, the user can achieve a healthy and happy life.

[0029] A good life support system according to an embodiment includes a reception unit, a visualization unit, a definition unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, emotions, goals, and values. The reception unit receives, for example, text data entered by the user. The reception unit can also receive user information using voice input. The reception unit can also receive user information using image data. For example, the reception unit analyzes the text data entered by the user and extracts emotions and goals. When voice input is used, the text data is converted into text data using voice recognition technology and analyzed. When image data is used, information is extracted using image recognition technology. The visualization unit uses a generation AI to visualize the state of the user's life based on the information received by the reception unit. The visualization is performed using, for example, graphs and charts, but is not limited to, examples. For example, the visualization unit displays the user's emotions, goals, and values ​​using graphs and charts. The visualization unit can also display the user's information using a dashboard so that it can be understood at a glance. Furthermore, the visualization unit can use interactive graphs to allow the user to view detailed information. For example, the visualization unit can display the user's emotional data over time, visually showing changes in emotions. When using a dashboard, multiple graphs and charts can be combined and displayed to allow the user to grasp the overall situation. When using an interactive graph, the user can click on a specific data point to display detailed information. The definition unit uses generative AI to define a good life for the user based on the information visualized by the visualization unit. The definition is based on, for example, the user's values ​​and goals, but is not limited to such examples. For example, the definition unit can analyze the user's values ​​and goals and propose a definition of a good life. The definition unit can also allow the user to modify the proposed definition. Furthermore, the definition unit can update the definition based on user feedback. For example, the definition unit can analyze the user's values ​​data and propose a definition of a good life based on factors such as health, happiness, and success.If the user modifies the proposed definition, the user inputs the modifications through the interface and updates the definition. If the definition is updated based on user feedback, past feedback data is analyzed to improve the accuracy of the definition. The providing unit provides specific steps for realizing the good life defined by the defining unit. The steps may be based on, for example, a daily action plan or goal setting, but are not limited to such examples. For example, the providing unit may suggest to the user that they exercise a little bit every day. The providing unit may also suggest to the user that they learn a new skill. Furthermore, the providing unit may provide a specific action plan based on the good life defined by the user. For example, the providing unit may suggest to the user that they jog for 30 minutes every day. If they learn a new skill, the providing unit may suggest that they take an online course. If they provide a specific action plan, the providing unit may set weekly goals and manage progress. This allows the good life support system according to the embodiment to enable the user to clarify their own good life and take specific actions toward achieving it. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit may input the user's action plan into the generation AI, which may then suggest optimal steps.

[0030] The reception unit can accept information on the user's emotions, goals, and values. The reception unit, for example, accepts text data entered by the user. For example, if the user enters, "I'm tired from work today," the reception unit accepts the information. The reception unit can also accept user information using voice input. For example, if the user says, "My goal is to live a healthy life," the reception unit accepts the voice data. The reception unit can also accept user information using image data. For example, if the user uploads handwritten notes as images, the reception unit accepts the image data. This allows for more personalized support by accepting information such as the user's emotions, goals, and values. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's input data into a generation AI, which can then analyze the emotions and goals.

[0031] The visualization unit can display the user's emotions, goals, and values ​​in graphs and charts. The visualization unit, for example, displays the user's emotional data in a graph. For example, it displays a graph showing changes in the user's emotions over time. The visualization unit can also display the user's goals in a chart. For example, it displays a chart comparing the user's short-term goals and long-term goals. The visualization unit can also display the user's values ​​in a pie chart. For example, it displays a pie chart showing the proportions of the user's values. This allows the user to understand their state at a glance by visually displaying the user's emotions, goals, and values. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's data into a generation AI, which then generates a graph or chart.

[0032] The definition unit can propose a definition of a good life based on the user's values ​​and goals. The definition unit, for example, analyzes the user's value data and proposes a definition of a good life. For example, if the user values ​​"health" and "happiness," the definition unit proposes "a healthy and happy life" as a definition of a good life. The definition unit can also analyze the user's goal data and propose a definition of a good life. For example, if the user's goal is "career success," the definition unit proposes "a successful career" as a definition of a good life. Furthermore, the definition unit can allow the user to modify the proposed definition. For example, if the user feels that the proposed definition needs to be more specific, the definition unit accepts the user's input and modifies the definition. This allows the user to clarify what a good life means to them by proposing a definition of a good life based on the user's values ​​and goals. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's values ​​and goal data into the generation AI, which then proposes a definition of a good life.

[0033] The providing unit can suggest to the user that they exercise a little bit every day or learn a new skill. For example, the providing unit can suggest to the user that they exercise a little bit every day. For example, the providing unit can suggest to the user that they jog for 30 minutes every day. The providing unit can also suggest to the user that they learn a new skill. For example, the providing unit can suggest to the user that they take an online course. Furthermore, the providing unit can provide a specific action plan based on the good life defined by the user. For example, the providing unit can set weekly goals and manage progress for the user. By suggesting specific actions to the user, the providing unit can support the user in moving closer to a good life. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's action plan into a generation AI, which can then suggest optimal steps.

[0034] The provision unit can provide a specific action plan based on the good life defined by the user. The provision unit provides a specific action plan based on the good life defined by the user, for example. For example, the provision unit sets weekly goals and manages progress for the user. The provision unit can also provide a daily action plan to the user. For example, the provision unit provides a daily task list to the user. Furthermore, the provision unit can adjust the action plan according to the user's progress. For example, the provision unit analyzes the user's progress data and modifies the action plan as necessary. This allows the user to take specific actions toward achieving the specific action plan based on the good life defined by the user. Some or all of the above-mentioned processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input the user's action plan into a generation AI, which then suggests optimal steps.

[0035] The reception unit can analyze the user's past conversation history and select the most appropriate timing to accept information. For example, the reception unit accepts information at a specific time period based on the user's past conversation history. For example, if the user previously had conversations at 8:00 a.m. every morning, the reception unit accepts information at that time period. The reception unit can also analyze the frequency with which the user has had conversations in the past and accept information at the optimal timing. For example, if the user has conversations three times a week, the reception unit accepts information based on that frequency. Furthermore, the reception unit can also accept information on a specific day of the week based on the user's past conversation history. For example, if the user has conversations every Monday, the reception unit accepts information on that day of the week. In this way, by analyzing the user's past conversation history, information can be accepted at the optimal timing. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's conversation history data into a generation AI, which can select the optimal acceptance timing.

[0036] When receiving information, the reception unit can filter the information based on the user's current living situation and areas of interest. For example, if the user inputs their current living situation, the reception unit prioritizes receiving related information based on that information. For example, if the user inputs "I'm currently looking for a job," the reception unit accepts related information based on that information. The reception unit can also set the user's areas of interest in advance and prioritize receiving information related to those areas. For example, if the user sets their interest in "health," the reception unit prioritizes receiving health-related information. Furthermore, the reception unit can filter unnecessary information based on the user's living situation and areas of interest. For example, if the user inputs "I'm not currently interested in traveling," the reception unit filters travel-related information. By filtering information based on the user's living situation and areas of interest, more relevant information can be received. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's living situation and area of ​​interest data into a generation AI, which then filters the information.

[0037] When accepting information, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the information using voice recognition technology. For example, if the user inputs "What are your goals for today?" by voice, the acceptance unit accepts the voice data. Furthermore, if the user selects text input, the acceptance unit can also provide a text box to accept information. For example, if the user inputs "What are your goals for today?" by text, the acceptance unit accepts the text data. Furthermore, if the user selects image input, the acceptance unit can also accept information using image recognition technology. For example, if the user uploads handwritten notes as an image, the acceptance unit accepts the image data. This allows for efficient information acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, or without, a generation AI. For example, the acceptance unit can input the user's input data into a generation AI, which can then select the optimal acceptance means.

[0038] When accepting information, the reception unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting information related to that area. For example, if the user inputs "I'm currently in Tokyo," the reception unit prioritizes accepting information related to Tokyo. The reception unit can also prioritize accepting information about locations close to the user's current location. For example, if the user inputs "I'm currently looking for a nearby cafe," the reception unit prioritizes accepting information about nearby cafes based on that information. Furthermore, the reception unit can filter highly relevant information based on the user's geographical location information. For example, if the user inputs "I'm currently traveling," the reception unit prioritizes accepting information related to travel. This allows highly relevant information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into a generation AI, which can select highly relevant information.

[0039] When receiving information, the reception unit can analyze the user's social media activity and accept related information. The reception unit may accept related information based on, for example, information shared by the user on social media. For example, if the user inputs, "I've been posting a lot about health recently," the reception unit accepts health-related information based on that information. The reception unit may also analyze the user's social media activity history and accept related information. For example, if the user inputs, "I've been posting a lot about travel recently," the reception unit accepts travel-related information based on that information. The reception unit may also accept related information based on the activity of the user's friends on social media. For example, if the user inputs, "My friend has been posting a lot about fitness recently," the reception unit accepts fitness-related information based on that information. In this way, related information can be accepted by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's social media activity data into a generation AI, which may select related information.

[0040] When accepting information, the reception unit can customize the reception method by reflecting the user's past feedback. The reception unit adjusts the reception method based on, for example, feedback provided by the user in the past. For example, if the user provides feedback that "the previous input method was complicated," the reception unit simplifies the input method based on that information. The reception unit can also select the optimal reception method based on the user's past feedback. For example, if the user provides feedback that "voice input was convenient," the reception unit prioritizes voice input. The reception unit can also improve the reception method by reflecting the user's feedback. For example, if the user provides feedback that "there are too many input steps," the reception unit reduces the number of steps based on that information. In this way, the reception method can be customized by reflecting the user's past feedback, enabling more appropriate information acceptance. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input user feedback data into the generation AI, which can then customize the reception method.

[0041] The visualization unit can adjust the level of detail of the visualization based on the importance of the information when visualizing it. For example, the visualization unit displays important information in detail and other information in a simplified manner. For example, if the user's health data is important, the visualization unit displays the data in detail. The visualization unit can also display information in different colors or sizes depending on the importance of the information. For example, important information can be displayed in red and other information in blue. Furthermore, the visualization unit can prioritize the display of information with high importance and add detailed explanations. For example, a detailed explanation can be displayed in a pop-up for important information. In this way, important information can be displayed in detail by adjusting the level of detail of the visualization based on the importance of the information. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can input information importance data to the generation AI, which can then adjust the level of detail of the visualization.

[0042] The visualization unit can apply different visualization algorithms depending on the category of information during visualization. For example, the visualization unit displays emotion information as a graph and goal information as a chart. For example, the visualization unit displays the user's emotion data as a line graph and the goal data as a bar graph. The visualization unit can also display value information as a tree map and other information as a bubble chart. For example, the user's value data as a tree map and the behavioral data as a bubble chart. Furthermore, the visualization unit can select the optimal visualization algorithm depending on the category of information. For example, the visualization unit can display the user's health data as a heat map and the user's fitness data as a network graph. This allows for a deeper understanding of the information by applying the optimal visualization algorithm depending on the category of information. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input information category data into the generation AI, which then selects the optimal visualization algorithm.

[0043] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results during visualization. For example, the visualization unit improves the display method based on the user's past visualization results. For example, the visualization unit improves the design of a graph based on feedback from the user about graphs previously displayed. The visualization unit can also select the optimal display method based on the user's past visualization results. For example, the visualization unit can display a similar chart based on the type of chart the user previously preferred. Furthermore, the visualization unit can improve the accuracy of visualization by reflecting the user's past visualization results. For example, the accuracy of current data can be improved based on the accuracy of data previously displayed by the user. This improves the accuracy of visualization by referring to the user's past visualization results, enabling more appropriate information display. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's past visualization result data into the generation AI, which can then improve the accuracy of the visualization.

[0044] The visualization unit can determine the priority of visualization based on the time of information submission during visualization. For example, the visualization unit can prioritize displaying the latest information and displaying older information in a simplified manner. For example, the visualization unit can prioritize displaying data recently entered by the user and displaying older data in a simplified manner. The visualization unit can also display information in different colors or sizes depending on the time of information submission. For example, the latest information can be displayed in a large size and older information can be displayed in a small size. Furthermore, the visualization unit can prioritize displaying recently submitted information and adding detailed explanations. For example, detailed explanations can be displayed in a pop-up for the latest data. In this way, by determining the priority of visualization based on the time of information submission, the latest information can be displayed preferentially. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can input information submission time data into the generation AI, which can then determine the priority of visualization.

[0045] The visualization unit can adjust the visualization order based on the relevance of the information during visualization. For example, the visualization unit prioritizes displaying highly relevant information and simplifies the display of other information. For example, if a user's health data and fitness data are highly relevant, the visualization unit prioritizes displaying those data. The visualization unit can also display information in different colors or sizes depending on the relevance of the information. For example, highly relevant information can be displayed in red and other information in blue. Furthermore, the visualization unit can prioritize displaying highly relevant information and adding detailed explanations. For example, detailed explanations can be displayed in pop-ups for highly relevant information. In this way, by adjusting the visualization order based on the relevance of the information, highly relevant information can be prioritized and displayed. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI. For example, the visualization unit can input relevance data of the information into the generation AI, which can then adjust the visualization order.

[0046] The visualization unit can adjust the use of technical terms in visualization according to the user's level of expertise during visualization. For example, if the user has technical expertise, the visualization unit uses technical terms to perform visualization. For example, if the user inputs "I have medical expertise," the visualization unit displays the data using medical terms. The visualization unit can also perform visualization using simpler language if the user does not have technical expertise. For example, if the user inputs "I do not have medical expertise," the visualization unit displays the data in simpler language. Furthermore, the visualization unit can select optimal terms according to the user's level of expertise. For example, if the user inputs "I have intermediate-level knowledge," the visualization unit displays the data using intermediate-level terms. This allows the display of information that is easy for the user to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's level of expertise data into the generation AI, which then selects optimal terms.

[0047] The definition unit can adjust the level of detail of the definition based on the user's values ​​and goals during definition. The definition unit provides a detailed definition based on the user's values. For example, if the user values ​​"health," the definition unit provides a detailed definition related to health. The definition unit can also provide a specific definition based on the user's goals. For example, if the user's goal is "career success," the definition unit provides a specific definition related to career. Furthermore, the definition unit can select the optimal definition method based on the user's values ​​and goals. For example, if the user inputs "I want to cherish time with my family," the definition unit provides a definition related to family. By adjusting the level of detail of the definition based on the user's values ​​and goals, it is possible to create a definition that is optimal for the user. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's values ​​and goal data into the generation AI, which can then adjust the level of detail of the definition.

[0048] The definition unit can improve the accuracy of the definition by referring to the user's past definition results during definition. For example, the definition unit improves the definition method based on the user's past definition results. For example, the definition unit improves the definition method based on the achievement status of goals set by the user in the past. The definition unit can also select the optimal definition method from the user's past definition results. For example, the definition unit can set a similar goal based on the achievement status of goals set by the user in the past. Furthermore, the definition unit can improve the accuracy of the definition by reflecting the user's past definition results. For example, the definition unit can improve the accuracy of the current goal based on the achievement status of goals set by the user in the past. By referring to the user's past definition results, the accuracy of the definition can be improved and more appropriate definitions can be made. Some or all of the above-mentioned processing in the definition unit can be performed, for example, using a generation AI or without a generation AI. For example, the definition unit can input the user's past definition result data into the generation AI, which can improve the accuracy of the definition.

[0049] The definition unit can customize the definition means based on the user's current living situation during definition. For example, if the user inputs the user's current living situation, the definition unit provides the optimal definition means based on that information. For example, if the user inputs "I am currently looking for a job," the definition unit provides a work-related definition based on that information. The definition unit can also customize the definition means according to the user's living situation to provide a method that is most understandable to the user. For example, if the user inputs "My home environment has changed," the definition unit provides a home-related definition based on that information. Furthermore, the definition unit can flexibly adjust the definition means taking into account the user's current living situation. For example, if the user inputs "I am currently in poor health," the definition unit provides a health-related definition based on that information. This allows the optimal definition for the user to be created by customizing the definition means based on the user's current living situation. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's living situation data into the generation AI, which can then customize the definition means.

[0050] The definition unit can select the optimal definition method by taking into account the user's geographical location information. For example, if the user is in a specific area, the definition unit provides a definition by taking into account values ​​and goals related to that area. For example, if the user inputs "I'm currently in Tokyo," the definition unit provides a definition by taking into account values ​​and goals related to Tokyo. The definition unit can also provide a definition based on information related to places close to the user's current location. For example, if the user inputs "I'm currently looking for a nearby cafe," the definition unit provides a definition related to cafes based on that information. The definition unit can also select the optimal definition method based on the user's geographical location information. For example, if the user inputs "I'm currently traveling," the definition unit provides a definition by taking into account values ​​and goals related to travel. In this way, the optimal definition method for the user is selected by taking into account the user's geographical location information. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's geographical location information data into the generation AI, which can then select the optimal definition method.

[0051] The definition unit can analyze the user's social media activity and suggest definition means at the time of definition. For example, the definition unit can suggest related definition means based on information shared by the user on social media. For example, if the user inputs "I've been posting a lot about health recently," the definition unit can suggest health-related definition means based on that information. The definition unit can also analyze the user's social media activity history and suggest related definition means. For example, if the user inputs "I've been posting a lot about travel recently," the definition unit can suggest travel-related definition means based on that information. Furthermore, the definition unit can suggest related definition means based on the activity of the user's friends on social media. For example, if the user inputs "My friends have been posting about fitness recently," the definition unit can suggest fitness-related definition means based on that information. In this way, related definition means can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the definition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the definition unit can input the user's social media activity data into the generation AI, which can then suggest related definition means.

[0052] The definition unit can customize the definition method by reflecting the user's past feedback during definition. The definition unit adjusts the definition method based on, for example, feedback provided by the user in the past. For example, if the user provides feedback that "the previous definition method was complicated," the definition unit simplifies the definition method based on that information. The definition unit can also select the optimal definition method based on the user's past feedback. For example, if the user provides feedback that "a simple definition method was convenient," the definition unit prioritizes the simple definition method. The definition unit can also improve the definition method by reflecting the user's feedback. For example, if the user provides feedback that "there are too many definition steps," the definition unit reduces the number of steps based on that information. This allows the definition method to be customized by reflecting the user's past feedback, enabling more appropriate definitions. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input user feedback data into the generation AI, which then customizes the definition method.

[0053] The providing unit can analyze the user's past behavioral history and select optimal steps when providing the data. The providing unit provides optimal steps based on, for example, steps the user has performed in the past. For example, if the user inputs "I have jogged in the past," the providing unit suggests jogging based on that information. The providing unit can also select effective steps from the user's past behavioral history. For example, if the user inputs "I have done yoga in the past," the providing unit suggests yoga based on that information. The providing unit can also analyze the user's past behavioral history and provide the most efficient steps. For example, if the user inputs "I have done strength training in the past," the providing unit suggests strength training based on that information. In this way, optimal steps are provided by analyzing the user's past behavioral history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's behavioral history data into a generation AI, which can select optimal steps.

[0054] The providing unit can customize the step means based on the user's current living situation when providing the step information. For example, if the user inputs the user's current living situation, the providing unit provides the optimal step based on that information. For example, if the user inputs "I am currently looking for a job," the providing unit provides work-related steps based on that information. The providing unit can also customize the steps according to the user's living situation and provide the easiest method for the user to perform. For example, if the user inputs "My home environment has changed," the providing unit provides home-related steps based on that information. Furthermore, the providing unit can flexibly adjust the steps taking into account the user's current living situation. For example, if the user inputs "I am currently in poor health," the providing unit provides health-related steps based on that information. In this way, by customizing the step means based on the user's current living situation, the optimal step for the user is provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's living situation data into the generation AI, which can then customize the steps.

[0055] The providing unit can improve the step method by reflecting user feedback when providing the step method. The providing unit, for example, adjusts the step method based on feedback provided by the user in the past. For example, if the user provides feedback that "the previous step method was complicated," the providing unit simplifies the step method based on that information. The providing unit can also select the optimal step method based on the user's past feedback. For example, if the user provides feedback that "simple step methods were convenient," the providing unit prioritizes simple step methods. The providing unit can also improve the step method by reflecting user feedback. For example, if the user provides feedback that "there are too many step procedures," the providing unit reduces the number of procedures based on that information. In this way, the step method is improved by reflecting the user's past feedback, and more appropriate steps are provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into a generation AI, which can then improve the step method.

[0056] The providing unit can select optimal steps by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides steps related to that area. For example, if the user inputs "I'm currently in Tokyo," the providing unit provides steps related to Tokyo. The providing unit can also provide steps related to locations close to the user's current location. For example, if the user inputs "I'm currently looking for a nearby cafe," the providing unit provides steps related to cafes based on that information. The providing unit can also select optimal steps based on the user's geographical location information. For example, if the user inputs "I'm currently traveling," the providing unit provides steps related to travel. In this way, optimal steps for the user are provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which then selects the optimal steps.

[0057] The providing unit can analyze the user's social media activity and suggest step measures at the time of providing the information. The providing unit can suggest relevant step measures based on, for example, information shared by the user on social media. For example, if the user inputs, "I've been posting a lot about health recently," the providing unit can suggest health-related step measures based on that information. The providing unit can also analyze the user's social media activity history and suggest relevant step measures. For example, if the user inputs, "I've been posting a lot about travel recently," the providing unit can suggest travel-related step measures based on that information. The providing unit can also suggest relevant step measures based on the activity of the user's friends on social media. For example, if the user inputs, "My friend has been posting a lot about fitness recently," the providing unit can suggest fitness-related step measures based on that information. In this way, relevant step measures can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into a generation AI, which can then suggest relevant step measures.

[0058] When providing the step method, the providing unit can customize the step method by reflecting the user's past feedback. The providing unit, for example, adjusts the step method based on feedback provided by the user in the past. For example, if the user provides feedback that "the previous step method was complicated," the providing unit simplifies the step method based on that information. The providing unit can also select the optimal step method based on the user's past feedback. For example, if the user provides feedback that "simple step methods were convenient," the providing unit prioritizes simple step methods. The providing unit can also improve the step method by reflecting the user's feedback. For example, if the user provides feedback that "there are too many step procedures," the providing unit reduces the number of procedures based on that information. In this way, the step method is customized by reflecting the user's past feedback, and more appropriate steps are provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI, which can then customize the step method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the user's input data and estimate the user's interests and concerns. For example, if the user inputs "I've been interested in cooking recently," the reception unit can preferentially accept information related to cooking based on that information. Also, if the user inputs "I've been interested in fitness recently," the reception unit can preferentially accept information related to fitness. Furthermore, the reception unit can filter related information based on the user's interests and concerns. For example, if the user inputs "I've not been interested in traveling recently," the reception unit filters information related to travel. In this way, by accepting information based on the user's interests and concerns, more relevant information can be provided.

[0061] The reception unit can analyze the user's past behavioral history and select the most appropriate timing to receive information. For example, based on the user's past behavioral history during a specific time period, the reception unit can receive information during that time period. For example, if the user previously exercised every morning at 8:00, the reception unit can receive information during that time period. The reception unit can also analyze how frequently the user has performed a behavior in the past and receive information at the optimal timing. For example, if the user exercised three times a week, the reception unit can receive information based on that frequency. Furthermore, the reception unit can receive information on a specific day of the week based on the user's past behavioral history. For example, if the user exercised every Monday, the reception unit can receive information on that day of the week. In this way, by analyzing the user's past behavioral history, the reception unit can receive information at the optimal timing.

[0062] When visualizing information, the visualization unit can adjust the level of detail of the visualization based on the importance of the information. For example, important information is displayed in detail, and other information is displayed in a simplified manner. For example, if the user's health data is important, the visualization unit displays the data in detail. The visualization unit can also display information in different colors or sizes depending on the importance of the information. For example, important information is displayed in red, and other information is displayed in blue. Furthermore, the visualization unit can prioritize the display of information with high importance and add detailed explanations. For example, a detailed explanation is displayed for important information in a pop-up. In this way, important information can be displayed in detail by adjusting the level of detail of the visualization based on the importance of the information.

[0063] The definition unit can adjust the level of detail of the definition based on the user's values ​​and goals when defining the definition. For example, it provides a detailed definition based on the user's values. For example, if the user values ​​"health," the definition unit provides a detailed definition related to health. The definition unit can also provide a specific definition based on the user's goals. For example, if the user's goal is "career success," the definition unit provides a specific definition related to career. Furthermore, the definition unit can select the optimal definition method based on the user's values ​​and goals. For example, if the user inputs "I want to cherish time with my family," the definition unit provides a definition related to family. In this way, by adjusting the level of detail of the definition based on the user's values ​​and goals, it is possible to create a definition that is optimal for the user.

[0064] When providing the step information, the providing unit can analyze the user's past behavioral history and select the optimal step. For example, the optimal step is provided based on the steps the user has performed in the past. For example, if the user inputs "I have jogged in the past," the providing unit suggests jogging based on that information. The providing unit can also select effective steps from the user's past behavioral history. For example, if the user inputs "I have done yoga in the past," the providing unit suggests yoga based on that information. Furthermore, the providing unit can analyze the user's past behavioral history and provide the most efficient step. For example, if the user inputs "I have done strength training in the past," the providing unit suggests strength training based on that information. In this way, the optimal step is provided by analyzing the user's past behavioral history.

[0065] When providing the step method, the providing unit can improve the step method by reflecting user feedback. For example, the step method is adjusted based on feedback provided by the user in the past. For example, if the user provides feedback that "the previous step method was complicated," the providing unit simplifies the step method based on that information. The providing unit can also select the optimal step method based on the user's past feedback. For example, if the user provides feedback that "simple step methods were convenient," the providing unit prioritizes simple step methods. Furthermore, the providing unit can also improve the step method by reflecting user feedback. For example, if the user provides feedback that "there are too many step procedures," the providing unit reduces the number of procedures based on that information. In this way, the step method is improved by reflecting the user's past feedback, and more appropriate steps are provided.

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

[0067] Step 1: The reception unit receives information from the user. Information from the user includes emotions, goals, values, etc. The reception unit can receive text data, voice input, and image data entered by the user. For example, it analyzes the text data and extracts emotions and goals. In the case of voice input, it converts the voice input into text data using voice recognition technology and then analyzes it. In the case of image data, it extracts information using image recognition technology. Step 2: The visualization unit uses generation AI to visualize the state of the user's life based on the information received by the reception unit. Visualization is performed using graphs, charts, dashboards, and interactive graphs. For example, the user's emotional data can be displayed over time to visually show changes in emotions. When using a dashboard, multiple graphs and charts can be displayed in combination to allow the user to grasp the overall situation. When using an interactive graph, the user can click on a specific data point to display detailed information. Step 3: The definition unit uses generative AI to define a good life for the user based on the information visualized by the visualization unit. The definition is based on the user's values ​​and goals. For example, the definition may be proposed based on an analysis of the user's values ​​and goals. The user can also modify the proposed definition, and the definition can be updated based on user feedback. Step 4: The provision part provides specific steps to achieve the good life defined by the definition part. The steps are based on daily action plans and goal setting. For example, it may suggest exercising a little bit every day or learning a new skill. When providing a specific action plan, it also sets weekly goals and manages progress. The processing in the provision part may be performed using a generation AI, which can suggest optimal steps.

[0068] (Example 2) A system according to an embodiment of the present invention uses a generative AI to define an individual's "good life" and support its realization. This system accepts information from a user, and the generative AI visualizes the user's life status based on that information, defines what a "good life" means for the user, and provides specific steps to achieve it. For example, a user may spend just three minutes a day conversing with the generative AI. Through this conversation, the generative AI grasps the user's current emotions, goals, values, and other information and visualizes the user's "life status." Next, the generative AI defines and helps the user recognize what a "good life" means for them. Finally, the generative AI provides specific steps to develop the "good life" axis and supports the user's efforts to achieve it. This allows the system to clarify the "good life" for the user and take concrete actions toward achieving it. For example, if a user defines a "good life" as "a healthy and happy life," the generative AI provides specific steps toward achieving that goal. By following these steps, the user can achieve a healthy and happy life.

[0069] A good life support system according to an embodiment includes a reception unit, a visualization unit, a definition unit, and a provision unit. The reception unit receives information from a user. The information from the user includes, but is not limited to, emotions, goals, and values. The reception unit receives, for example, text data entered by the user. The reception unit can also receive user information using voice input. The reception unit can also receive user information using image data. For example, the reception unit analyzes the text data entered by the user and extracts emotions and goals. When voice input is used, the text data is converted into text data using voice recognition technology and analyzed. When image data is used, information is extracted using image recognition technology. The visualization unit uses a generation AI to visualize the state of the user's life based on the information received by the reception unit. The visualization is performed using, for example, graphs and charts, but is not limited to, examples. For example, the visualization unit displays the user's emotions, goals, and values ​​using graphs and charts. The visualization unit can also display the user's information using a dashboard so that it can be understood at a glance. Furthermore, the visualization unit can use interactive graphs to allow the user to view detailed information. For example, the visualization unit can display the user's emotional data over time, visually showing changes in emotions. When using a dashboard, multiple graphs and charts can be combined and displayed to allow the user to grasp the overall situation. When using an interactive graph, the user can click on a specific data point to display detailed information. The definition unit uses generative AI to define a good life for the user based on the information visualized by the visualization unit. The definition is based on, for example, the user's values ​​and goals, but is not limited to such examples. For example, the definition unit can analyze the user's values ​​and goals and propose a definition of a good life. The definition unit can also allow the user to modify the proposed definition. Furthermore, the definition unit can update the definition based on user feedback. For example, the definition unit can analyze the user's values ​​data and propose a definition of a good life based on factors such as health, happiness, and success.If the user modifies the proposed definition, the user inputs the modifications through the interface and updates the definition. If the definition is updated based on user feedback, past feedback data is analyzed to improve the accuracy of the definition. The providing unit provides specific steps for realizing the good life defined by the defining unit. The steps may be based on, for example, a daily action plan or goal setting, but are not limited to such examples. For example, the providing unit may suggest to the user that they exercise a little bit every day. The providing unit may also suggest to the user that they learn a new skill. Furthermore, the providing unit may provide a specific action plan based on the good life defined by the user. For example, the providing unit may suggest to the user that they jog for 30 minutes every day. If they learn a new skill, the providing unit may suggest that they take an online course. If they provide a specific action plan, the providing unit may set weekly goals and manage progress. This allows the good life support system according to the embodiment to enable the user to clarify their own good life and take specific actions toward achieving it. Some or all of the above-described processing by the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit may input the user's action plan into the generation AI, which may then suggest optimal steps.

[0070] The reception unit can accept information on the user's emotions, goals, and values. The reception unit, for example, accepts text data entered by the user. For example, if the user enters, "I'm tired from work today," the reception unit accepts the information. The reception unit can also accept user information using voice input. For example, if the user says, "My goal is to live a healthy life," the reception unit accepts the voice data. The reception unit can also accept user information using image data. For example, if the user uploads handwritten notes as images, the reception unit accepts the image data. This allows for more personalized support by accepting information such as the user's emotions, goals, and values. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's input data into a generation AI, which can then analyze the emotions and goals.

[0071] The visualization unit can display the user's emotions, goals, and values ​​in graphs and charts. The visualization unit, for example, displays the user's emotional data in a graph. For example, it displays a graph showing changes in the user's emotions over time. The visualization unit can also display the user's goals in a chart. For example, it displays a chart comparing the user's short-term goals and long-term goals. The visualization unit can also display the user's values ​​in a pie chart. For example, it displays a pie chart showing the proportions of the user's values. This allows the user to understand their state at a glance by visually displaying the user's emotions, goals, and values. Some or all of the above-mentioned processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's data into a generation AI, which then generates a graph or chart.

[0072] The definition unit can propose a definition of a good life based on the user's values ​​and goals. The definition unit, for example, analyzes the user's value data and proposes a definition of a good life. For example, if the user values ​​"health" and "happiness," the definition unit proposes "a healthy and happy life" as a definition of a good life. The definition unit can also analyze the user's goal data and propose a definition of a good life. For example, if the user's goal is "career success," the definition unit proposes "a successful career" as a definition of a good life. Furthermore, the definition unit can allow the user to modify the proposed definition. For example, if the user feels that the proposed definition needs to be more specific, the definition unit accepts the user's input and modifies the definition. This allows the user to clarify what a good life means to them by proposing a definition of a good life based on the user's values ​​and goals. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's values ​​and goal data into the generation AI, which then proposes a definition of a good life.

[0073] The providing unit can suggest to the user that they exercise a little bit every day or learn a new skill. For example, the providing unit can suggest to the user that they exercise a little bit every day. For example, the providing unit can suggest to the user that they jog for 30 minutes every day. The providing unit can also suggest to the user that they learn a new skill. For example, the providing unit can suggest to the user that they take an online course. Furthermore, the providing unit can provide a specific action plan based on the good life defined by the user. For example, the providing unit can set weekly goals and manage progress for the user. By suggesting specific actions to the user, the providing unit can support the user in moving closer to a good life. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's action plan into a generation AI, which can then suggest optimal steps.

[0074] The provision unit can provide a specific action plan based on the good life defined by the user. The provision unit provides a specific action plan based on the good life defined by the user, for example. For example, the provision unit sets weekly goals and manages progress for the user. The provision unit can also provide a daily action plan to the user. For example, the provision unit provides a daily task list to the user. Furthermore, the provision unit can adjust the action plan according to the user's progress. For example, the provision unit analyzes the user's progress data and modifies the action plan as necessary. This allows the user to take specific actions toward achieving the specific action plan based on the good life defined by the user. Some or all of the above-mentioned processing in the provision unit may be performed using, or without, a generation AI. For example, the provision unit can input the user's action plan into a generation AI, which then suggests optimal steps.

[0075] The reception unit can estimate the user's emotions and adjust the information reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user inputs "I'm tired today," the reception unit simplifies the interface based on that information. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. For example, if the user inputs "I'm relaxed today," the reception unit displays detailed input options. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. For example, if the user inputs "I'm in a hurry," the reception unit recommends voice input. This allows for more appropriate information reception by adjusting the information reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input the user's input data to the generation AI, which may infer the emotion and adjust the reception method.

[0076] The reception unit can analyze the user's past conversation history and select the most appropriate timing to accept information. For example, the reception unit accepts information at a specific time period based on the user's past conversation history. For example, if the user previously had conversations at 8:00 a.m. every morning, the reception unit accepts information at that time period. The reception unit can also analyze the frequency with which the user has had conversations in the past and accept information at the optimal timing. For example, if the user has conversations three times a week, the reception unit accepts information based on that frequency. Furthermore, the reception unit can also accept information on a specific day of the week based on the user's past conversation history. For example, if the user has conversations every Monday, the reception unit accepts information on that day of the week. In this way, by analyzing the user's past conversation history, information can be accepted at the optimal timing. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's conversation history data into a generation AI, which can select the optimal acceptance timing.

[0077] When receiving information, the reception unit can filter the information based on the user's current living situation and areas of interest. For example, if the user inputs their current living situation, the reception unit prioritizes receiving related information based on that information. For example, if the user inputs "I'm currently looking for a job," the reception unit accepts related information based on that information. The reception unit can also set the user's areas of interest in advance and prioritize receiving information related to those areas. For example, if the user sets their interest in "health," the reception unit prioritizes receiving health-related information. Furthermore, the reception unit can filter unnecessary information based on the user's living situation and areas of interest. For example, if the user inputs "I'm not currently interested in traveling," the reception unit filters travel-related information. By filtering information based on the user's living situation and areas of interest, more relevant information can be received. Some or all of the above-described processing in the reception unit can be performed using, or without, a generation AI. For example, the reception unit can input the user's living situation and area of ​​interest data into a generation AI, which then filters the information.

[0078] When accepting information, the acceptance unit can select the optimal acceptance means depending on the user's input method. For example, if the user selects voice input, the acceptance unit accepts the information using voice recognition technology. For example, if the user inputs "What are your goals for today?" by voice, the acceptance unit accepts the voice data. Furthermore, if the user selects text input, the acceptance unit can also provide a text box to accept information. For example, if the user inputs "What are your goals for today?" by text, the acceptance unit accepts the text data. Furthermore, if the user selects image input, the acceptance unit can also accept information using image recognition technology. For example, if the user uploads handwritten notes as an image, the acceptance unit accepts the image data. This allows for efficient information acceptance by selecting the optimal acceptance means depending on the user's input method. Some or all of the above-described processing in the acceptance unit may be performed using, or without, a generation AI. For example, the acceptance unit can input the user's input data into a generation AI, which can then select the optimal acceptance means.

[0079] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prioritizes receiving important information. For example, if the user inputs "I'm stressed today," the reception unit prioritizes receiving important information based on that information. The reception unit can also prioritize receiving detailed information if the user is relaxed. For example, if the user inputs "I'm relaxed today," the reception unit prioritizes receiving detailed information. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving information that requires prompt processing. For example, if the user inputs "I'm in a hurry," the reception unit prioritizes receiving information that requires prompt processing. In this way, by determining the priority of information according to the user's emotions, important information can be 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-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input user input data to the generation AI, which may then estimate emotions and determine the priority of the information.

[0080] When accepting information, the reception unit can prioritize accepting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes accepting information related to that area. For example, if the user inputs "I'm currently in Tokyo," the reception unit prioritizes accepting information related to Tokyo. The reception unit can also prioritize accepting information about locations close to the user's current location. For example, if the user inputs "I'm currently looking for a nearby cafe," the reception unit prioritizes accepting information about nearby cafes based on that information. Furthermore, the reception unit can filter highly relevant information based on the user's geographical location information. For example, if the user inputs "I'm currently traveling," the reception unit prioritizes accepting information related to travel. This allows highly relevant information to be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into a generation AI, which can select highly relevant information.

[0081] When receiving information, the reception unit can analyze the user's social media activity and accept related information. The reception unit may accept related information based on, for example, information shared by the user on social media. For example, if the user inputs, "I've been posting a lot about health recently," the reception unit accepts health-related information based on that information. The reception unit may also analyze the user's social media activity history and accept related information. For example, if the user inputs, "I've been posting a lot about travel recently," the reception unit accepts travel-related information based on that information. The reception unit may also accept related information based on the activity of the user's friends on social media. For example, if the user inputs, "My friend has been posting a lot about fitness recently," the reception unit accepts fitness-related information based on that information. In this way, related information can be accepted by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit may input the user's social media activity data into a generation AI, which may select related information.

[0082] When accepting information, the reception unit can customize the reception method by reflecting the user's past feedback. The reception unit adjusts the reception method based on, for example, feedback provided by the user in the past. For example, if the user provides feedback that "the previous input method was complicated," the reception unit simplifies the input method based on that information. The reception unit can also select the optimal reception method based on the user's past feedback. For example, if the user provides feedback that "voice input was convenient," the reception unit prioritizes voice input. The reception unit can also improve the reception method by reflecting the user's feedback. For example, if the user provides feedback that "there are too many input steps," the reception unit reduces the number of steps based on that information. In this way, the reception method can be customized by reflecting the user's past feedback, enabling more appropriate information acceptance. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input user feedback data into the generation AI, which can then customize the reception method.

[0083] The visualization unit can estimate the user's emotions and adjust the visualization expression method based on the estimated user emotions. For example, if the user is feeling stressed, the visualization unit displays a simple, highly visible graph. For example, if the user inputs "I'm stressed today," the visualization unit displays a simple graph based on that information. Furthermore, if the user is relaxed, the visualization unit can display a chart containing detailed information. For example, if the user inputs "I'm relaxed today," the visualization unit displays a detailed chart. Furthermore, if the user is excited, the visualization unit can also perform visualization with a visually stimulating effect. For example, if the user inputs "I'm excited today," the visualization unit displays a graph with a visually stimulating effect. This allows for more appropriate visualization by adjusting the visualization expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the visualization unit can be performed using, for example, a generation AI, or without a generation AI. For example, the visualization unit can input user input data into the generation AI, which can then infer emotions and adjust the visualization expression method.

[0084] The visualization unit can adjust the level of detail of the visualization based on the importance of the information when visualizing it. For example, the visualization unit displays important information in detail and other information in a simplified manner. For example, if the user's health data is important, the visualization unit displays the data in detail. The visualization unit can also display information in different colors or sizes depending on the importance of the information. For example, important information can be displayed in red and other information in blue. Furthermore, the visualization unit can prioritize the display of information with high importance and add detailed explanations. For example, a detailed explanation can be displayed in a pop-up for important information. In this way, important information can be displayed in detail by adjusting the level of detail of the visualization based on the importance of the information. Some or all of the above-mentioned processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can input information importance data to the generation AI, which can then adjust the level of detail of the visualization.

[0085] The visualization unit can apply different visualization algorithms depending on the category of information during visualization. For example, the visualization unit displays emotion information as a graph and goal information as a chart. For example, the visualization unit displays the user's emotion data as a line graph and the goal data as a bar graph. The visualization unit can also display value information as a tree map and other information as a bubble chart. For example, the user's value data as a tree map and the behavioral data as a bubble chart. Furthermore, the visualization unit can select the optimal visualization algorithm depending on the category of information. For example, the visualization unit can display the user's health data as a heat map and the user's fitness data as a network graph. This allows for a deeper understanding of the information by applying the optimal visualization algorithm depending on the category of information. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input information category data into the generation AI, which then selects the optimal visualization algorithm.

[0086] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results during visualization. For example, the visualization unit improves the display method based on the user's past visualization results. For example, the visualization unit improves the design of a graph based on feedback from the user about graphs previously displayed. The visualization unit can also select the optimal display method based on the user's past visualization results. For example, the visualization unit can display a similar chart based on the type of chart the user previously preferred. Furthermore, the visualization unit can improve the accuracy of visualization by reflecting the user's past visualization results. For example, the accuracy of current data can be improved based on the accuracy of data previously displayed by the user. This improves the accuracy of visualization by referring to the user's past visualization results, enabling more appropriate information display. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's past visualization result data into the generation AI, which can then improve the accuracy of the visualization.

[0087] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated user emotions. For example, if the user is in a hurry, the visualization unit generates a short, concise visualization. For example, if the user inputs "I'm in a hurry," the visualization unit displays a short graph based on that information. Furthermore, if the user is relaxed, the visualization unit can generate a longer visualization with detailed explanations. For example, if the user inputs "I'm relaxed," the visualization unit displays a graph with detailed explanations. Furthermore, if the user is excited, the visualization unit can generate a visualization with visually stimulating effects. For example, if the user inputs "I'm excited," the visualization unit displays a graph with visually stimulating effects. This allows the length of the visualization to be adjusted according to the user's emotions, thereby displaying information optimally for the user. Emotion estimation is achieved using an emotion estimation function, such as 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-described processing in the visualization unit can be performed using, for example, a generation AI, or without a generation AI. For example, the visualization unit can input user input data into the generation AI, which can then estimate emotions and adjust the length of visualization.

[0088] The visualization unit can determine the priority of visualization based on the time of information submission during visualization. For example, the visualization unit can prioritize displaying the latest information and displaying older information in a simplified manner. For example, the visualization unit can prioritize displaying data recently entered by the user and displaying older data in a simplified manner. The visualization unit can also display information in different colors or sizes depending on the time of information submission. For example, the latest information can be displayed in a large size and older information can be displayed in a small size. Furthermore, the visualization unit can prioritize displaying recently submitted information and adding detailed explanations. For example, detailed explanations can be displayed in a pop-up for the latest data. In this way, by determining the priority of visualization based on the time of information submission, the latest information can be displayed preferentially. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the visualization unit can input information submission time data into the generation AI, which can then determine the priority of visualization.

[0089] The visualization unit can adjust the visualization order based on the relevance of the information during visualization. For example, the visualization unit prioritizes displaying highly relevant information and simplifies the display of other information. For example, if a user's health data and fitness data are highly relevant, the visualization unit prioritizes displaying those data. The visualization unit can also display information in different colors or sizes depending on the relevance of the information. For example, highly relevant information can be displayed in red and other information in blue. Furthermore, the visualization unit can prioritize displaying highly relevant information and adding detailed explanations. For example, detailed explanations can be displayed in pop-ups for highly relevant information. In this way, by adjusting the visualization order based on the relevance of the information, highly relevant information can be prioritized and displayed. Some or all of the above-described processing in the visualization unit can be performed using, for example, a generation AI. For example, the visualization unit can input relevance data of the information into the generation AI, which can then adjust the visualization order.

[0090] The visualization unit can adjust the use of technical terms in visualization according to the user's level of expertise during visualization. For example, if the user has technical expertise, the visualization unit uses technical terms to perform visualization. For example, if the user inputs "I have medical expertise," the visualization unit displays the data using medical terms. The visualization unit can also perform visualization using simpler language if the user does not have technical expertise. For example, if the user inputs "I do not have medical expertise," the visualization unit displays the data in simpler language. Furthermore, the visualization unit can select optimal terms according to the user's level of expertise. For example, if the user inputs "I have intermediate-level knowledge," the visualization unit displays the data using intermediate-level terms. This allows the display of information that is easy for the user to understand by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the visualization unit may be performed using, or without, a generation AI. For example, the visualization unit can input the user's level of expertise data into the generation AI, which then selects optimal terms.

[0091] The definition unit can estimate the user's emotions and adjust the definition of a good life based on the estimated user emotions. For example, if the user is feeling stressed, the definition unit provides a simple, highly visible definition. For example, if the user inputs "I'm stressed today," the definition unit provides a simple definition based on that information. The definition unit can also provide a definition that includes detailed information if the user is relaxed. For example, if the user inputs "I'm relaxed today," the definition unit provides a detailed definition. Furthermore, if the user is excited, the definition unit can provide a definition that adds a visually stimulating effect. For example, if the user inputs "I'm excited today," the definition unit provides a definition that adds a visually stimulating effect. This allows the definition of a good life to be optimized for the user by adjusting it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative 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-described processing in the definition unit may be performed using, for example, a generation AI. For example, the definition unit may input user input data to the generation AI, which may then estimate the emotion and adjust the definition method.

[0092] The definition unit can adjust the level of detail of the definition based on the user's values ​​and goals during definition. The definition unit provides a detailed definition based on the user's values. For example, if the user values ​​"health," the definition unit provides a detailed definition related to health. The definition unit can also provide a specific definition based on the user's goals. For example, if the user's goal is "career success," the definition unit provides a specific definition related to career. Furthermore, the definition unit can select the optimal definition method based on the user's values ​​and goals. For example, if the user inputs "I want to cherish time with my family," the definition unit provides a definition related to family. By adjusting the level of detail of the definition based on the user's values ​​and goals, it is possible to create a definition that is optimal for the user. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's values ​​and goal data into the generation AI, which can then adjust the level of detail of the definition.

[0093] The definition unit can improve the accuracy of the definition by referring to the user's past definition results during definition. For example, the definition unit improves the definition method based on the user's past definition results. For example, the definition unit improves the definition method based on the achievement status of goals set by the user in the past. The definition unit can also select the optimal definition method from the user's past definition results. For example, the definition unit can set a similar goal based on the achievement status of goals set by the user in the past. Furthermore, the definition unit can improve the accuracy of the definition by reflecting the user's past definition results. For example, the definition unit can improve the accuracy of the current goal based on the achievement status of goals set by the user in the past. By referring to the user's past definition results, the accuracy of the definition can be improved and more appropriate definitions can be made. Some or all of the above-mentioned processing in the definition unit can be performed, for example, using a generation AI or without a generation AI. For example, the definition unit can input the user's past definition result data into the generation AI, which can improve the accuracy of the definition.

[0094] The definition unit can customize the definition means based on the user's current living situation during definition. For example, if the user inputs the user's current living situation, the definition unit provides the optimal definition means based on that information. For example, if the user inputs "I am currently looking for a job," the definition unit provides a work-related definition based on that information. The definition unit can also customize the definition means according to the user's living situation to provide a method that is most understandable to the user. For example, if the user inputs "My home environment has changed," the definition unit provides a home-related definition based on that information. Furthermore, the definition unit can flexibly adjust the definition means taking into account the user's current living situation. For example, if the user inputs "I am currently in poor health," the definition unit provides a health-related definition based on that information. This allows the optimal definition for the user to be created by customizing the definition means based on the user's current living situation. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's living situation data into the generation AI, which can then customize the definition means.

[0095] The definition unit can estimate the user's emotions and prioritize definitions of a good life based on the estimated user emotions. For example, if the user is feeling stressed, the definition unit can prioritize definitions related to stress reduction. For example, if the user inputs "I'm stressed today," the definition unit can provide definitions related to stress reduction based on that information. Furthermore, if the user is feeling relaxed, the definition unit can prioritize definitions related to long-term goals. For example, if the user inputs "I'm relaxing today," the definition unit can provide definitions related to long-term goals. Furthermore, if the user is in a hurry, the definition unit can prioritize definitions that can be implemented quickly. For example, if the user inputs "I'm in a hurry," the definition unit can provide definitions that can be implemented quickly. This allows the definitions of a good life to be prioritized based on the user's emotions, thereby providing the optimal definition for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the definition unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the definition unit may input user input data to the generation AI, which may infer emotions and determine the priority of definitions.

[0096] The definition unit can select the optimal definition method by taking into account the user's geographical location information. For example, if the user is in a specific area, the definition unit provides a definition by taking into account values ​​and goals related to that area. For example, if the user inputs "I'm currently in Tokyo," the definition unit provides a definition by taking into account values ​​and goals related to Tokyo. The definition unit can also provide a definition based on information related to places close to the user's current location. For example, if the user inputs "I'm currently looking for a nearby cafe," the definition unit provides a definition related to cafes based on that information. The definition unit can also select the optimal definition method based on the user's geographical location information. For example, if the user inputs "I'm currently traveling," the definition unit provides a definition by taking into account values ​​and goals related to travel. In this way, the optimal definition method for the user is selected by taking into account the user's geographical location information. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input the user's geographical location information data into the generation AI, which can then select the optimal definition method.

[0097] The definition unit can analyze the user's social media activity and suggest definition means at the time of definition. For example, the definition unit can suggest related definition means based on information shared by the user on social media. For example, if the user inputs "I've been posting a lot about health recently," the definition unit can suggest health-related definition means based on that information. The definition unit can also analyze the user's social media activity history and suggest related definition means. For example, if the user inputs "I've been posting a lot about travel recently," the definition unit can suggest travel-related definition means based on that information. Furthermore, the definition unit can suggest related definition means based on the activity of the user's friends on social media. For example, if the user inputs "My friends have been posting about fitness recently," the definition unit can suggest fitness-related definition means based on that information. In this way, related definition means can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the definition unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the definition unit can input the user's social media activity data into the generation AI, which can then suggest related definition means.

[0098] The definition unit can customize the definition method by reflecting the user's past feedback during definition. The definition unit adjusts the definition method based on, for example, feedback provided by the user in the past. For example, if the user provides feedback that "the previous definition method was complicated," the definition unit simplifies the definition method based on that information. The definition unit can also select the optimal definition method based on the user's past feedback. For example, if the user provides feedback that "a simple definition method was convenient," the definition unit prioritizes the simple definition method. The definition unit can also improve the definition method by reflecting the user's feedback. For example, if the user provides feedback that "there are too many definition steps," the definition unit reduces the number of steps based on that information. This allows the definition method to be customized by reflecting the user's past feedback, enabling more appropriate definitions. Some or all of the above-described processing in the definition unit may be performed using, or without, a generation AI. For example, the definition unit can input user feedback data into the generation AI, which then customizes the definition method.

[0099] The providing unit can estimate the user's emotions and adjust the method of providing specific steps based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide simple, easy-to-follow steps. For example, if the user inputs "I'm stressed today," the providing unit can provide simple steps based on that information. Furthermore, if the user is relaxed, the providing unit can provide steps with detailed explanations. For example, if the user inputs "I'm relaxed today," the providing unit can provide detailed steps. Furthermore, if the user is in a hurry, the providing unit can provide quickly executable steps. For example, if the user inputs "I'm in a hurry," the providing unit can provide quickly executable steps. Thus, by adjusting the method of providing specific steps according to the user's emotions, optimal steps are provided for the user. Emotion estimation is realized using an emotion estimation function, such as 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-described processing in the providing unit can be performed using, for example, a generation AI, or without a generation AI. For example, the providing unit can input the user's input data to the generating AI, which can then estimate the emotion and adjust the method of providing the step.

[0100] The providing unit can analyze the user's past behavioral history and select optimal steps when providing the data. The providing unit provides optimal steps based on, for example, steps the user has performed in the past. For example, if the user inputs "I have jogged in the past," the providing unit suggests jogging based on that information. The providing unit can also select effective steps from the user's past behavioral history. For example, if the user inputs "I have done yoga in the past," the providing unit suggests yoga based on that information. The providing unit can also analyze the user's past behavioral history and provide the most efficient steps. For example, if the user inputs "I have done strength training in the past," the providing unit suggests strength training based on that information. In this way, optimal steps are provided by analyzing the user's past behavioral history. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's behavioral history data into a generation AI, which can select optimal steps.

[0101] The providing unit can customize the step means based on the user's current living situation when providing the step information. For example, if the user inputs the user's current living situation, the providing unit provides the optimal step based on that information. For example, if the user inputs "I am currently looking for a job," the providing unit provides work-related steps based on that information. The providing unit can also customize the steps according to the user's living situation and provide the easiest method for the user to perform. For example, if the user inputs "My home environment has changed," the providing unit provides home-related steps based on that information. Furthermore, the providing unit can flexibly adjust the steps taking into account the user's current living situation. For example, if the user inputs "I am currently in poor health," the providing unit provides health-related steps based on that information. In this way, by customizing the step means based on the user's current living situation, the optimal step for the user is provided. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's living situation data into the generation AI, which can then customize the steps.

[0102] The providing unit can improve the step method by reflecting user feedback when providing the step method. The providing unit, for example, adjusts the step method based on feedback provided by the user in the past. For example, if the user provides feedback that "the previous step method was complicated," the providing unit simplifies the step method based on that information. The providing unit can also select the optimal step method based on the user's past feedback. For example, if the user provides feedback that "simple step methods were convenient," the providing unit prioritizes simple step methods. The providing unit can also improve the step method by reflecting user feedback. For example, if the user provides feedback that "there are too many step procedures," the providing unit reduces the number of procedures based on that information. In this way, the step method is improved by reflecting the user's past feedback, and more appropriate steps are provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into a generation AI, which can then improve the step method.

[0103] The providing unit can estimate the user's emotions and determine the priority of steps based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize steps related to stress reduction. For example, if the user inputs "I'm stressed today," the providing unit can provide steps related to stress reduction based on that information. Furthermore, if the user is relaxed, the providing unit can prioritize steps related to long-term goals. For example, if the user inputs "I'm relaxed today," the providing unit can provide steps related to long-term goals. Furthermore, if the user is in a hurry, the providing unit can prioritize steps that can be performed quickly. For example, if the user inputs "I'm in a hurry," the providing unit can provide steps that can be performed quickly. Thus, by prioritizing steps according to the user's emotions, optimal steps are provided for the user. 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-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the providing unit may input user input data to the generation AI, which may then estimate emotions and determine the priority of steps.

[0104] The providing unit can select optimal steps by taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides steps related to that area. For example, if the user inputs "I'm currently in Tokyo," the providing unit provides steps related to Tokyo. The providing unit can also provide steps related to locations close to the user's current location. For example, if the user inputs "I'm currently looking for a nearby cafe," the providing unit provides steps related to cafes based on that information. The providing unit can also select optimal steps based on the user's geographical location information. For example, if the user inputs "I'm currently traveling," the providing unit provides steps related to travel. In this way, optimal steps for the user are provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's geographical location information data into the generation AI, which then selects the optimal steps.

[0105] The providing unit can analyze the user's social media activity and suggest step measures at the time of providing the information. The providing unit can suggest relevant step measures based on, for example, information shared by the user on social media. For example, if the user inputs, "I've been posting a lot about health recently," the providing unit can suggest health-related step measures based on that information. The providing unit can also analyze the user's social media activity history and suggest relevant step measures. For example, if the user inputs, "I've been posting a lot about travel recently," the providing unit can suggest travel-related step measures based on that information. The providing unit can also suggest relevant step measures based on the activity of the user's friends on social media. For example, if the user inputs, "My friend has been posting a lot about fitness recently," the providing unit can suggest fitness-related step measures based on that information. In this way, relevant step measures can be suggested by analyzing the user's social media activity. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the providing unit can input the user's social media activity data into a generation AI, which can then suggest relevant step measures.

[0106] When providing the step method, the providing unit can customize the step method by reflecting the user's past feedback. The providing unit, for example, adjusts the step method based on feedback provided by the user in the past. For example, if the user provides feedback that "the previous step method was complicated," the providing unit simplifies the step method based on that information. The providing unit can also select the optimal step method based on the user's past feedback. For example, if the user provides feedback that "simple step methods were convenient," the providing unit prioritizes simple step methods. The providing unit can also improve the step method by reflecting the user's feedback. For example, if the user provides feedback that "there are too many step procedures," the providing unit reduces the number of procedures based on that information. In this way, the step method is customized by reflecting the user's past feedback, and more appropriate steps are provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI, which can then customize the step method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, visualization unit, definition unit, and provision 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 is realized by the control unit 46A of the smart device 14 and receives information from the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the state of the user's life using a generative AI. The definition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and defines a good life for the user. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides specific steps. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, visualization unit, definition unit, and provision 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 is realized by the control unit 46A of the smart glasses 214 and receives information from the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the state of the user's life using a generative AI. The definition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and defines a good life for the user. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides specific steps. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, visualization unit, definition unit, and provision 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 is realized by the control unit 46A of the headset type terminal 314 and receives information from the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the state of the user's life using a generative AI. The definition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and defines a good life for the user. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides specific steps. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, visualization unit, definition unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives information from the user. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the state of the user's life using a generative AI. The definition unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and defines a good life for the user. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides specific steps.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's input data and estimate the user's interests and concerns. For example, if the user inputs "I've been interested in cooking recently," the reception unit can preferentially accept information related to cooking based on that information. Also, if the user inputs "I've been interested in fitness recently," the reception unit can preferentially accept information related to fitness. Furthermore, the reception unit can filter related information based on the user's interests and concerns. For example, if the user inputs "I've not been interested in traveling recently," the reception unit filters information related to travel. In this way, by accepting information based on the user's interests and concerns, more relevant information can be provided.

[0109] The visualization unit can estimate the user's emotions and adjust the colors of the visualization based on the estimated user's emotions. For example, if the user is feeling stressed, the visualization unit displays a graph in calm colors. For example, if the user inputs "I'm stressed today," the visualization unit displays a graph in calm colors like blue and green based on that information. Also, if the user is relaxed, the visualization unit can display a graph in bright colors. For example, if the user inputs "I'm relaxed today," the visualization unit displays a graph in bright yellow and orange colors. Furthermore, if the user is excited, the visualization unit can display a graph in vibrant colors. For example, if the user inputs "I'm excited today," the visualization unit displays a graph in vibrant red and pink colors. This allows for more appropriate information display by adjusting the colors of the visualization according to the user's emotions.

[0110] The definition unit can estimate the user's emotions and adjust the way the definition of a good life is expressed based on the estimated user emotions. For example, if the user is feeling stressed, a simple and highly visible definition method is provided. For example, if the user inputs "I'm stressed today," the definition unit provides a simple definition method based on that information. Also, if the user is relaxed, a definition method including more detailed information can be provided. For example, if the user inputs "I'm relaxed today," the definition unit provides a detailed definition method. Furthermore, if the user is excited, a definition method with a visually stimulating effect can be provided. For example, if the user inputs "I'm excited today," the definition unit provides a definition method with a visually stimulating effect. This allows the definition method of a good life to be adjusted according to the user's emotions, enabling the user to find the optimal definition.

[0111] The providing unit can estimate the user's emotions and adjust the method of providing specific steps based on the estimated user's emotions. For example, if the user is feeling stressed, simple and easy-to-follow steps are provided. For example, if the user inputs "I'm stressed today," the providing unit provides simple steps based on that information. Also, if the user is relaxed, steps including detailed explanations can be provided. For example, if the user inputs "I'm relaxed today," the providing unit provides detailed steps. Furthermore, if the user is in a hurry, steps that can be quickly implemented can be provided. For example, if the user inputs "I'm in a hurry," the providing unit provides steps that can be quickly implemented. In this way, by adjusting the method of providing specific steps according to the user's emotions, optimal steps can be provided for the user.

[0112] The providing unit can estimate the user's emotions and determine the priority of steps based on the estimated user's emotions. For example, if the user is feeling stressed, steps related to stress reduction are provided preferentially. For example, if the user inputs "I'm stressed today," the providing unit provides steps related to stress reduction based on that information. Furthermore, if the user is relaxed, steps related to long-term goals can be provided preferentially. For example, if the user inputs "I'm relaxing today," the providing unit provides steps related to long-term goals. Furthermore, if the user is in a hurry, steps that can be performed quickly can be provided preferentially. For example, if the user inputs "I'm in a hurry," the providing unit provides steps that can be performed quickly. In this way, by determining the priority of steps according to the user's emotions, the optimal steps for the user are provided.

[0113] The reception unit can analyze the user's past behavioral history and select the most appropriate timing to receive information. For example, based on the user's past behavioral history during a specific time period, the reception unit can receive information during that time period. For example, if the user previously exercised every morning at 8:00, the reception unit can receive information during that time period. The reception unit can also analyze how frequently the user has performed a behavior in the past and receive information at the optimal timing. For example, if the user exercised three times a week, the reception unit can receive information based on that frequency. Furthermore, the reception unit can receive information on a specific day of the week based on the user's past behavioral history. For example, if the user exercised every Monday, the reception unit can receive information on that day of the week. In this way, by analyzing the user's past behavioral history, the reception unit can receive information at the optimal timing.

[0114] When visualizing information, the visualization unit can adjust the level of detail of the visualization based on the importance of the information. For example, important information is displayed in detail, and other information is displayed in a simplified manner. For example, if the user's health data is important, the visualization unit displays the data in detail. The visualization unit can also display information in different colors or sizes depending on the importance of the information. For example, important information is displayed in red, and other information is displayed in blue. Furthermore, the visualization unit can prioritize the display of information with high importance and add detailed explanations. For example, a detailed explanation is displayed for important information in a pop-up. In this way, important information can be displayed in detail by adjusting the level of detail of the visualization based on the importance of the information.

[0115] The definition unit can adjust the level of detail of the definition based on the user's values ​​and goals when defining the definition. For example, it provides a detailed definition based on the user's values. For example, if the user values ​​"health," the definition unit provides a detailed definition related to health. The definition unit can also provide a specific definition based on the user's goals. For example, if the user's goal is "career success," the definition unit provides a specific definition related to career. Furthermore, the definition unit can select the optimal definition method based on the user's values ​​and goals. For example, if the user inputs "I want to cherish time with my family," the definition unit provides a definition related to family. In this way, by adjusting the level of detail of the definition based on the user's values ​​and goals, it is possible to create a definition that is optimal for the user.

[0116] When providing the step information, the providing unit can analyze the user's past behavioral history and select the optimal step. For example, the optimal step is provided based on the steps the user has performed in the past. For example, if the user inputs "I have jogged in the past," the providing unit suggests jogging based on that information. The providing unit can also select effective steps from the user's past behavioral history. For example, if the user inputs "I have done yoga in the past," the providing unit suggests yoga based on that information. Furthermore, the providing unit can analyze the user's past behavioral history and provide the most efficient step. For example, if the user inputs "I have done strength training in the past," the providing unit suggests strength training based on that information. In this way, the optimal step is provided by analyzing the user's past behavioral history.

[0117] When providing the step method, the providing unit can improve the step method by reflecting user feedback. For example, the step method is adjusted based on feedback provided by the user in the past. For example, if the user provides feedback that "the previous step method was complicated," the providing unit simplifies the step method based on that information. The providing unit can also select the optimal step method based on the user's past feedback. For example, if the user provides feedback that "simple step methods were convenient," the providing unit prioritizes simple step methods. Furthermore, the providing unit can also improve the step method by reflecting user feedback. For example, if the user provides feedback that "there are too many step procedures," the providing unit reduces the number of procedures based on that information. In this way, the step method is improved by reflecting the user's past feedback, and more appropriate steps are provided.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives information from the user. Information from the user includes emotions, goals, values, etc. The reception unit can receive text data, voice input, and image data entered by the user. For example, it analyzes the text data and extracts emotions and goals. In the case of voice input, it converts the voice input into text data using voice recognition technology and then analyzes it. In the case of image data, it extracts information using image recognition technology. Step 2: The visualization unit uses generation AI to visualize the state of the user's life based on the information received by the reception unit. Visualization is performed using graphs, charts, dashboards, and interactive graphs. For example, the user's emotional data can be displayed over time to visually show changes in emotions. When using a dashboard, multiple graphs and charts can be displayed in combination to allow the user to grasp the overall situation. When using an interactive graph, the user can click on a specific data point to display detailed information. Step 3: The definition unit uses generative AI to define a good life for the user based on the information visualized by the visualization unit. The definition is based on the user's values ​​and goals. For example, the definition may be proposed based on an analysis of the user's values ​​and goals. The user can also modify the proposed definition, and the definition can be updated based on user feedback. Step 4: The provision part provides specific steps to achieve the good life defined by the definition part. The steps are based on daily action plans and goal setting. For example, it may suggest exercising a little bit every day or learning a new skill. When providing a specific action plan, it also sets weekly goals and manages progress. The processing in the provision part may be performed using a generation AI, which can suggest optimal steps.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 information from a user; a visualization unit that visualizes the user's life state based on the information received by the reception unit; a definition unit that defines an ideal life for a user based on the information visualized by the visualization unit; a provision unit that provides specific steps for realizing the ideal life defined by the definition unit. A system characterized by:

2. The reception unit Accept information about the user's emotions, goals, and values 2. The system of claim 1.

3. The visualization unit Displaying user emotions, goals, and values ​​in graphs and charts 2. The system of claim 1.

4. The definition section Suggest a definition of a good life based on the user's values ​​and goals 2. The system of claim 1.

5. The providing unit Suggesting that users exercise a little each day or learn a new skill 2. The system of claim 1.

6. The providing unit Providing specific action plans based on the user's definition of a good life 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the way information is received based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past conversation history and select the most appropriate timing for receiving information 2. The system of claim 1.

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