System

Abstract

An object of the system according to the embodiment is to provide a continuous motivation by converting a user's dream or desire into a specific action plan.SOLUTION: A system includes a dream or desire input part, a dream or desire analysis part, a guideline generation part, and a message generation part. The dream or desire input unit inputs the user's dream or desire. The dream and desire analysis component analyzes the dreams and desires inputted by the dream and desire input component. The guideline generation unit generates a step-by-step guideline based on the result analyzed by the dream and desire analysis unit. A message generation part generates a motivational message on the basis of a result analyzed by the dream and desire analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to convert users' dreams and hopes into concrete action plans and provide sustained motivation.

[0005] The system according to the embodiment aims to convert the dreams and hopes of the user into a concrete action plan and provide sustained motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a dream and hope input unit, a dream and hope analysis unit, a guideline generation unit, and a message generation unit. The dream and hope input unit inputs the user's dreams and hopes. The dream and hope analysis unit analyzes the dreams and hopes input by the dream and hope input unit. The guideline generation unit generates step-by-step guidelines based on the results of the analysis by the dream and hope analysis unit. The message generation unit generates motivational messages based on the results of the analysis by the dream and hope analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert the dreams and hopes of the user into a concrete action plan and provide sustained motivation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​motivation coaching system according to an embodiment of the present invention converts a user's dreams and goals into words, and the generation AI provides inspiration and a concrete action plan, thereby providing powerful support for the user to realize their dreams and goals.

[0029] The AI ​​motivation coaching system according to the embodiment includes a dream / hope input unit, a dream / hope analysis unit, a guideline generation unit, and a message generation unit. The dream / hope input unit inputs the user's dreams and hopes. For example, the user can input a specific goal such as "I want to be a writer." The dream / hope input unit uses the information input in text format as prompts for the generation AI. The dream / hope analysis unit analyzes the dreams and hopes input by the input unit. For example, the generation AI identifies the skills and steps necessary to achieve the user's goal. The generation AI performs the analysis using a pre-finished model. Based on the results of the analysis by the analysis unit, the guideline generation unit generates specific step-by-step guidelines for the user to achieve their goal. For example, it provides a specific action plan such as "Write 500 characters every day," "Attend a writer's workshop," and "Send your manuscript to a publisher." The message generation unit generates messages to motivate the user based on the results of the analysis by the analysis unit. For example, it provides encouraging words such as "You can do it," "Take one step at a time," and "Keep trying without fear of failure." As a result, the AI ​​motivation coaching system according to the embodiment can support users in achieving their goals by converting their dreams and hopes into concrete action plans and motivational messages.

[0030] The input unit for dreams and hopes can be input in a natural conversational style using voice input. For example, when a user inputs their dreams and hopes, the input unit for dreams and hopes uses voice recognition technology to build a system that allows them to input in a natural conversational style. For example, if a user says, "I want to be a writer," this is converted into text. This allows the user to input their dreams and hopes in a natural conversational style.

[0031] The input unit for dreams and hopes can refer to the user's past input history and automatically suggest similar dreams and hopes. For example, the input unit for dreams and hopes can save the user's past input history in a database and build a system that automatically suggests similar dreams and hopes. For example, if a user previously input "I want to be a writer," the system can suggest "Write a new book." This makes it possible to suggest similar dreams and hopes based on the user's past input history.

[0032] The dream and hope input section can allow the user to attach images and videos when inputting their dreams and hopes. The dream and hope input section adds a function that allows the user to attach images and videos when inputting their dreams and hopes. For example, for a goal such as "I want to be a writer," the user can attach images of the work they have written. This allows the user to attach images and videos when inputting their dreams and hopes.

[0033] The input unit for dreams and hopes supports input in different languages ​​and can accommodate international users. The input unit for dreams and hopes, for example, provides a multilingual interface to support input in different languages. For example, it allows dreams and hopes to be input in multiple languages, such as English, French, and Chinese. This supports input in different languages ​​and can accommodate international users.

[0034] The dream and hope analysis unit can refer to the user's past experiences of success and failure to perform a more personalized analysis. For example, when the generation AI analyzes dreams and hopes, the dream and hope analysis unit can refer to the user's past experiences of success and failure from a database to perform a personalized analysis. For example, the analysis can be based on data from projects that have been successful in the past. This allows the user to refer to their past experiences of success and failure to perform a more personalized analysis.

[0035] The dream and hope analysis part can refer to the latest trends and news in related industries and provide realistic advice. For example, when the generative AI analyzes dreams and hopes, the dream and hope analysis part automatically collects the latest trends and news in related industries and builds a system that provides realistic advice. For example, it reflects the latest technological trends. This allows it to refer to the latest trends and news in related industries and provide realistic advice.

[0036] The dream and hope analysis part can incorporate the perspectives of different cultures and regions and conduct analysis from a global perspective. For example, when the generative AI analyzes dreams and hopes, the dream and hope analysis part builds a multilingual database to incorporate the perspectives of different cultures and regions. For example, it conducts analysis based on the culture and customs of each country. This allows it to incorporate the perspectives of different cultures and regions and conduct analysis from a global perspective.

[0037] The dream and hope analysis unit can compare dreams and hopes with similar dreams and hopes of other users and extract common patterns of success. For example, when the generation AI analyzes dreams and hopes, the dream and hope analysis unit builds a system that compares them with similar dreams and hopes of other users. For example, it extracts the success patterns of users who have the same goals. This makes it possible to compare dreams and hopes with similar dreams and hopes of other users and extract common patterns of success.

[0038] The guideline generation unit can customize the guideline to suit the user's schedule and lifestyle. For example, when the generation AI generates guidelines, the guideline generation unit builds a system that customizes the guideline to suit the user's schedule and lifestyle. For example, it suggests an optimal schedule based on the user's calendar information. This allows customization to suit the user's schedule and lifestyle.

[0039] The guideline generation unit can monitor the user's progress in real time and update the guidelines as necessary. The guideline generation unit, for example, builds a system in which the generation AI monitors the user's progress in real time and updates the guidelines as necessary. For example, when the user has achieved part of a goal, the next step is suggested. This makes it possible to monitor the user's progress in real time and update the guidelines as necessary.

[0040] The guideline generation unit can add a collaboration function that allows users to cooperate with other users to achieve their goals. For example, when the generation AI generates a guideline, the guideline generation unit builds a system that adds a collaboration function that allows users to cooperate with other users to achieve their goals. For example, users with the same goal form a team. This makes it possible to add a collaboration function that allows users to cooperate with other users to achieve their goals.

[0041] The message generation unit can provide personalized messages by referring to the user's past success experiences and positive feedback. For example, when the generation AI generates a motivational message, the message generation unit builds a system that provides personalized messages by referring to the user's past success experiences and positive feedback from a database. For example, a message is generated based on data from successful projects in the past. This makes it possible to provide personalized messages by referring to the user's past success experiences and positive feedback.

[0042] The message generation unit can incorporate different cultural and regional perspectives to provide messages from a global perspective. For example, when the generation AI generates motivational messages, the message generation unit builds a multilingual database to incorporate different cultural and regional perspectives. For example, it provides messages based on the culture and customs of each country. This allows it to incorporate different cultural and regional perspectives to provide messages from a global perspective.

[0043] The message generation unit can share the success stories and positive feedback of other users. For example, when the generation AI generates a motivational message, the message generation unit builds a system that shares the success stories and positive feedback of other users. For example, it can introduce an episode of a successful user. This allows other users to share their success stories and positive feedback.

[0044] The inspiration providing unit can provide personalized inspiration by referring to the user's past successful experiences and positive feedback. For example, when the generation AI provides inspiration, the inspiration providing unit references the user's past successful experiences and positive feedback from a database to build a system that provides personalized inspiration. For example, inspiration is provided based on data on projects that have been successful in the past. This makes it possible to provide personalized inspiration by referring to the user's past successful experiences and positive feedback.

[0045] The inspiration providing unit can incorporate perspectives from different cultures and regions to provide inspiration from a global perspective. For example, the inspiration providing unit builds a multilingual database so that when the generative AI provides inspiration, it can incorporate perspectives from different cultures and regions. For example, it can provide inspiration based on the culture and customs of each country. This allows it to incorporate perspectives from different cultures and regions to provide inspiration from a global perspective.

[0046] The inspiration providing unit can share the success stories and positive feedback of other users. For example, when the generation AI provides inspiration, the inspiration providing unit builds a system that shares the success stories and positive feedback of other users. For example, it can introduce stories of successful users. This allows other users to share their success stories and positive feedback.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The AI ​​Motivation Coach System can also include a health management unit that monitors the user's health status. For example, it can track the user's heart rate and sleep patterns and provide an optimal action plan based on the user's health status. This allows the system to support the user in achieving their goals while taking their health status into consideration. The health management unit can also provide advice on how to relax if the user is feeling stressed. Furthermore, the health management unit can incorporate the user's diet and exercise records and generate guidelines to promote a balanced lifestyle.

[0049] The AI ​​Motivation Coach System can also include a hobby analysis module that analyzes the user's hobbies and interests. For example, it can store the user's past activities and topics of interest in a database and suggest new hobbies and activities based on that information. This allows the user to have fun while working towards achieving their goals. The hobby analysis module can also introduce events and communities related to the user's interests. Furthermore, the hobby analysis module can suggest online courses and workshops for the user to learn new skills.

[0050] The AI ​​motivation coaching system may further include a social support unit that utilizes the user's social network. For example, the system may collect messages of support from the user's friends and family and provide words of encouragement during the goal achievement process. This allows the user to move forward toward their goal without feeling alone. The social support unit may also provide a platform for the user to connect with other users who share the same goal. Furthermore, the social support unit may provide a function for the user to share their progress toward achieving their goal and receive feedback.

[0051] The AI ​​Motivation Coach System can further include a learning style analysis unit that analyzes a user's learning style. For example, if the user is a visual learner, the system can provide an action plan that makes heavy use of visual content, allowing the user to work on their goals in a way that suits them. The learning style analysis unit can also suggest audio guides or podcasts if the user is an auditory learner. Furthermore, the learning style analysis unit can suggest practical workshops or hands-on activities if the user is an experiential learner.

[0052] The AI ​​Motivation Coach System can also be equipped with a time management section to support users in managing their time. For example, it can analyze the user's schedule and suggest optimal time allocation, allowing the user to work on their goals efficiently. The time management section can also suggest the Pomodoro technique to help the user maintain focus. Furthermore, the time management section can notify the user when to take a break, supporting balanced time management.

[0053] The AI ​​motivation coaching system may further include a learning progress tracking unit that tracks the user's learning progress. For example, it may record what the user has learned and visualize the progress. This allows the user to work toward their goals while feeling their own growth. The learning progress tracking unit may also suggest the user's next steps based on their learning progress. Furthermore, the learning progress tracking unit may provide appropriate resources and support if the user encounters difficulties during the learning process.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The input section for dreams and hopes is where the user enters their dreams and hopes. For example, a user can enter a specific goal such as "I want to be a writer." The input section for dreams and hopes also uses the information entered in text format as a prompt for the generation AI. Step 2: In the dream and hope analysis section, the generation AI analyzes the dreams and hopes input by the input section. For example, the generation AI identifies the skills and steps required to achieve the user's goals. The generation AI performs the analysis using a pre-fine-tuned model. Step 3: The guideline generator generates specific step-by-step guidelines for the user to achieve their goals based on the results of the analysis by the analyzer, such as "write 500 characters every day," "attend a writer's workshop," and "send your manuscript to a publisher." Step 4: The message generator generates a message to motivate the user based on the results of the analysis by the analyzer. For example, it provides encouraging words such as "You can do it," "Take one step at a time," and "Keep trying without being afraid of failure."

[0056] (Example 2) The AI ​​motivation coaching system according to an embodiment of the present invention converts a user's dreams and goals into words, and the generation AI provides inspiration and a concrete action plan, thereby providing powerful support for the user to realize their dreams and goals.

[0057] The AI ​​motivation coaching system according to the embodiment includes a dream / hope input unit, a dream / hope analysis unit, a guideline generation unit, and a message generation unit. The dream / hope input unit inputs the user's dreams and hopes. For example, the user can input a specific goal such as "I want to be a writer." The dream / hope input unit uses the information input in text format as prompts for the generation AI. The dream / hope analysis unit analyzes the dreams and hopes input by the input unit. For example, the generation AI identifies the skills and steps necessary to achieve the user's goal. The generation AI performs the analysis using a pre-finished model. Based on the results of the analysis by the analysis unit, the guideline generation unit generates specific step-by-step guidelines for the user to achieve their goal. For example, it provides a specific action plan such as "Write 500 characters every day," "Attend a writer's workshop," and "Send your manuscript to a publisher." The message generation unit generates messages to motivate the user based on the results of the analysis by the analysis unit. For example, it provides encouraging words such as "You can do it," "Take one step at a time," and "Keep trying without fear of failure." As a result, the AI ​​motivation coaching system according to the embodiment can support users in achieving their goals by converting their dreams and hopes into concrete action plans and motivational messages.

[0058] The input unit for dreams and hopes can be input in a natural conversational style using voice input. For example, when a user inputs their dreams and hopes, the input unit for dreams and hopes uses voice recognition technology to build a system that allows them to input in a natural conversational style. For example, if a user says, "I want to be a writer," this is converted into text. This allows the user to input their dreams and hopes in a natural conversational style.

[0059] The input unit for dreams and hopes can refer to the user's past input history and automatically suggest similar dreams and hopes. For example, the input unit for dreams and hopes can save the user's past input history in a database and build a system that automatically suggests similar dreams and hopes. For example, if a user previously input "I want to be a writer," the system can suggest "Write a new book." This makes it possible to suggest similar dreams and hopes based on the user's past input history.

[0060] The dream and hope input unit can use the emotion estimation function to analyze the emotions of the user when inputting their dreams and hopes in real time, and provide feedback to elicit positive emotions. The dream and hope input unit can, for example, use the emotion estimation function to build a system that analyzes the emotions of the user when inputting their dreams and hopes in real time. For example, it can analyze the user's facial expressions and tone of voice and calculate an emotion score. This makes it possible to analyze the user's emotions in real time and provide feedback to elicit positive emotions.

[0061] The dream and hope input section can allow the user to attach images and videos when inputting their dreams and hopes. The dream and hope input section adds a function that allows the user to attach images and videos when inputting their dreams and hopes. For example, for a goal such as "I want to be a writer," the user can attach images of the work they have written. This allows the user to attach images and videos when inputting their dreams and hopes.

[0062] The input unit for dreams and hopes supports input in different languages ​​and can accommodate international users. The input unit for dreams and hopes, for example, provides a multilingual interface to support input in different languages. For example, it allows dreams and hopes to be input in multiple languages, such as English, French, and Chinese. This supports input in different languages ​​and can accommodate international users.

[0063] The dream and hope input unit uses the emotion estimation function to collect other users' emotional reactions to the dreams and hopes input by the user, and can suggest dreams and hopes that are likely to gain empathy. The dream and hope input unit, for example, uses the emotion estimation function to build a system that collects other users' emotional reactions to the dreams and hopes input by the user. For example, it identifies dreams and hopes that other users feel are "wonderful." This allows it to collect other users' emotional reactions to the dreams and hopes input by the user, and suggest dreams and hopes that are likely to gain empathy.

[0064] The dream and hope analysis unit can refer to the user's past experiences of success and failure to perform a more personalized analysis. For example, when the generation AI analyzes dreams and hopes, the dream and hope analysis unit can refer to the user's past experiences of success and failure from a database to perform a personalized analysis. For example, the analysis can be based on data from projects that have been successful in the past. This allows the user to refer to their past experiences of success and failure to perform a more personalized analysis.

[0065] The dream and hope analysis part can refer to the latest trends and news in related industries and provide realistic advice. For example, when the generative AI analyzes dreams and hopes, the dream and hope analysis part automatically collects the latest trends and news in related industries and builds a system that provides realistic advice. For example, it reflects the latest technological trends. This allows it to refer to the latest trends and news in related industries and provide realistic advice.

[0066] The dream and hope analysis unit uses the emotion estimation function to evaluate the emotional value of the user's dreams and hopes, and can emphasize emotionally positive elements. The dream and hope analysis unit uses, for example, the emotion estimation function to build a system that evaluates the emotional value of the user's dreams and hopes. For example, when a user inputs "I want to be a writer," it calculates an emotion score. This makes it possible to evaluate the emotional value of the user's dreams and hopes and emphasize positive elements.

[0067] The dream and hope analysis part can incorporate the perspectives of different cultures and regions and conduct analysis from a global perspective. For example, when the generative AI analyzes dreams and hopes, the dream and hope analysis part builds a multilingual database to incorporate the perspectives of different cultures and regions. For example, it conducts analysis based on the culture and customs of each country. This allows it to incorporate the perspectives of different cultures and regions and conduct analysis from a global perspective.

[0068] The dream and hope analysis unit can compare dreams and hopes with similar dreams and hopes of other users and extract common patterns of success. For example, when the generation AI analyzes dreams and hopes, the dream and hope analysis unit builds a system that compares them with similar dreams and hopes of other users. For example, it extracts the success patterns of users who have the same goals. This makes it possible to compare dreams and hopes with similar dreams and hopes of other users and extract common patterns of success.

[0069] The dream and hope analysis unit can use the emotion estimation function to analyze the emotional reactions of other users to the user's dreams and hopes, and identify elements that are likely to be emotionally relatable. The dream and hope analysis unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of other users to the user's dreams and hopes. For example, it identifies elements that other users find "wonderful." This makes it possible to analyze the emotional reactions of other users to the user's dreams and hopes, and identify elements that are likely to be relatable.

[0070] The guideline generation unit can customize the guideline to suit the user's schedule and lifestyle. For example, when the generation AI generates guidelines, the guideline generation unit builds a system that customizes the guideline to suit the user's schedule and lifestyle. For example, it suggests an optimal schedule based on the user's calendar information. This allows customization to suit the user's schedule and lifestyle.

[0071] The guideline generation unit can monitor the user's progress in real time and update the guidelines as necessary. The guideline generation unit, for example, builds a system in which the generation AI monitors the user's progress in real time and updates the guidelines as necessary. For example, when the user has achieved part of a goal, the next step is suggested. This makes it possible to monitor the user's progress in real time and update the guidelines as necessary.

[0072] The guideline generation unit can use the emotion estimation function to analyze the emotions of the user when executing the guideline and provide feedback to maintain motivation. The guideline generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of the user when executing the guideline in real time. For example, the guideline generation unit analyzes the user's facial expressions and tone of voice to calculate an emotion score. This makes it possible to analyze the emotions of the user when executing the guideline and provide feedback to maintain motivation.

[0073] The guideline generation unit can add a collaboration function that allows users to cooperate with other users to achieve their goals. For example, when the generation AI generates a guideline, the guideline generation unit builds a system that adds a collaboration function that allows users to cooperate with other users to achieve their goals. For example, users with the same goal form a team. This makes it possible to add a collaboration function that allows users to cooperate with other users to achieve their goals.

[0074] The guideline generation unit uses the emotion estimation function to collect emotional responses when a user executes a guideline, and can provide motivational messages at optimal timing. The guideline generation unit, for example, uses the emotion estimation function to build a system that collects emotional responses in real time when a user executes a guideline. For example, the system analyzes the user's facial expressions and tone of voice to calculate an emotion score. This allows the system to collect emotional responses when a user executes a guideline and provide motivational messages at optimal timing.

[0075] The message generation unit can provide personalized messages by referring to the user's past success experiences and positive feedback. For example, when the generation AI generates a motivational message, the message generation unit builds a system that provides personalized messages by referring to the user's past success experiences and positive feedback from a database. For example, a message is generated based on data from successful projects in the past. This makes it possible to provide personalized messages by referring to the user's past success experiences and positive feedback.

[0076] The message generation unit can analyze the user's current emotional state in real time and provide a message that corresponds to that. For example, when the generation AI generates a motivational message, the message generation unit builds a system that analyzes the user's current emotional state in real time. For example, it analyzes the user's facial expressions and tone of voice and calculates an emotional score. This allows the user's current emotional state to be analyzed in real time and a message that corresponds to that can be provided.

[0077] The message generation unit can use the emotion estimation function to identify a message pattern that evokes the most positive emotion in the user and generate a message based on that. The message generation unit, for example, uses the emotion estimation function to build a system that identifies a message pattern that evokes the most positive emotion in the user. For example, the message generation unit analyzes the user's facial expression and tone of voice and calculates an emotion score. This allows the message generation unit to identify a message pattern that evokes the most positive emotion in the user and generate a message based on that.

[0078] The message generation unit can incorporate different cultural and regional perspectives to provide messages from a global perspective. For example, when the generation AI generates motivational messages, the message generation unit builds a multilingual database to incorporate different cultural and regional perspectives. For example, it provides messages based on the culture and customs of each country. This allows it to incorporate different cultural and regional perspectives to provide messages from a global perspective.

[0079] The message generation unit can share the success stories and positive feedback of other users. For example, when the generation AI generates a motivational message, the message generation unit builds a system that shares the success stories and positive feedback of other users. For example, it can introduce an episode of a successful user. This allows other users to share their success stories and positive feedback.

[0080] The message generation unit can use the emotion estimation function to identify a message pattern that the user most empathizes with and generate a message based on that. The message generation unit, for example, uses the emotion estimation function to build a system that identifies a message pattern that the user most empathizes with. For example, the message generation unit analyzes the user's facial expression and tone of voice and calculates an emotion score. This allows the message pattern that the user most empathizes with to be identified and a message to be generated based on that.

[0081] The inspiration providing unit can provide personalized inspiration by referring to the user's past successful experiences and positive feedback. For example, when the generation AI provides inspiration, the inspiration providing unit references the user's past successful experiences and positive feedback from a database to build a system that provides personalized inspiration. For example, inspiration is provided based on data on projects that have been successful in the past. This makes it possible to provide personalized inspiration by referring to the user's past successful experiences and positive feedback.

[0082] The inspiration providing unit can analyze the user's current emotional state in real time and provide inspiration accordingly. For example, the inspiration providing unit constructs a system that analyzes the user's current emotional state in real time when the generation AI provides inspiration. For example, it analyzes the user's facial expressions and voice tone and calculates an emotional score. This allows the user's current emotional state to be analyzed in real time and inspiration to be provided accordingly.

[0083] The inspiration providing unit can use the emotion estimation function to identify a pattern of inspiration that evokes the most positive emotion for the user and provide inspiration based on that. The inspiration providing unit, for example, uses the emotion estimation function to build a system that identifies a pattern of inspiration that evokes the most positive emotion for the user. For example, the inspiration providing unit analyzes the user's facial expression and voice tone to calculate an emotion score. This allows the inspiration providing unit to identify a pattern of inspiration that evokes the most positive emotion for the user and provide inspiration based on that.

[0084] The inspiration providing unit can incorporate perspectives from different cultures and regions to provide inspiration from a global perspective. For example, the inspiration providing unit builds a multilingual database so that when the generative AI provides inspiration, it can incorporate perspectives from different cultures and regions. For example, it can provide inspiration based on the culture and customs of each country. This allows it to incorporate perspectives from different cultures and regions to provide inspiration from a global perspective.

[0085] The inspiration providing unit can share the success stories and positive feedback of other users. For example, when the generation AI provides inspiration, the inspiration providing unit builds a system that shares the success stories and positive feedback of other users. For example, it can introduce stories of successful users. This allows other users to share their success stories and positive feedback.

[0086] The inspiration providing unit can use the emotion estimation function to identify a pattern of inspiration that the user most identifies with and provide inspiration based on that. The inspiration providing unit, for example, uses the emotion estimation function to build a system that identifies a pattern of inspiration that the user most identifies with. For example, it analyzes the user's facial expression and tone of voice and calculates an emotion score. This allows the inspiration providing unit to identify a pattern of inspiration that the user most identifies with and provide inspiration based on that.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The AI ​​Motivation Coach System can also include a health management unit that monitors the user's health status. For example, it can track the user's heart rate and sleep patterns and provide an optimal action plan based on the user's health status. This allows the system to support the user in achieving their goals while taking their health status into consideration. The health management unit can also provide advice on how to relax if the user is feeling stressed. Furthermore, the health management unit can incorporate the user's diet and exercise records and generate guidelines to promote a balanced lifestyle.

[0089] The AI ​​Motivation Coach System can also include a hobby analysis module that analyzes the user's hobbies and interests. For example, it can store the user's past activities and topics of interest in a database and suggest new hobbies and activities based on that information. This allows the user to have fun while working towards achieving their goals. The hobby analysis module can also introduce events and communities related to the user's interests. Furthermore, the hobby analysis module can suggest online courses and workshops for the user to learn new skills.

[0090] The AI ​​motivation coaching system may further include a social support unit that utilizes the user's social network. For example, the system may collect messages of support from the user's friends and family and provide words of encouragement during the goal achievement process. This allows the user to move forward toward their goal without feeling alone. The social support unit may also provide a platform for the user to connect with other users who share the same goal. Furthermore, the social support unit may provide a function for the user to share their progress toward achieving their goal and receive feedback.

[0091] The AI ​​motivation coaching system can further include a relaxation module that estimates the user's emotions and provides relaxation techniques based on the estimated emotions. For example, if the user is feeling stressed, the system can provide deep breathing or meditation guidance, allowing the user to relax and maintain positive emotions while working toward their goals. The relaxation module can also play relaxing music or natural sounds according to the user's emotional state. The relaxation module can also suggest aromatherapy to help the user relax.

[0092] The AI ​​motivation coaching system can further include a visual content unit that estimates the user's emotions and provides positive visual content based on the estimated emotions. For example, if the user is feeling down, images or videos displaying encouraging messages can be provided. This allows the user to receive visually positive stimuli and increase motivation. The visual content unit can also display inspirational artwork or landscape photos depending on the user's emotional state. The visual content unit can also provide nature videos that help the user relax.

[0093] The AI ​​Motivation Coach System can further include a learning style analysis unit that analyzes a user's learning style. For example, if the user is a visual learner, the system can provide an action plan that makes heavy use of visual content, allowing the user to work on their goals in a way that suits them. The learning style analysis unit can also suggest audio guides or podcasts if the user is an auditory learner. Furthermore, the learning style analysis unit can suggest practical workshops or hands-on activities if the user is an experiential learner.

[0094] The AI ​​motivation coaching system can further include a feedback unit that estimates the user's emotions and provides appropriate feedback based on the estimated emotions. For example, if the user feels anxious, the feedback unit can provide reassuring feedback, allowing the user to work toward their goals with peace of mind. The feedback unit can also provide positive feedback to further enhance the user's emotions if the user feels joyful. Furthermore, if the user feels tired, the feedback unit can provide feedback encouraging the user to take a rest.

[0095] The AI ​​Motivation Coach System can also be equipped with a time management section to support users in managing their time. For example, it can analyze the user's schedule and suggest optimal time allocation, allowing the user to work on their goals efficiently. The time management section can also suggest the Pomodoro technique to help the user maintain focus. Furthermore, the time management section can notify the user when to take a break, supporting balanced time management.

[0096] The AI ​​motivation coach system can further include a goal setting unit that estimates the user's emotions and supports appropriate goal setting based on the estimated emotions. For example, if the user is confident, the system can suggest challenging goals, allowing the user to maximize their abilities. The goal setting unit can also suggest achievable small goals if the user is feeling anxious. Furthermore, if the user is losing motivation, the goal setting unit can help the user set short-term goals and achieve a sense of accomplishment.

[0097] The AI ​​motivation coaching system may further include a learning progress tracking unit that tracks the user's learning progress. For example, it may record what the user has learned and visualize the progress. This allows the user to work toward their goals while feeling their own growth. The learning progress tracking unit may also suggest the user's next steps based on their learning progress. Furthermore, the learning progress tracking unit may provide appropriate resources and support if the user encounters difficulties during the learning process.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The input section for dreams and hopes is where the user enters their dreams and hopes. For example, a user can enter a specific goal such as "I want to be a writer." The input section for dreams and hopes also uses the information entered in text format as a prompt for the generation AI. Step 2: In the dream and hope analysis section, the generation AI analyzes the dreams and hopes input by the input section. For example, the generation AI identifies the skills and steps required to achieve the user's goals. The generation AI performs the analysis using a pre-fine-tuned model. Step 3: The guideline generator generates specific step-by-step guidelines for the user to achieve their goals based on the results of the analysis by the analyzer, such as "write 500 characters every day," "attend a writer's workshop," and "send your manuscript to a publisher." Step 4: The message generator generates a message to motivate the user based on the results of the analysis by the analyzer. For example, it provides encouraging words such as "You can do it," "Take one step at a time," and "Keep trying without being afraid of failure."

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The 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.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input section for inputting the user's dreams and hopes; a dream and hope analysis unit that analyzes the dreams and hopes input by the dream and hope input unit; a guideline generation unit that generates step-by-step guidelines based on the results of the analysis by the dream and hope analysis unit; a message generation unit that generates a motivational message based on the results of the analysis by the dream and hope analysis unit. A system characterized by:

2. The input section for dreams and hopes is Analyzes emotions in real time as users input their dreams and hopes, and provides feedback to elicit positive emotions 2. The system of claim 1.

3. The input section for dreams and hopes is Users can attach images and videos when entering their dreams and hopes.

2. The system of claim 1.

4. The dream and hope analysis section Referencing users' past successes and failures to conduct more personalized analysis 2. The system of claim 1.

5. The guideline generation unit Customize to fit your schedule and lifestyle 2. The system of claim 1.

6. The message generation unit Referencing users' past successes and positive feedback to deliver personalized messages 2. The system of claim 1.

7. The inspiration department is Analyzes the user's current emotional state in real time and provides inspiration accordingly 2. The system of claim 1.

8. The dream and hope analysis section Evaluate the emotional value of your users' dreams and aspirations and highlight the emotionally positive aspects.

2. The system of claim 1.

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