System

Abstract

An object of a system according to an embodiment is to collect high performer Tips and provide the Tips to a user in an optimal form.SOLUTION: A system includes a Tips collection part, an analysis part, a customization part, and an assist provision part. The Tips collection unit collects Tips of the high performer. The analysis unit analyzes the Tips collected by the Tips collection unit. The customization unit customizes the Tips analyzed by the analysis unit in accordance with a user. The assist providing unit provides the assist customized by the customization unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively collect tips from high performers and provide them to individual users in an optimal format.

[0005] The system according to the embodiment aims to collect tips from high performers and provide them to users in an optimal format. [Means for solving the problem]

[0006] The system according to the embodiment includes a tips collection unit, an analysis unit, a customization unit, and an assistance providing unit. The tips collection unit collects tips from high performers. The analysis unit analyzes the tips collected by the tips collection unit. The customization unit customizes the tips analyzed by the analysis unit to suit the user. The assistance providing unit provides assistance customized by the customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect tips from high performers and provide them to users in an optimal format. [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 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 system according to an embodiment of the present invention is a system that realizes the lifestyle of people who can continue to achieve high results while having a comfortable life. This system learns the tips practiced by the top 1% of people known as high performers and executes them through individually customized AI assistance. This allows the system to enable users to continue to achieve high results while having a comfortable life.

[0029] The system according to the embodiment includes a tips collection unit, an analysis unit, a customization unit, and an assistance provision unit. The tips collection unit collects tips from high performers. For example, it collects information from interviews, books, blogs, and other sources of information from successful businesspeople, athletes, and scholars. The analysis unit analyzes the collected tips. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the collected information and extract important tips. The generation AI can also use a multimodal generation AI to integrate and analyze data from multiple sources. The customization unit customizes the analyzed tips to suit the user. For example, it generates specific action plans and advice based on the user's lifestyle habits and goals. The generation AI provides optimal assistance based on the user's profile information. The assistance provision unit provides customized assistance. For example, it monitors the user's daily behavior and provides appropriate feedback. The generation AI analyzes the user's behavioral data and suggests areas for improvement and next steps. This allows the system to enable users to learn tips from high performers and receive individually customized assistance, thereby enabling them to continue achieving high results. For example, business people can create specific action plans to work more efficiently, and athletes can receive advice on maximizing the effectiveness of their training. Also, by monitoring daily actions and receiving appropriate feedback, it becomes easier to maintain motivation to achieve goals.

[0030] The tips collection unit can also collect information from private interviews and private notes of high performers. For example, the tips collection unit collects private interviews of high performers, and the generation AI analyzes their contents. For example, it can analyze private interviews of business people and extract the secrets of their success. The tips collection unit also collects private notes of high performers, and the generation AI analyzes their contents. For example, it can analyze handwritten notes or digital notebooks and extract important information. This allows for deeper insights.

[0031] The analysis unit can analyze the behavioral patterns of high performers and identify optimal behavior depending on the time of day and environmental conditions. For example, the analysis unit collects behavioral data of high performers, and the generation AI analyzes that data. For example, it analyzes a businessperson's daily schedule and identifies the optimal time of day. The analysis unit also analyzes the behavioral patterns of high performers and identifies optimal behavior depending on environmental conditions. For example, it suggests optimal behavior taking into account environmental conditions such as temperature, humidity, and noise level. This allows for efficient behavior by identifying optimal behavior.

[0032] The customization unit can analyze the user's past behavioral data and identify the most effective assistance method. For example, the customization unit collects the user's past behavioral data, and the generation AI analyzes that data. For example, it analyzes a business person's past schedule and identifies the optimal assistance method. The customization unit also suggests the optimal assistance method based on the user's behavioral data. For example, it identifies an effective assistance method based on past success stories. This makes it possible to identify the optimal assistance method based on past behavioral data.

[0033] The customization unit can collect the user's physiological data and provide optimal assistance based on that data. For example, the customization unit collects the user's physiological data, and the generation AI analyzes that data. For example, it analyzes the heart rate data of a business person and provides assistance with stress management. The customization unit also analyzes the user's sleep patterns and provides optimal assistance. For example, it suggests optimal rest times based on the sleep data. This makes it possible to provide optimal assistance based on the physiological data.

[0034] The assistance providing unit can analyze the user's behavioral data in real time and provide instantaneous feedback. For example, the assistance providing unit collects the user's behavioral data in real time, and the generation AI analyzes the data. For example, the assistance providing unit analyzes the work progress of a business person in real time and provides instantaneous feedback. The assistance providing unit also analyzes the behavioral data in real time and provides appropriate feedback. For example, it provides instantaneous advice to improve work efficiency. This allows for rapid improvement by providing feedback in real time.

[0035] The assistance providing unit can analyze user behavioral data over the long term and identify changes in behavioral patterns. For example, the assistance providing unit collects user behavioral data over the long term, and the generation AI analyzes that data. For example, the assistance providing unit analyzes the work patterns of business people over the long term and identifies changes in behavioral patterns. The assistance providing unit also proposes sustainable improvements based on the long-term behavioral data. For example, it identifies changes in behavioral patterns based on past data and suggests areas for improvement. This makes it possible to identify long-term changes in behavioral patterns and make sustainable improvements.

[0036] The assistance providing unit can analyze the user's past success experiences and identify ways to increase motivation based on them. For example, the assistance providing unit collects data on the user's past success experiences, and the generation AI analyzes the data. For example, it analyzes the past success stories of business people and identifies ways to increase motivation. The assistance providing unit also suggests ways to increase motivation based on past success experiences. For example, it provides specific advice based on success stories. This makes it possible to identify ways to increase motivation based on past success experiences.

[0037] The assistance providing unit can visualize the user's degree of goal achievement, allowing the user to feel a sense of accomplishment. For example, the assistance providing unit collects data on the user's degree of goal achievement, and the generation AI analyzes the data. For example, the assistance providing unit visualizes the degree of goal achievement of a business person, allowing the user to feel a sense of accomplishment. The assistance providing unit also displays the degree of goal achievement in graphs and charts, providing feedback to the user. For example, a dashboard can be used to display the degree of goal achievement in real time. This makes the degree of goal achievement visible, thereby enhancing the user's sense of accomplishment.

[0038] The assistance providing unit can analyze the user's learning history and provide an optimal learning plan. For example, the assistance providing unit collects the user's learning history data, and the generation AI analyzes that data. For example, the assistance providing unit analyzes a business person's past learning history and provides an optimal learning plan. The assistance providing unit also suggests an optimal learning plan based on the learning history. For example, it provides an effective learning plan based on past learning content and test results. This makes it possible to provide an optimal learning plan based on the learning history.

[0039] The assistance providing unit can provide a customized learning plan according to the user's learning style. For example, the assistance providing unit collects learning style data from the user and the generation AI analyzes the data. For example, the assistance providing unit analyzes the learning style of a business person and provides an optimal learning plan. The assistance providing unit also proposes a customized learning plan according to the learning style. For example, it provides the optimal learning method according to learning style, such as visual, auditory, or experiential. This makes it possible to provide a customized learning plan according to the learning style.

[0040] The assistance providing unit can compare the user's study plan with the plans of other users and provide a benchmark. For example, the assistance providing unit collects the user's study plan data, and the generation AI compares it with the data of other users. For example, the assistance providing unit compares the study plan of a business person with that of other users and provides a benchmark. The assistance providing unit also suggests areas for improvement in the user's study plan by comparing it with the plans of other users. For example, it suggests improvements to the study plan based on the average or highest value of other users. This makes it possible to improve the user's study plan by comparing it with other users.

[0041] The assistance providing unit can make the user's study plan accessible from different devices. For example, the assistance providing unit makes the user's study plan data accessible from different devices, and the generation AI analyzes the data. For example, the assistance providing unit makes a business person's study plan accessible from a tablet and a PC. The assistance providing unit also improves user convenience by supporting access from different devices. For example, the assistance providing unit makes it possible to access a study plan from multiple devices, such as a smartphone, tablet, and PC. This improves user convenience by making it accessible from different devices.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The system may further include a gamification section. The gamification section provides elements that make user actions fun, like a game. For example, the gamification section may allow users to earn points each time they achieve a specific goal and use those points to purchase virtual items. It may also provide a ranking function that allows users to compete with each other, thereby increasing motivation. Furthermore, it may allow users to earn badges by completing specific actions, giving them a sense of accomplishment. This allows users to continue taking actions toward achieving their goals while having fun.

[0044] The system may further include a social interaction section. The social interaction section provides a function that allows users to interact with other users. For example, a forum may be provided where users can share tips and success stories. A chat function may also be provided where users can exchange advice and support each other. Furthermore, users may create groups with a common goal and share their progress within the group. This allows users to continue taking action toward achieving their goals while interacting with other people.

[0045] The system can further include a virtual coaching unit. The virtual coaching unit provides individual coaching to the user. For example, it creates a training plan based on the user's goals and periodically checks the user's progress. It also provides specific advice when the user encounters difficulties. It also suggests next steps based on the user's behavioral data. This allows the user to continue taking action toward achieving their goals while receiving individual coaching.

[0046] The system may further include a health management unit. The health management unit monitors the user's health status and provides an optimal health management plan. For example, the health management unit collects the user's dietary data and proposes a balanced diet plan. It also collects the user's exercise data and proposes an appropriate exercise plan. It also analyzes the user's sleep data and proposes an optimal sleeping environment. This allows the user to continue taking actions to achieve their goals while maintaining their health.

[0047] The system may further include a reminder module. The reminder module reminds the user to perform the goals and tasks they have set so that they do not forget to do so. For example, the reminder module may send a notification when the deadline for a task set by the user is approaching. The reminder module may also remind the user to start taking action at a specific time. Furthermore, the reminder module may periodically remind the user so that the user can check their progress toward achieving their goal. This helps the user to remember to continue taking action toward achieving their goal.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The Tips Collection Department collects tips from high performers, for example, from interviews, books, blogs, etc. of successful business people, athletes, and scholars. Step 2: The analysis unit analyzes the collected tips. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the collected information and extract important tips. The generation AI may also use a multimodal generation AI to integrate and analyze data from multiple sources. Step 3: The customization unit customizes the analyzed tips to suit the user. For example, it generates specific action plans and advice based on the user's lifestyle habits and goals. The generation AI provides optimal assistance based on the user's profile information. Step 4: The assistance provider provides customized assistance. For example, it monitors the user's daily behavior and provides appropriate feedback. The generation AI analyzes the user's behavioral data and suggests areas for improvement and next steps.

[0050] (Example 2) The system according to an embodiment of the present invention is a system that realizes the lifestyle of people who can continue to achieve high results while having a comfortable life. This system learns the tips practiced by the top 1% of people known as high performers and executes them through individually customized AI assistance. This allows the system to enable users to continue to achieve high results while having a comfortable life.

[0051] The system according to the embodiment includes a tips collection unit, an analysis unit, a customization unit, and an assistance provision unit. The tips collection unit collects tips from high performers. For example, it collects information from interviews, books, blogs, and other sources of information from successful businesspeople, athletes, and scholars. The analysis unit analyzes the collected tips. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the collected information and extract important tips. The generation AI can also use a multimodal generation AI to integrate and analyze data from multiple sources. The customization unit customizes the analyzed tips to suit the user. For example, it generates specific action plans and advice based on the user's lifestyle habits and goals. The generation AI provides optimal assistance based on the user's profile information. The assistance provision unit provides customized assistance. For example, it monitors the user's daily behavior and provides appropriate feedback. The generation AI analyzes the user's behavioral data and suggests areas for improvement and next steps. This allows the system to enable users to learn tips from high performers and receive individually customized assistance, thereby enabling them to continue achieving high results. For example, business people can create specific action plans to work more efficiently, and athletes can receive advice on maximizing the effectiveness of their training. Also, by monitoring daily actions and receiving appropriate feedback, it becomes easier to maintain motivation to achieve goals.

[0052] The tips collection unit can also collect information from private interviews and private notes of high performers. For example, the tips collection unit collects private interviews of high performers, and the generation AI analyzes their contents. For example, it can analyze private interviews of business people and extract the secrets of their success. The tips collection unit also collects private notes of high performers, and the generation AI analyzes their contents. For example, it can analyze handwritten notes or digital notebooks and extract important information. This allows for deeper insights.

[0053] The analysis unit can analyze the behavioral patterns of high performers and identify optimal behavior depending on the time of day and environmental conditions. For example, the analysis unit collects behavioral data of high performers, and the generation AI analyzes that data. For example, it analyzes a businessperson's daily schedule and identifies the optimal time of day. The analysis unit also analyzes the behavioral patterns of high performers and identifies optimal behavior depending on environmental conditions. For example, it suggests optimal behavior taking into account environmental conditions such as temperature, humidity, and noise level. This allows for efficient behavior by identifying optimal behavior.

[0054] The analysis unit uses the emotion estimation function to analyze how high performers feel in specific situations and can extract tips based on those emotions. For example, the analysis unit collects emotional data from high performers, and the generation AI analyzes that data. For example, it analyzes the emotions of business people during meetings and extracts tips for stress management. The analysis unit also uses the emotion estimation function to analyze emotions in specific situations. For example, it uses facial expression recognition technology to analyze facial expressions during meetings and infer emotions. This allows for the extraction of tips based on emotions, enabling more effective assistance.

[0055] The customization unit can analyze the user's past behavioral data and identify the most effective assistance method. For example, the customization unit collects the user's past behavioral data, and the generation AI analyzes that data. For example, it analyzes a business person's past schedule and identifies the optimal assistance method. The customization unit also suggests the optimal assistance method based on the user's behavioral data. For example, it identifies an effective assistance method based on past success stories. This makes it possible to identify the optimal assistance method based on past behavioral data.

[0056] The customization unit can collect the user's physiological data and provide optimal assistance based on that data. For example, the customization unit collects the user's physiological data, and the generation AI analyzes that data. For example, it analyzes the heart rate data of a business person and provides assistance with stress management. The customization unit also analyzes the user's sleep patterns and provides optimal assistance. For example, it suggests optimal rest times based on the sleep data. This makes it possible to provide optimal assistance based on the physiological data.

[0057] The customization unit can use the emotion estimation function to provide assistance in real time according to the user's emotional state. For example, the customization unit collects user emotion data, and the generation AI analyzes the data. For example, the customization unit analyzes the emotional state of a business person and provides stress management assistance in real time. The customization unit also uses the emotion estimation function to analyze emotions in real time and provide appropriate assistance. For example, it uses facial expression recognition technology to analyze emotions in real time and provide feedback. This makes it possible to provide assistance in real time according to the emotional state.

[0058] The assistance providing unit can analyze the user's behavioral data in real time and provide instantaneous feedback. For example, the assistance providing unit collects the user's behavioral data in real time, and the generation AI analyzes the data. For example, the assistance providing unit analyzes the work progress of a business person in real time and provides instantaneous feedback. The assistance providing unit also analyzes the behavioral data in real time and provides appropriate feedback. For example, it provides instantaneous advice to improve work efficiency. This allows for rapid improvement by providing feedback in real time.

[0059] The assistance providing unit can analyze user behavioral data over the long term and identify changes in behavioral patterns. For example, the assistance providing unit collects user behavioral data over the long term, and the generation AI analyzes that data. For example, the assistance providing unit analyzes the work patterns of business people over the long term and identifies changes in behavioral patterns. The assistance providing unit also proposes sustainable improvements based on the long-term behavioral data. For example, it identifies changes in behavioral patterns based on past data and suggests areas for improvement. This makes it possible to identify long-term changes in behavioral patterns and make sustainable improvements.

[0060] The assistance providing unit can provide feedback based on the user's emotional state using the emotion estimation function. For example, the assistance providing unit collects user emotion data and the generation AI analyzes the data. For example, the assistance providing unit analyzes the emotional state of a business person and provides feedback based on the emotion. The assistance providing unit also uses the emotion estimation function to provide feedback based on the emotional state. For example, it uses facial expression recognition technology to analyze emotions and provide appropriate feedback. This makes it possible to provide psychological support to the user by providing feedback based on the emotional state.

[0061] The assistance providing unit can analyze the user's past success experiences and identify ways to increase motivation based on them. For example, the assistance providing unit collects data on the user's past success experiences, and the generation AI analyzes the data. For example, it analyzes the past success stories of business people and identifies ways to increase motivation. The assistance providing unit also suggests ways to increase motivation based on past success experiences. For example, it provides specific advice based on success stories. This makes it possible to identify ways to increase motivation based on past success experiences.

[0062] The assistance providing unit can visualize the user's degree of goal achievement, allowing the user to feel a sense of accomplishment. For example, the assistance providing unit collects data on the user's degree of goal achievement, and the generation AI analyzes the data. For example, the assistance providing unit visualizes the degree of goal achievement of a business person, allowing the user to feel a sense of accomplishment. The assistance providing unit also displays the degree of goal achievement in graphs and charts, providing feedback to the user. For example, a dashboard can be used to display the degree of goal achievement in real time. This makes the degree of goal achievement visible, thereby enhancing the user's sense of accomplishment.

[0063] The assistance providing unit can use the emotion estimation function to provide an encouraging message according to the user's emotional state. For example, the assistance providing unit collects the user's emotional data and the generation AI analyzes the data. For example, the assistance providing unit analyzes the emotional state of a businessperson and provides an encouraging message according to the emotion. The assistance providing unit also uses the emotion estimation function to provide an encouraging message according to the emotional state. For example, it uses facial expression recognition technology to analyze emotions and provide an appropriate message. In this way, by providing an encouraging message according to the emotional state, it is possible to maintain and improve the user's motivation.

[0064] The assistance providing unit can analyze the user's learning history and provide an optimal learning plan. For example, the assistance providing unit collects the user's learning history data, and the generation AI analyzes that data. For example, the assistance providing unit analyzes a business person's past learning history and provides an optimal learning plan. The assistance providing unit also suggests an optimal learning plan based on the learning history. For example, it provides an effective learning plan based on past learning content and test results. This makes it possible to provide an optimal learning plan based on the learning history.

[0065] The assistance providing unit can provide a customized learning plan according to the user's learning style. For example, the assistance providing unit collects learning style data from the user and the generation AI analyzes the data. For example, the assistance providing unit analyzes the learning style of a business person and provides an optimal learning plan. The assistance providing unit also proposes a customized learning plan according to the learning style. For example, it provides the optimal learning method according to learning style, such as visual, auditory, or experiential. This makes it possible to provide a customized learning plan according to the learning style.

[0066] The assistance providing unit can use the emotion estimation function to provide a study plan that corresponds to the user's emotional state. For example, the assistance providing unit collects user emotional data and the generation AI analyzes the data. For example, the assistance providing unit analyzes the emotional state of a businessperson and provides a study plan that corresponds to the emotion. The assistance providing unit also uses the emotion estimation function to provide a study plan that corresponds to the emotional state. For example, it uses facial expression recognition technology to analyze emotions and provide an appropriate study plan. In this way, by providing a study plan that corresponds to the emotional state, the user's learning effectiveness can be improved.

[0067] The assistance providing unit can compare the user's study plan with the plans of other users and provide a benchmark. For example, the assistance providing unit collects the user's study plan data, and the generation AI compares it with the data of other users. For example, the assistance providing unit compares the study plan of a business person with that of other users and provides a benchmark. The assistance providing unit also suggests areas for improvement in the user's study plan by comparing it with the plans of other users. For example, it suggests improvements to the study plan based on the average or highest value of other users. This makes it possible to improve the user's study plan by comparing it with other users.

[0068] The assistance providing unit can make the user's study plan accessible from different devices. For example, the assistance providing unit makes the user's study plan data accessible from different devices, and the generation AI analyzes the data. For example, the assistance providing unit makes a business person's study plan accessible from a tablet and a PC. The assistance providing unit also improves user convenience by supporting access from different devices. For example, the assistance providing unit makes it possible to access a study plan from multiple devices, such as a smartphone, tablet, and PC. This improves user convenience by making it accessible from different devices.

[0069] The assistance providing unit can use the emotion estimation function to identify the study plan that will evoke the most positive emotions in the user and provide that plan preferentially. For example, the assistance providing unit collects user emotion data, and the generation AI analyzes the data. For example, it analyzes the emotional state of a business person and identifies the study plan that will evoke the most positive emotions. The assistance providing unit also uses the emotion estimation function to provide a study plan that will evoke positive emotions. For example, it uses facial expression recognition technology to analyze emotions and provide an appropriate study plan. This can increase the user's motivation to study by providing a study plan that evokes positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The system may further include a gamification section. The gamification section provides elements that make user actions fun, like a game. For example, the gamification section may allow users to earn points each time they achieve a specific goal and use those points to purchase virtual items. It may also provide a ranking function that allows users to compete with each other, thereby increasing motivation. Furthermore, it may allow users to earn badges by completing specific actions, giving them a sense of accomplishment. This allows users to continue taking actions toward achieving their goals while having fun.

[0072] The system may further include a social interaction section. The social interaction section provides a function that allows users to interact with other users. For example, a forum may be provided where users can share tips and success stories. A chat function may also be provided where users can exchange advice and support each other. Furthermore, users may create groups with a common goal and share their progress within the group. This allows users to continue taking action toward achieving their goals while interacting with other people.

[0073] The system can further include a virtual coaching unit. The virtual coaching unit provides individual coaching to the user. For example, it creates a training plan based on the user's goals and periodically checks the user's progress. It also provides specific advice when the user encounters difficulties. It also suggests next steps based on the user's behavioral data. This allows the user to continue taking action toward achieving their goals while receiving individual coaching.

[0074] The system may further include a health management unit. The health management unit monitors the user's health status and provides an optimal health management plan. For example, the health management unit collects the user's dietary data and proposes a balanced diet plan. It also collects the user's exercise data and proposes an appropriate exercise plan. It also analyzes the user's sleep data and proposes an optimal sleeping environment. This allows the user to continue taking actions to achieve their goals while maintaining their health.

[0075] The system may further include a reminder module. The reminder module reminds the user to perform the goals and tasks they have set so that they do not forget to do so. For example, the reminder module may send a notification when the deadline for a task set by the user is approaching. The reminder module may also remind the user to start taking action at a specific time. Furthermore, the reminder module may periodically remind the user so that the user can check their progress toward achieving their goal. This helps the user to remember to continue taking action toward achieving their goal.

[0076] The customization unit can analyze the user's emotional data and suggest relaxation methods according to the user's stress level. For example, if the user is feeling high stress, it can suggest deep breathing or meditation. It can also provide music or natural sounds that will help the user relax. It can also provide advice on creating an environment where the user can relax. This allows the user to continue taking action to achieve their goals while reducing stress.

[0077] The customization unit can analyze the user's emotional data and provide encouraging messages if their motivation is low. For example, if the user is feeling down, it can share positive messages or success stories. It can also boost motivation by having the user review past achievements. It also checks that the user is progressing toward their goal and suggests the next step. This allows the user to maintain motivation and continue taking action to achieve their goal.

[0078] The analysis unit can analyze the user's emotional data and suggest a study method that suits a specific emotional state. For example, if the user lacks concentration, it will suggest a study method that allows them to concentrate in a short amount of time. If the user is relaxed, it will suggest a study method that encourages deep understanding. Furthermore, if the user is feeling stressed, it will suggest a method that allows them to study while relaxing. This allows the user to practice the optimal study method according to their emotional state.

[0079] The assistance providing unit can analyze the user's emotional data and provide feedback according to their emotional state. For example, if the user is feeling positive, the assistance providing unit provides feedback encouraging them to take on further challenges. If the user is feeling negative, the assistance providing unit provides encouraging messages or suggests relaxation techniques. If the user is feeling neutral, the assistance providing unit provides specific advice for moving on to the next step. This allows the user to receive appropriate feedback according to their emotional state.

[0080] The assistance providing unit can analyze the user's emotional data and suggest goal setting according to their emotional state. For example, if the user is highly motivated, it will suggest a challenging goal. If the user is lowly motivated, it will suggest a small, easy-to-achieve goal. Furthermore, if the user is feeling stressed, it will suggest a goal that will help them relax. This allows the user to set optimal goals according to their emotional state and continue taking action to achieve them.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The Tips Collection Department collects tips from high performers, for example, from interviews, books, blogs, etc. of successful business people, athletes, and scholars. Step 2: The analysis unit analyzes the collected tips. For example, the generation AI may use a text generation AI (e.g., LLM) to analyze the collected information and extract important tips. The generation AI may also use a multimodal generation AI to integrate and analyze data from multiple sources. Step 3: The customization unit customizes the analyzed tips to suit the user. For example, it generates specific action plans and advice based on the user's lifestyle habits and goals. The generation AI provides optimal assistance based on the user's profile information. Step 4: The assistance provider provides customized assistance. For example, it monitors the user's daily behavior and provides appropriate feedback. The generation AI analyzes the user's behavioral data and suggests areas for improvement and next steps.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] 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.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 tips collection department that collects tips from high performers, an analysis unit that analyzes the tips collected by the tips collection unit; a customization unit that customizes the Tips analyzed by the analysis unit to suit the user; an assistance providing unit that provides assistance customized by the customization unit; A system characterized by:

2. The tips collection unit Gather information from closed interviews and private notes of the high performers The system of claim 1 .

3. The customization unit Analyzing the user's past behavioral data and identifying the most effective assistance method The system of claim 1 .

4. The assistance providing unit Analyze the user's behavioral data in real time and provide immediate feedback The system of claim 1 .

5. The analysis unit Using emotion estimation, we analyze how the high performers feel in specific situations and extract tips based on those emotions. The system of claim 1 .

6. The customization unit Using an emotion estimation function, assistance is provided in real time according to the user's emotional state. The system of claim 1 .

7. The assistance providing unit Using emotion estimation functionality to provide feedback based on the user's emotional state The system of claim 1 .

8. The assistance providing unit Using emotion estimation, the learning plan that evokes the most positive emotion from the user is identified and that plan is provided as a priority. The system of claim 1 .

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