System

Abstract

An object of the system according to the embodiment is to provide specific advice by back-calculating a future possibility based on past information, interests, and goals of an individual.SOLUTION: A system includes an information collection part, an interest specification part, a back calculation part, and an advice provision part. The information collection unit collects past information of an individual. The interest specifying unit specifies an interest and a goal of an individual based on the information collected by the information collecting unit. The reverse calculation unit reversely calculates a future possibility based on the interest and the goal specified by the interest specifying unit. The advice providing unit provides specific advice based on a result of the back calculation performed by the back calculation 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 technology has had the problem of making it difficult for individuals to make specific plans for their future.

[0005] The system according to the embodiment aims to provide specific advice by calculating future possibilities based on an individual's past information, interests, and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an interest identification unit, a reverse calculation unit, and an advice provision unit. The information collection unit collects past information about an individual. The interest identification unit identifies the individual's interests and goals based on the information collected by the information collection unit. The reverse calculation unit reverse-calculates future possibilities based on the interests and goals identified by the interest identification unit. The advice provision unit provides specific advice based on the results of the reverse calculation by the reverse calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide specific advice by calculating future possibilities based on an individual's past information, interests, and goals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The advice providing system according to an embodiment of the present invention uses a generation AI to analyze an individual's past information, interests, and goals, and then provides specific advice by calculating future possibilities. This allows the advice providing system to turn an individual's abstract ideas about the future into concrete plans.

[0029] An advice providing system according to an embodiment includes an information collecting unit, an interest identifying unit, a reverse calculation unit, and an advice providing unit. The information collecting unit collects past information about an individual. For example, the information collecting unit collects data such as the individual's educational background, work history, hobbies, and past projects. The information collecting unit can also collect the individual's health information and daily activity data. For example, the data can be obtained from a health app or a fitness tracker. The interest identifying unit identifies the individual's interests and goals based on the information collected by the information collecting unit. For example, the interest identifying unit analyzes information such as the individual's future career aspirations and the skills they want to acquire. The interest identifying unit can also analyze social media data to identify the individual's hobbies and interests. The reverse calculation unit reverse-calculates future possibilities based on the interests and goals identified by the interest identifying unit. For example, if an individual is aiming for a specific career, the reverse calculation unit identifies the steps and skills necessary to achieve that career. The reverse calculation unit can also create a realistic plan taking into account the individual's lifestyle and values. The advice providing unit provides specific advice based on the results of the reverse calculation by the reverse calculation unit. For example, the system may make specific suggestions, such as what courses an individual should take or what projects they should participate in to acquire specific skills. The advice-providing unit can also provide customized advice based on an individual's learning style and preferences. This allows the advice-providing system according to the embodiment to provide specific advice by calculating future possibilities based on an individual's past information. For example, someone considering a career change can efficiently move toward their goal by taking specific steps based on advice from the AI. Students can also use the AI's advice as a reference when considering their future career paths to find a path that suits them.

[0030] The information collection unit can analyze a personal diary or memo and extract important information from small everyday events. The information collection unit, for example, analyzes a personal diary and extracts important information from small everyday events. For example, it extracts keywords from the diary text and identifies important events. The information collection unit also analyzes a personal note and extracts important information from small everyday events. For example, it analyzes the contents of the note and identifies important events and tasks. The information collection unit also analyzes a personal schedule or calendar and extracts important information from small everyday events. For example, it analyzes the contents of the schedule and identifies important events and tasks. This makes it possible to extract important information from small everyday events.

[0031] The interest identification unit can analyze an individual's lifestyle and values ​​and set goals based on the results. The interest identification unit, for example, analyzes an individual's lifestyle and sets goals based on the results. For example, realistic goals are set based on daily behavioral patterns and lifestyle habits. The interest identification unit also analyzes an individual's values ​​and sets goals based on the results. For example, meaningful goals are set based on the values ​​and beliefs that the individual holds dear. The interest identification unit also comprehensively analyzes the lifestyle and values ​​and sets balanced goals. For example, realistic goals that fit the lifestyle are combined with meaningful goals based on the values. This makes it possible to set goals based on an individual's lifestyle and values.

[0032] The reverse calculation unit can analyze an individual's skill set and market demand, and propose the most in-demand career path. For example, the reverse calculation unit analyzes an individual's skill set and compares it with market demand to propose the most in-demand career path. For example, it proposes occupations and industries that are in high demand based on the current skill set. The reverse calculation unit also analyzes market demand and proposes the most in-demand career path in combination with the individual's skill set. For example, it proposes a highly in-demand career path based on the latest job information and industry trends. The reverse calculation unit also comprehensively analyzes an individual's skill set and market demand, and proposes the most in-demand career path. For example, it proposes occupations and industries that are in high demand that make use of the strengths of the skill set. This makes it possible to propose the most in-demand career path based on an individual's skill set and market demand.

[0033] The advice providing unit can provide customized advice based on an individual's learning style and preferences. For example, the advice providing unit analyzes an individual's learning style and provides customized advice based on the analysis. For example, an individual with a visual learning style is provided with advice using diagrams and graphs. The advice providing unit also analyzes an individual's preferences and provides customized advice based on the analysis. For example, an individual interested in a specific theme or topic is provided with advice related to that theme. The advice providing unit also comprehensively analyzes the learning style and preferences and provides balanced customized advice. For example, an individual with a visual learning style and an interest in a specific theme is provided with advice related to that theme using diagrams and graphs. In this way, customized advice can be provided based on an individual's learning style and preferences.

[0034] The advice providing unit can provide advice in different media formats to suit individual preferences. The advice providing unit, for example, builds a system that provides advice in different media formats. For example, advice in video format, advice in audio format, and advice in text format is provided. The advice providing unit also provides advice in the optimal media format to suit individual preferences. For example, advice in video format is provided to individuals with a visual learning style. The advice providing unit also combines different media formats to provide balanced advice. For example, advice in a combination of video format and text format is provided. This makes it possible to provide advice in different media formats to suit individual preferences.

[0035] The advice providing unit can incorporate feedback from other users by utilizing personal networks and communities. The advice providing unit, for example, utilizes personal networks and communities to build a system that collects feedback from other users. For example, it utilizes online forums and social media. The advice providing unit also improves advice based on feedback from other users. For example, it analyzes the feedback and adjusts the content of the advice. The advice providing unit also utilizes networks and communities to collect feedback from other users in real time and reflect it in the advice. For example, it utilizes online chat and real-time comment functions. This makes it possible to utilize personal networks and communities to incorporate feedback from other users.

[0036] The advice providing unit can analyze an individual's behavioral history and evaluate the degree to which the advice is implemented. The advice providing unit, for example, analyzes an individual's behavioral history and evaluates the degree to which the advice is implemented. For example, it analyzes records of behavior based on the advice and calculates the degree of implementation. The advice providing unit also builds a system that evaluates the degree to which the advice is implemented based on the behavioral history. For example, it analyzes the frequency and results of behavior and evaluates the degree of implementation. The advice providing unit also comprehensively analyzes an individual's behavioral history and evaluates the degree to which the advice is implemented. For example, it calculates and evaluates the degree of implementation based on records and results of behavior. This makes it possible to analyze an individual's behavioral history and evaluate the degree to which the advice is implemented.

[0037] The advice providing unit can analyze an individual's long-term results and evaluate the effectiveness of the advice. The advice providing unit, for example, analyzes an individual's long-term results and evaluates the effectiveness of the advice. For example, it tracks and evaluates the results of actions based on the advice over the long term. The advice providing unit also builds a system to evaluate the effectiveness of the advice based on the long-term results. For example, it analyzes the sustainability and impact of the results and evaluates the effectiveness. The advice providing unit also comprehensively analyzes an individual's long-term results and evaluates the effectiveness of the advice. For example, it calculates and evaluates the effectiveness based on the records and impact of the results. In this way, it is possible to analyze an individual's long-term results and evaluate the effectiveness of the advice.

[0038] The advice providing unit can utilize different feedback formats to evaluate advice from multiple perspectives. The advice providing unit, for example, utilizes different feedback formats to build a system for evaluation from multiple perspectives. For example, feedback is collected by combining questionnaires, interviews, and reviews. The advice providing unit also sets evaluation criteria for each feedback format and evaluates from multiple perspectives. For example, the questionnaire results, interview content, and review evaluations are analyzed comprehensively. The advice providing unit also develops a feedback collection system to utilize different feedback formats to evaluate from multiple perspectives. For example, feedback is collected by combining online questionnaires, video interviews, and text reviews. This allows advice to be evaluated from multiple perspectives by utilizing different feedback formats.

[0039] The advice providing unit can analyze social media reactions and collect feedback from a wide range of perspectives. The advice providing unit, for example, builds a system that analyzes social media reactions and collects feedback from a wide range of perspectives. For example, it analyzes the content of posts, comments, and the number of likes. The advice providing unit also evaluates advice based on social media reactions. For example, it analyzes positive and negative reactions and evaluates the effectiveness of the advice. The advice providing unit also comprehensively analyzes social media reactions and collects feedback from a wide range of perspectives. For example, it collects and evaluates feedback based on the content of posts, comments, and the number of likes. This makes it possible to analyze social media reactions and collect feedback from a wide range of perspectives.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The advice providing system may further include a behavior evaluation unit that analyzes the user's behavior history and evaluates the degree to which the advice is implemented. For example, it may track whether the user has acted based on the advice and evaluate the degree to which the advice is implemented. It may also be possible to evaluate the effectiveness of the advice based on the behavior history. Furthermore, it may be possible to provide specific steps and reminders to make it easier for the user to implement the advice. This may support the user's behavior and maximize the effectiveness of the advice.

[0042] The advice providing system may further include a feedback collection unit that utilizes the user's network or community to incorporate feedback from other users. For example, online forums or social media may be utilized to collect opinions and advice from other users. The advice content may also be improved based on the feedback. Furthermore, feedback may be collected in real time and reflected in the advice. This allows the system to utilize the user's network or community to provide advice from a more multifaceted perspective.

[0043] The advice providing system may further include a health analysis unit that analyzes the user's health information and provides advice based on the user's health condition. For example, the system may acquire data from the user's fitness tracker or health app and analyze the user's health condition. The system may also provide appropriate exercise and diet advice based on the user's health condition. Furthermore, the system may provide a specific plan tailored to the user's health goals. This allows the system to provide appropriate advice based on the user's health condition.

[0044] The advice providing system can further include a learning analysis unit that provides customized advice based on the user's learning style and preferences. For example, a user with a visual learning style can be provided with advice using diagrams and graphs. A user who is interested in a particular theme or topic can be provided with advice related to that theme. Furthermore, the system can comprehensively analyze the user's learning style and preferences to provide balanced, customized advice. This allows the system to provide appropriate advice based on the user's learning style and preferences.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The information collection unit collects an individual's past information, such as their educational background, work history, hobbies, and past projects. It can also collect their health information and daily activity data, such as data from health apps and fitness trackers. Step 2: The interest identification unit identifies the individual's interests and goals based on the information collected by the information collection unit. For example, it analyzes information such as the individual's future career aspirations and the skills they want to acquire. It can also analyze social media data to identify the individual's hobbies and interests. Step 3: The reverse calculation section calculates future possibilities based on the interests and goals identified by the interest identification section. For example, if an individual is aiming for a specific career, it identifies the steps and skills needed to reach that career. It also takes into account the individual's lifestyle and values ​​to create a realistic plan. Step 4: The advice provider provides specific advice based on the results of the back-calculation process. For example, it makes specific suggestions about what courses an individual should take or what projects they should participate in to acquire specific skills. It can also provide customized advice based on an individual's learning style and preferences.

[0047] (Example 2) The advice providing system according to an embodiment of the present invention uses a generation AI to analyze an individual's past information, interests, and goals, and then provides specific advice by calculating future possibilities. This allows the advice providing system to turn an individual's abstract ideas about the future into concrete plans.

[0048] An advice providing system according to an embodiment includes an information collecting unit, an interest identifying unit, a reverse calculation unit, and an advice providing unit. The information collecting unit collects past information about an individual. For example, the information collecting unit collects data such as the individual's educational background, work history, hobbies, and past projects. The information collecting unit can also collect the individual's health information and daily activity data. For example, the data can be obtained from a health app or a fitness tracker. The interest identifying unit identifies the individual's interests and goals based on the information collected by the information collecting unit. For example, the interest identifying unit analyzes information such as the individual's future career aspirations and the skills they want to acquire. The interest identifying unit can also analyze social media data to identify the individual's hobbies and interests. The reverse calculation unit reverse-calculates future possibilities based on the interests and goals identified by the interest identifying unit. For example, if an individual is aiming for a specific career, the reverse calculation unit identifies the steps and skills necessary to achieve that career. The reverse calculation unit can also create a realistic plan taking into account the individual's lifestyle and values. The advice providing unit provides specific advice based on the results of the reverse calculation by the reverse calculation unit. For example, the system may make specific suggestions, such as what courses an individual should take or what projects they should participate in to acquire specific skills. The advice-providing unit can also provide customized advice based on an individual's learning style and preferences. This allows the advice-providing system according to the embodiment to provide specific advice by calculating future possibilities based on an individual's past information. For example, someone considering a career change can efficiently move toward their goal by taking specific steps based on advice from the AI. Students can also use the AI's advice as a reference when considering their future career paths to find a path that suits them.

[0049] The information collection unit can analyze a personal diary or memo and extract important information from small everyday events. The information collection unit, for example, analyzes a personal diary and extracts important information from small everyday events. For example, it extracts keywords from the diary text and identifies important events. The information collection unit also analyzes a personal note and extracts important information from small everyday events. For example, it analyzes the contents of the note and identifies important events and tasks. The information collection unit also analyzes a personal schedule or calendar and extracts important information from small everyday events. For example, it analyzes the contents of the schedule and identifies important events and tasks. This makes it possible to extract important information from small everyday events.

[0050] The interest identification unit can analyze an individual's lifestyle and values ​​and set goals based on the results. The interest identification unit, for example, analyzes an individual's lifestyle and sets goals based on the results. For example, realistic goals are set based on daily behavioral patterns and lifestyle habits. The interest identification unit also analyzes an individual's values ​​and sets goals based on the results. For example, meaningful goals are set based on the values ​​and beliefs that the individual holds dear. The interest identification unit also comprehensively analyzes the lifestyle and values ​​and sets balanced goals. For example, realistic goals that fit the lifestyle are combined with meaningful goals based on the values. This makes it possible to set goals based on an individual's lifestyle and values.

[0051] The reverse calculation unit can analyze an individual's skill set and market demand, and propose the most in-demand career path. For example, the reverse calculation unit analyzes an individual's skill set and compares it with market demand to propose the most in-demand career path. For example, it proposes occupations and industries that are in high demand based on the current skill set. The reverse calculation unit also analyzes market demand and proposes the most in-demand career path in combination with the individual's skill set. For example, it proposes a highly in-demand career path based on the latest job information and industry trends. The reverse calculation unit also comprehensively analyzes an individual's skill set and market demand, and proposes the most in-demand career path. For example, it proposes occupations and industries that are in high demand that make use of the strengths of the skill set. This makes it possible to propose the most in-demand career path based on an individual's skill set and market demand.

[0052] The advice providing unit can provide customized advice based on an individual's learning style and preferences. For example, the advice providing unit analyzes an individual's learning style and provides customized advice based on the analysis. For example, an individual with a visual learning style is provided with advice using diagrams and graphs. The advice providing unit also analyzes an individual's preferences and provides customized advice based on the analysis. For example, an individual interested in a specific theme or topic is provided with advice related to that theme. The advice providing unit also comprehensively analyzes the learning style and preferences and provides balanced customized advice. For example, an individual with a visual learning style and an interest in a specific theme is provided with advice related to that theme using diagrams and graphs. In this way, customized advice can be provided based on an individual's learning style and preferences.

[0053] The advice providing unit uses the emotion estimation function to analyze the emotions that an individual has toward the advice in real time, and can prioritize emotionally positive advice. The advice providing unit, for example, uses the emotion estimation function to analyze the emotions that an individual has toward the advice in real time. For example, it analyzes facial expressions and voice when the advice is presented and calculates an emotion score. The advice providing unit also analyzes the emotions toward the advice in real time and prioritizes emotionally positive advice. For example, it selects advice based on the emotion score when the advice is presented. The advice providing unit also uses the emotion estimation function to analyze the emotions that an individual has toward the advice in real time, and builds a system that prioritizes emotionally positive advice. For example, it selects advice based on the emotion score when the advice is presented. This makes it possible to analyze the emotions that an individual has toward the advice in real time and prioritize emotionally positive advice.

[0054] The advice providing unit can provide advice in different media formats to suit individual preferences. The advice providing unit, for example, builds a system that provides advice in different media formats. For example, advice in video format, advice in audio format, and advice in text format is provided. The advice providing unit also provides advice in the optimal media format to suit individual preferences. For example, advice in video format is provided to individuals with a visual learning style. The advice providing unit also combines different media formats to provide balanced advice. For example, advice in a combination of video format and text format is provided. This makes it possible to provide advice in different media formats to suit individual preferences.

[0055] The advice providing unit can incorporate feedback from other users by utilizing personal networks and communities. The advice providing unit, for example, utilizes personal networks and communities to build a system that collects feedback from other users. For example, it utilizes online forums and social media. The advice providing unit also improves advice based on feedback from other users. For example, it analyzes the feedback and adjusts the content of the advice. The advice providing unit also utilizes networks and communities to collect feedback from other users in real time and reflect it in the advice. For example, it utilizes online chat and real-time comment functions. This makes it possible to utilize personal networks and communities to incorporate feedback from other users.

[0056] The advice providing unit uses the emotion estimation function to analyze the emotions that an individual has toward the advice in real time, and can prioritize emotionally positive advice. The advice providing unit, for example, uses the emotion estimation function to analyze the emotions that an individual has toward the advice in real time. For example, it analyzes facial expressions and voice when the advice is presented and calculates an emotion score. The advice providing unit also analyzes the emotions toward the advice in real time and prioritizes emotionally positive advice. For example, it selects advice based on the emotion score when the advice is presented. The advice providing unit also uses the emotion estimation function to analyze the emotions that an individual has toward the advice in real time, and builds a system that prioritizes emotionally positive advice. For example, it selects advice based on the emotion score when the advice is presented. This makes it possible to analyze the emotions that an individual has toward the advice in real time and prioritize emotionally positive advice.

[0057] The advice providing unit can analyze an individual's behavioral history and evaluate the degree to which the advice is implemented. The advice providing unit, for example, analyzes an individual's behavioral history and evaluates the degree to which the advice is implemented. For example, it analyzes records of behavior based on the advice and calculates the degree of implementation. The advice providing unit also builds a system that evaluates the degree to which the advice is implemented based on the behavioral history. For example, it analyzes the frequency and results of behavior and evaluates the degree of implementation. The advice providing unit also comprehensively analyzes an individual's behavioral history and evaluates the degree to which the advice is implemented. For example, it calculates and evaluates the degree of implementation based on records and results of behavior. This makes it possible to analyze an individual's behavioral history and evaluate the degree to which the advice is implemented.

[0058] The advice providing unit can analyze an individual's long-term results and evaluate the effectiveness of the advice. The advice providing unit, for example, analyzes an individual's long-term results and evaluates the effectiveness of the advice. For example, it tracks and evaluates the results of actions based on the advice over the long term. The advice providing unit also builds a system to evaluate the effectiveness of the advice based on the long-term results. For example, it analyzes the sustainability and impact of the results and evaluates the effectiveness. The advice providing unit also comprehensively analyzes an individual's long-term results and evaluates the effectiveness of the advice. For example, it calculates and evaluates the effectiveness based on the records and impact of the results. In this way, it is possible to analyze an individual's long-term results and evaluate the effectiveness of the advice.

[0059] The advice providing unit can utilize different feedback formats to evaluate advice from multiple perspectives. The advice providing unit, for example, utilizes different feedback formats to build a system for evaluation from multiple perspectives. For example, feedback is collected by combining questionnaires, interviews, and reviews. The advice providing unit also sets evaluation criteria for each feedback format and evaluates from multiple perspectives. For example, the questionnaire results, interview content, and review evaluations are analyzed comprehensively. The advice providing unit also develops a feedback collection system to utilize different feedback formats to evaluate from multiple perspectives. For example, feedback is collected by combining online questionnaires, video interviews, and text reviews. This allows advice to be evaluated from multiple perspectives by utilizing different feedback formats.

[0060] The advice providing unit can analyze social media reactions and collect feedback from a wide range of perspectives. The advice providing unit, for example, builds a system that analyzes social media reactions and collects feedback from a wide range of perspectives. For example, it analyzes the content of posts, comments, and the number of likes. The advice providing unit also evaluates advice based on social media reactions. For example, it analyzes positive and negative reactions and evaluates the effectiveness of the advice. The advice providing unit also comprehensively analyzes social media reactions and collects feedback from a wide range of perspectives. For example, it collects and evaluates feedback based on the content of posts, comments, and the number of likes. This makes it possible to analyze social media reactions and collect feedback from a wide range of perspectives.

[0061] The advice providing unit uses the emotion estimation function to analyze the emotions that an individual has toward feedback in real time, and can prioritize emotionally positive feedback. The advice providing unit, for example, uses the emotion estimation function to analyze the emotions that an individual has toward feedback in real time. For example, it analyzes facial expressions and voice at the time of presentation of feedback and calculates an emotion score. The advice providing unit also analyzes the emotions toward feedback in real time and prioritizes emotionally positive feedback. For example, it selects feedback based on the emotion score at the time of presentation of the feedback. The advice providing unit also uses the emotion estimation function to analyze the emotions that an individual has toward feedback in real time, and builds a system that prioritizes emotionally positive feedback. For example, it selects feedback based on the emotion score at the time of presentation of the feedback. This makes it possible to analyze the emotions that an individual has toward feedback in real time and prioritize emotionally positive feedback.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The advice providing system can further include an emotion adjustment unit that estimates the user's emotion and adjusts the content of the advice based on the estimated emotion. For example, if the user is feeling stressed, the system can provide advice to help them relax. If the user wants to increase their motivation, the system can provide words of encouragement or success stories. Furthermore, if the user is feeling anxious, the system can provide advice that gives a sense of security. This makes it possible to provide appropriate advice according to the user's emotion.

[0064] The advice providing system may further include a behavior evaluation unit that analyzes the user's behavior history and evaluates the degree to which the advice is implemented. For example, it may track whether the user has acted based on the advice and evaluate the degree to which the advice is implemented. It may also be possible to evaluate the effectiveness of the advice based on the behavior history. Furthermore, it may be possible to provide specific steps and reminders to make it easier for the user to implement the advice. This may support the user's behavior and maximize the effectiveness of the advice.

[0065] The advice providing system may further include a feedback collection unit that utilizes the user's network or community to incorporate feedback from other users. For example, online forums or social media may be utilized to collect opinions and advice from other users. The advice content may also be improved based on the feedback. Furthermore, feedback may be collected in real time and reflected in the advice. This allows the system to utilize the user's network or community to provide advice from a more multifaceted perspective.

[0066] The advice providing system may further include a health analysis unit that analyzes the user's health information and provides advice based on the user's health condition. For example, the system may acquire data from the user's fitness tracker or health app and analyze the user's health condition. The system may also provide appropriate exercise and diet advice based on the user's health condition. Furthermore, the system may provide a specific plan tailored to the user's health goals. This allows the system to provide appropriate advice based on the user's health condition.

[0067] The advice providing system can further include a learning analysis unit that provides customized advice based on the user's learning style and preferences. For example, a user with a visual learning style can be provided with advice using diagrams and graphs. A user who is interested in a particular theme or topic can be provided with advice related to that theme. Furthermore, the system can comprehensively analyze the user's learning style and preferences to provide balanced, customized advice. This allows the system to provide appropriate advice based on the user's learning style and preferences.

[0068] The advice providing system can further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of advice based on the estimated emotions. For example, important advice can be provided when the user is relaxed. Also, if the user is busy, the schedule can be adjusted to provide advice later. Furthermore, advice for difficult tasks can be provided when the user is emotionally calm. This makes it possible to provide advice at appropriate timing according to the user's emotions.

[0069] The advice providing system may further include a content adjustment unit that estimates the user's emotions and personalizes the content of advice based on the estimated emotions. For example, when the user is feeling down, encouraging words or positive messages may be provided. When the user is excited, advice to calm down may be provided. Furthermore, when the user is feeling anxious, advice that gives a sense of security may be provided. In this way, personalized advice can be provided according to the user's emotions.

[0070] The advice providing system can further include a format adjustment unit that estimates the user's emotions and adjusts the format of the advice based on the estimated emotions. For example, if the user prefers visual information, advice using diagrams and graphs can be provided. If the user prefers auditory information, advice can be provided in audio format. Furthermore, if the user prefers text format, advice can be provided in detailed sentences. In this way, advice can be provided in an appropriate format according to the user's emotions.

[0071] The advice providing system can further include a frequency adjustment unit that estimates the user's emotions and adjusts the frequency of advice based on the estimated emotions. For example, when the user is feeling stressed, the frequency of advice can be reduced. Also, when the user feels that they want to increase their motivation, the frequency of advice can be increased. Furthermore, when the user is relaxed, advice can be provided at an appropriate frequency. In this way, advice can be provided at an appropriate frequency according to the user's emotions.

[0072] The advice providing system can further include a tone adjustment unit that estimates the user's emotions and adjusts the tone of the advice based on the estimated emotions. For example, when the user is depressed, advice can be provided in a gentle tone. When the user is excited, advice can be provided in a calm tone. Furthermore, when the user is feeling anxious, advice can be provided in a tone that gives a sense of security. In this way, advice can be provided in an appropriate tone according to the user's emotions.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The information collection unit collects an individual's past information, such as their educational background, work history, hobbies, and past projects. It can also collect their health information and daily activity data, such as data from health apps and fitness trackers. Step 2: The interest identification unit identifies the individual's interests and goals based on the information collected by the information collection unit. For example, it analyzes information such as the individual's future career aspirations and the skills they want to acquire. It can also analyze social media data to identify the individual's hobbies and interests. Step 3: The reverse calculation section calculates future possibilities based on the interests and goals identified by the interest identification section. For example, if an individual is aiming for a specific career, it identifies the steps and skills needed to reach that career. It also takes into account the individual's lifestyle and values ​​to create a realistic plan. Step 4: The advice provider provides specific advice based on the results of the back-calculation process. For example, it makes specific suggestions about what courses an individual should take or what projects they should participate in to acquire specific skills. It can also provide customized advice based on an individual's learning style and preferences.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0088] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0089] 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.

[0090] 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.

[0091] 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 AI 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0103] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] 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 AI 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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 AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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, in order to avoid confusion and to 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.

[0141] 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]

[0142] 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 information collection unit that collects past information about individuals; an interest identification unit that identifies individual interests and goals based on the information collected by the information collection unit; a back-calculation unit that back-calculates future possibilities based on the interests and goals identified by the interest identification unit; an advice providing unit that provides specific advice based on the result of the back-calculation performed by the back-calculation unit; A system characterized by:

2. The information collecting unit Analyze the individual's diary or memo and extract important information from small everyday events. The system of claim 1 .

3. The interest identification unit Analyze the individual's lifestyle and values ​​and set goals based on them The system of claim 1 .

4. The inverse calculation unit Analyze the individual's skill set and market demand to suggest the most in-demand career paths The system of claim 1 .

5. The advice providing unit Providing customized advice based on the individual's learning style and preferences The system of claim 1 .

Images