System

Abstract

An object of a system according to an embodiment is to support a user so that the user can naturally continue an action.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit receives input of a target and an action from a user. The generation unit analyzes the information received by the reception unit and generates an idea of behavior design. The provision unit provides the idea generated by the generation unit to the user.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 techniques have had the problem of making it difficult for users to continuously perform ideal actions.

[0005] The system according to the embodiment aims to support the user so that the user can continue the activity naturally. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a target and an action from a user. The generating unit analyzes the information received by the receiving unit and generates ideas for behavioral design. The providing unit provides the ideas generated by the generating unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can support the user to continue the action naturally. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A behavioral design support system according to an embodiment of the present invention accepts input of targets and actions from a user, analyzes the information, generates behavioral design ideas, and provides them to the user. The behavioral design support system inputs the target and action of a service the user wants the user to perform, and the generation AI analyzes the information to generate behavioral design ideas. These ideas utilize the user's cognitive biases to naturally support behavior. For example, the behavioral design support system inputs the target and action of a service the user wants the user to perform. For example, if a health management app wants users to exercise daily, the user inputs the target and action such as "ask a man in his 30s to exercise for 30 minutes every day." This information is input to the generation AI. The behavioral design support system then uses the generation AI to analyze the input information and generate behavioral design ideas. The generation AI has learned the behavioral design approach and can generate ideas that utilize the user's cognitive biases. For example, an idea such as "game-ify exercise and introduce a system for accumulating points" is generated. The generated idea is provided to the user. The user can then design a service based on this idea to naturally support the user's behavior. For example, in a health management app, by gamifying exercise and introducing a system for accumulating points, users can continue to exercise while having fun. In this way, the behavioral design support system can encourage users to change their behavior by coming up with ideas that take advantage of the user's cognitive biases. In this way, the behavioral design support system can provide ideas that naturally support users' behavior. For example, it can design services that encourage users to take action voluntarily. A generative AI that has learned the behavioral design approach can encourage users to change their behavior by coming up with ideas that take advantage of the user's cognitive biases. For example, in a health management app, by gamifying exercise and introducing a system for accumulating points, users can continue to exercise while having fun.

[0029] The behavioral design support system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a target and an action from a user. The target includes, for example, a specific user group or market segment, but is not limited to these examples. The action includes, for example, a user's behavior, operation, selection, etc., but is not limited to these examples. The receiving unit, for example, inputs the target and action of a service that the user wants the user to perform. The generating unit uses a generating AI to analyze the information received by the receiving unit and generate behavioral design ideas. The generating AI, for example, learns behavioral design approaches using a specific machine learning model or neural network. The generating unit, for example, can generate ideas that utilize the user's cognitive bias. For example, the generating AI can propose a system that gamifies exercise and allows users to accumulate points. The providing unit provides the idea generated by the generating unit to the user. For example, the providing unit notifies the user of the generated idea. This allows the behavioral design support system according to the embodiment to generate and provide behavioral design ideas based on the user's target and action.

[0030] The generation unit can learn behavioral design approaches using generative AI. The generation unit, for example, learns behavioral design approaches using generative AI. The generative AI learns behavioral design approaches using, for example, a specific machine learning model or neural network. The generative AI can learn the theory and practice of behavioral design and generate ideas that utilize the user's cognitive biases. For example, the generative AI generates ideas that encourage the user to take voluntary action based on behavioral design theory. In this way, the generative AI can learn the behavioral design approach and generate more effective ideas.

[0031] The behavioral design support system includes a feedback receiving unit that receives feedback from a user. The feedback receiving unit receives feedback from a user. The feedback includes, but is not limited to, user opinions, evaluations, and comments, for example. The feedback receiving unit inputs feedback on an idea provided by a user, for example. This allows the system to be improved by receiving feedback from the user.

[0032] The behavioral design support system includes an improvement unit that improves the generative AI based on feedback. The improvement unit improves the generative AI based on feedback. Improvements include, but are not limited to, algorithm adjustment and parameter optimization, for example. The improvement unit adjusts the algorithm of the generative AI based on feedback from the user, for example. This improves the generative AI based on feedback, thereby improving the accuracy of the system.

[0033] The generation unit can use the generation AI to propose a system for gamifying exercise and earning points in a health management app. For example, the generation unit uses the generation AI to propose a system for gamifying exercise and earning points in a health management app. Health management apps include, but are not limited to, functions such as exercise records, diet management, and health checkups. Examples of gamifying exercise include, but are not limited to, a point system and a level-up function. For example, the generation AI proposes ideas for users to continue exercising while having fun. This allows users to continue exercising while having fun in the health management app.

[0034] The generation unit can use the generative AI to propose mechanisms that encourage students to learn voluntarily in the field of education. The generation unit uses the generative AI to propose mechanisms that encourage students to learn voluntarily, for example, in the field of education. The field of education includes, but is not limited to, primary education, higher education, and vocational education. Mechanisms that encourage voluntary learning include, but are not limited to, incentive systems and gamification. The generative AI proposes, for example, ideas that allow students to continue learning while having fun. This makes it possible to propose mechanisms that encourage students to learn voluntarily in the field of education.

[0035] The generation unit can use the generative AI to propose behavioral designs for employees to work efficiently in the workplace. The generation unit, for example, uses the generative AI to propose behavioral designs for employees to work efficiently in the workplace. Workplaces include, for example, office environments and remote work environments, but are not limited to these examples. Behavioral designs for working efficiently include, for example, task management, time management, and communication optimization, but are not limited to these examples. The generative AI, for example, proposes ideas for employees to work efficiently. This makes it possible to propose behavioral designs for employees to work efficiently in the workplace.

[0036] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, but is not limited to, the type of input data and the analysis algorithm. The selection of the optimal input method includes, for example, but is not limited to, the user's operability and efficiency. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. Targets and actions that the user has previously input are automatically displayed as candidates. The reception unit predicts and suggests the input method to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past input history.

[0037] The reception unit can perform filtering based on the user's current project and areas of interest when inputting a target and an action. For example, the reception unit performs filtering based on the user's current project and areas of interest when inputting a target and an action. Filtering includes, but is not limited to, selection criteria for highly relevant information and filtering algorithms, for example. For example, targets and actions related to a project the user is currently working on are preferentially displayed. Related targets and actions are suggested based on the user's areas of interest. Optimal targets and actions are suggested by referring to the user's past project history. This makes it possible to suggest related targets and actions based on the user's current project and areas of interest.

[0038] The reception unit can select the optimal input means according to the user's input method when inputting a target and an action. For example, when inputting a target and an action, the reception unit selects the optimal input means according to the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit preferentially suggests voice input. If the user prefers text input, the reception unit preferentially suggests text input. If the user prefers image input, the reception unit preferentially suggests image input. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0039] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting a target and an action. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting a target and an action. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the reception unit prioritizes suggesting targets and actions related to the user's current location. The reception unit suggests optimal targets and actions based on the user's geographical location information. The reception unit suggests targets and actions related to places the user has visited in the past. In this way, highly relevant targets and actions can be suggested by taking the user's geographical location information into consideration.

[0040] The reception unit can analyze the user's social media activity and input related information when the target and action are input. For example, the reception unit analyzes the user's social media activity and inputs related information when the target and action are input. Social media activity includes, but is not limited to, the content of posts, follower reactions, and engagement. For example, the reception unit can suggest targets and actions related to places where the user has checked in on social media. The reception unit can analyze the content of the user's social media posts and suggest related targets and actions. The reception unit can also refer to the activities of the user's friends on social media to suggest related targets and actions. In this way, the reception unit can suggest related targets and actions by analyzing the user's social media activity.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting a target and an action. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting a target and an action. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. A specific input method is preferentially suggested based on the user's past feedback. The input interface is customized by reflecting the user's feedback. In this way, the optimal input method can be suggested by reflecting the user's past feedback.

[0042] The generation unit can adjust the level of detail of an idea based on the importance of the target and the action when generating an idea. For example, the generation unit adjusts the level of detail of an idea based on the importance of the target and the action when generating an idea. Adjustments to the level of detail include, but are not limited to, information depth, concreteness, and abstractness. For example, a detailed idea is generated for a target and an action with high importance. A concise idea is generated for a target and an action with low importance. The level of detail of an idea is gradually adjusted according to the importance of the target and the action. This makes it possible to provide more appropriate ideas by adjusting the level of detail of an idea according to the importance of the target and the action.

[0043] The generation unit can apply different generation algorithms depending on the target and action categories when generating ideas. For example, the generation unit applies different generation algorithms depending on the target and action categories when generating ideas. Examples of generation algorithms include, but are not limited to, specific machine learning models and rule-based algorithms. For example, a health-related generation algorithm is applied to targets and actions related to health management. An education-related generation algorithm is applied to targets and actions related to education. A workplace-related generation algorithm is applied to targets and actions related to the workplace. In this way, by applying different generation algorithms depending on the target and action categories, more appropriate ideas can be provided.

[0044] The generation unit can improve the accuracy of generation when generating ideas by referring to the user's past idea results. For example, the generation unit improves the accuracy of generation when generating ideas by referring to the user's past idea results. Past idea results include, but are not limited to, successful ideas, unsuccessful ideas, and user reactions, for example. For example, an optimal idea is generated based on ideas generated by the user in the past. The user's past idea results are analyzed to improve the accuracy of generation. The generation algorithm is adjusted by referring to the user's past idea results. In this way, the accuracy of generation is improved by referring to the user's past idea results.

[0045] The generation unit can determine the priority of ideas based on the submission times of targets and actions when generating ideas. For example, the generation unit determines the priority of ideas based on the submission times of targets and actions when generating ideas. The submission times include, but are not limited to, for example, a submission deadline, a timestamp, and prioritization according to time. For example, ideas are generated preferentially for targets and actions whose submission times are close. Ideas are generated later for targets and actions whose submission times are further away. The priority of ideas is gradually adjusted according to the submission times. In this way, by determining the priority of ideas based on the submission times of targets and actions, ideas can be provided in a more appropriate order.

[0046] The generation unit can adjust the order of ideas based on the relevance between targets and actions when generating ideas. For example, the generation unit adjusts the order of ideas based on the relevance between targets and actions when generating ideas. Relevance includes, but is not limited to, common themes, related keywords, and mutual dependencies. For example, ideas are generated preferentially for targets and actions that are highly related. Ideas are generated later for targets and actions that are less related. The order of ideas is adjusted in stages according to the relevance between targets and actions. In this way, by adjusting the order of ideas based on the relevance between targets and actions, more relevant ideas can be provided.

[0047] The generation unit can adjust the use of technical terms in an idea according to the user's level of expertise when generating an idea. For example, the generation unit adjusts the use of technical terms in an idea according to the user's level of expertise when generating an idea. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the generation unit generates an idea that uses a lot of technical terms. If the user does not have expert knowledge, the generation unit generates an idea that avoids technical terms. The generation unit adjusts the use of technical terms in stages according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide ideas that are easier to understand.

[0048] The providing unit can select the optimal delivery method by referring to the user's past feedback when providing an idea. For example, the providing unit selects the optimal delivery method by referring to the user's past feedback when providing an idea. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal delivery method is suggested based on feedback provided by the user in the past. A specific delivery method is suggested with priority based on the user's past feedback. The delivery interface is customized by reflecting the user's feedback. In this way, the optimal delivery method can be suggested by referring to the user's past feedback.

[0049] The providing unit can customize the provided content according to the user's current task when providing an idea. For example, the providing unit customizes the provided content according to the user's current task when providing an idea. The current task includes, but is not limited to, for example, an ongoing project, current work content, task priority, etc. For example, ideas related to the task the user is currently working on are provided preferentially. The most suitable idea is suggested based on the user's current task. The most suitable idea is suggested by referring to the user's past task history. In this way, by customizing the provided content according to the user's current task, more relevant ideas can be provided.

[0050] The providing unit can select the optimal means of providing an idea by taking into consideration device information of the user when providing an idea. For example, the providing unit selects the optimal means of providing an idea by taking into consideration device information of the user when providing an idea. Device information includes, but is not limited to, for example, the type of device, OS, browser, etc. For example, if the user is using a smartphone, a providing method suited to the screen size is suggested. If the user is using a tablet, a providing method optimized for a large screen is suggested. If the user is using a desktop, a providing method including detailed information is suggested. In this way, the optimal means of providing an idea can be selected by taking into consideration device information of the user.

[0051] The providing unit can provide highly relevant ideas preferentially in consideration of the user's geographical location information when providing ideas. For example, the providing unit provides highly relevant ideas preferentially in consideration of the user's geographical location information when providing ideas. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, ideas related to the user's current location are provided preferentially. The most suitable ideas are suggested based on the user's geographical location information. Ideas related to places the user has visited in the past are suggested. In this way, highly relevant ideas can be provided by taking the user's geographical location information into consideration.

[0052] The providing unit can analyze the user's social media activity when providing ideas and provide related ideas. For example, the providing unit analyzes the user's social media activity when providing ideas and provides related ideas. Social media activity includes, but is not limited to, for example, posted content, follower reactions, and engagement. For example, ideas related to places the user has checked in to on social media are provided. Related ideas are provided by analyzing the content of the user's social media posts. Related ideas are provided by taking into account the activities of the user's friends on social media. In this way, highly relevant ideas can be provided by analyzing the user's social media activity.

[0053] The providing unit can customize the delivery method by reflecting the user's past feedback when providing an idea. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing an idea. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal delivery method is proposed based on feedback provided by the user in the past. A specific delivery method is preferentially proposed based on the user's past feedback. The delivery interface is customized by reflecting the user's feedback. In this way, the optimal delivery method can be proposed by reflecting the user's past feedback.

[0054] The feedback receiving unit can select the optimal reception method by referring to the user's past feedback history when receiving feedback. For example, the feedback receiving unit selects the optimal reception method by referring to the user's past feedback history when receiving feedback. The past feedback history includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal reception method is proposed based on feedback provided by the user in the past. A specific reception method is preferentially proposed based on the user's past feedback history. The reception interface is customized by reflecting the user's feedback history. In this way, the optimal reception method can be proposed by referring to the user's past feedback history.

[0055] The feedback receiving unit can customize the received content according to the user's current situation when receiving feedback. For example, the feedback receiving unit customizes the received content according to the user's current situation when receiving feedback. The current situation includes, but is not limited to, for example, ongoing projects, current work content, task priorities, etc. For example, feedback related to the task the user is currently working on is received preferentially. The most appropriate feedback is requested based on the user's current situation. The most appropriate feedback is requested by referring to the user's past situation history. In this way, by customizing the received content according to the user's current situation, it is possible to receive more relevant feedback.

[0056] The feedback receiving unit can select the optimal receiving means by taking into consideration device information of the user when receiving feedback. For example, the feedback receiving unit selects the optimal receiving means by taking into consideration device information of the user when receiving feedback. Device information includes, but is not limited to, for example, the type of device, OS, browser, etc. For example, if the user is using a smartphone, a feedback form tailored to the screen size is provided. If the user is using a tablet, a feedback form optimized for a large screen is provided. If the user is using a desktop, a feedback form including detailed information is provided. In this way, the optimal receiving means can be selected by taking into consideration device information of the user.

[0057] The feedback receiving unit can preferentially receive highly relevant feedback in consideration of the user's geographical location information when receiving feedback. For example, the feedback receiving unit preferentially receives highly relevant feedback in consideration of the user's geographical location information when receiving feedback. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, feedback related to the user's current location is preferentially received. Optimal feedback is obtained based on the user's geographical location information. Feedback related to places the user has visited in the past is obtained. In this way, highly relevant feedback can be received by taking the user's geographical location information into consideration.

[0058] The feedback receiving unit can analyze the user's social media activity and receive relevant feedback when receiving feedback. For example, the feedback receiving unit analyzes the user's social media activity and receives relevant feedback when receiving feedback. Social media activity includes, but is not limited to, for example, the content of posts, follower reactions, and engagement. For example, feedback related to places where the user checked in on social media is received. The content of the user's social media posts is analyzed and relevant feedback is received. Related feedback is received based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive highly relevant feedback.

[0059] The feedback receiving unit can customize the reception method by reflecting the user's past feedback when receiving feedback. The feedback receiving unit, for example, customizes the reception method by reflecting the user's past feedback when receiving feedback. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal reception method is proposed based on feedback provided by the user in the past. A specific reception method is preferentially proposed based on the user's past feedback. The reception interface is customized by reflecting the user's feedback. In this way, the optimal reception method can be proposed by reflecting the user's past feedback.

[0060] The improvement unit can select the optimal improvement method by referring to the user's past feedback when making an improvement. For example, the improvement unit selects the optimal improvement method by referring to the user's past feedback when making an improvement. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal improvement method is proposed based on feedback provided by the user in the past. A specific improvement method is proposed with priority from the user's past feedback. The improvement interface is customized by reflecting the user's feedback. In this way, the optimal improvement method can be proposed by referring to the user's past feedback.

[0061] The improvement unit can customize the improvement content according to the user's current situation when making an improvement. For example, the improvement unit customizes the improvement content according to the user's current situation when making an improvement. The current situation includes, but is not limited to, ongoing projects, current work content, task priorities, etc. For example, the improvement unit prioritizes suggesting improvement methods related to the task the user is currently working on. The unit suggests the optimal improvement method based on the user's current situation. The unit suggests the optimal improvement method by referring to the user's past situation history. In this way, by customizing the improvement content according to the user's current situation, more relevant improvements are possible.

[0062] The improvement unit can select the optimal improvement measure by taking into consideration the user's device information when making an improvement. For example, the improvement unit selects the optimal improvement measure by taking into consideration the user's device information when making an improvement. Device information includes, but is not limited to, the device type, OS, browser, etc. For example, if the user is using a smartphone, an improvement method suited to the screen size is suggested. If the user is using a tablet, an improvement method optimized for a large screen is suggested. If the user is using a desktop, an improvement method including detailed information is suggested. In this way, the optimal improvement measure can be selected by taking into consideration the user's device information.

[0063] When making improvements, the improvement unit can prioritize highly relevant improvements by taking into account the user's geographical location information. For example, when making improvements, the improvement unit prioritizes highly relevant improvements by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the improvement unit prioritizes suggesting improvement methods related to the user's current location. The improvement unit suggests optimal improvement methods based on the user's geographical location information. The improvement unit suggests improvement methods related to places the user has visited in the past. In this way, by taking into account the user's geographical location information, highly relevant improvements are possible.

[0064] The improvement unit can analyze the user's social media activity during improvement and make related improvements. For example, the improvement unit analyzes the user's social media activity during improvement and makes related improvements. Social media activity includes, but is not limited to, the content of posts, follower reactions, and engagement. For example, the improvement unit may suggest improvement methods related to places where the user has checked in on social media. The content of the user's social media posts may be analyzed and related improvement methods may be suggested. Related improvement methods may be suggested based on the activities of the user's friends on social media. In this way, highly relevant improvements can be made by analyzing the user's social media activity.

[0065] The improvement unit can customize the improvement method by reflecting the user's past feedback when making an improvement. For example, the improvement unit customizes the improvement method by reflecting the user's past feedback when making an improvement. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the improvement unit proposes an optimal improvement method based on feedback provided by the user in the past. A specific improvement method is preferentially proposed based on the user's past feedback. The improvement interface is customized by reflecting the user's feedback. In this way, the optimal improvement method can be proposed by reflecting the user's past feedback.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The reception unit can evaluate the relevance between targets and actions based on the user's input and prioritize analysis of highly relevant combinations. For example, if a user inputs "Have men in their 30s exercise for 30 minutes every day," the reception unit evaluates the relevance between this target and action and sends it to the generation unit. This allows the generation unit to generate ideas based on highly relevant information. Furthermore, the reception unit can also refer to the history of targets and actions previously input by the user and suggest similar combinations. This allows the user to efficiently input targets and actions.

[0068] The feedback receiving unit can analyze user feedback in real time and immediately reflect it in the generation unit. For example, if a user provides feedback that a provided idea is "difficult to implement," the feedback receiving unit transmits this information to the generation unit, which then immediately generates a new idea. Furthermore, the feedback receiving unit can evaluate the importance of the feedback based on the content of the user's feedback and prioritize analysis of feedback with high importance. This allows user feedback to be quickly reflected and the system to be improved.

[0069] The generation unit can analyze the user's past behavioral data and generate ideas based on the behavioral patterns. For example, it can generate similar ideas based on behavioral patterns that the user has been successful in the past. It can generate ideas to help the user avoid behavioral patterns that have failed them in the past. It can provide ideas at the optimal time based on the user's behavioral data. This makes it possible to provide more effective ideas by utilizing the user's past behavioral data.

[0070] The providing unit can customize the idea providing method according to the user's current task. For example, it can provide ideas related to the project the user is currently working on with priority. It can suggest the most suitable ideas based on the user's current work content. It can provide related ideas by referring to the user's past task history. This makes it possible to provide more relevant ideas according to the user's current task.

[0071] The providing unit can select the optimal providing means by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit proposes a providing method that matches the screen size. If the user is using a tablet, the providing unit proposes a providing method optimized for a large screen. If the user is using a desktop, the providing unit proposes a providing method that includes detailed information. In this way, the optimal providing means can be selected by taking into consideration the user's device information.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives input of a target and an action from the user. The target includes a specific user group or market segment, and the action includes user behavior, operation, selection, etc. For example, the user inputs the target and action of the service they want the service to perform. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate ideas for behavioral design. The generation AI learns behavioral design approaches using specific machine learning models and neural networks, and can, for example, come up with ideas that take advantage of the user's cognitive biases. For example, it can propose a system that turns exercise into a game and allows users to accumulate points. Step 3: The providing unit provides the idea generated by the generating unit to the user, for example, by notifying the user of the generated idea.

[0074] (Example 2) A behavioral design support system according to an embodiment of the present invention accepts input of targets and actions from a user, analyzes the information, generates behavioral design ideas, and provides them to the user. The behavioral design support system inputs the target and action of a service the user wants the user to perform, and the generation AI analyzes the information to generate behavioral design ideas. These ideas utilize the user's cognitive biases to naturally support behavior. For example, the behavioral design support system inputs the target and action of a service the user wants the user to perform. For example, if a health management app wants users to exercise daily, the user inputs the target and action such as "ask a man in his 30s to exercise for 30 minutes every day." This information is input to the generation AI. The behavioral design support system then uses the generation AI to analyze the input information and generate behavioral design ideas. The generation AI has learned the behavioral design approach and can generate ideas that utilize the user's cognitive biases. For example, an idea such as "game-ify exercise and introduce a system for accumulating points" is generated. The generated idea is provided to the user. The user can then design a service based on this idea to naturally support the user's behavior. For example, in a health management app, by gamifying exercise and introducing a system for accumulating points, users can continue to exercise while having fun. In this way, the behavioral design support system can encourage users to change their behavior by coming up with ideas that take advantage of the user's cognitive biases. In this way, the behavioral design support system can provide ideas that naturally support users' behavior. For example, it can design services that encourage users to take action voluntarily. A generative AI that has learned the behavioral design approach can encourage users to change their behavior by coming up with ideas that take advantage of the user's cognitive biases. For example, in a health management app, by gamifying exercise and introducing a system for accumulating points, users can continue to exercise while having fun.

[0075] The behavioral design support system according to the embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input of a target and an action from a user. The target includes, for example, a specific user group or market segment, but is not limited to these examples. The action includes, for example, a user's behavior, operation, selection, etc., but is not limited to these examples. The receiving unit, for example, inputs the target and action of a service that the user wants the user to perform. The generating unit uses a generating AI to analyze the information received by the receiving unit and generate behavioral design ideas. The generating AI, for example, learns behavioral design approaches using a specific machine learning model or neural network. The generating unit, for example, can generate ideas that utilize the user's cognitive bias. For example, the generating AI can propose a system that gamifies exercise and allows users to accumulate points. The providing unit provides the idea generated by the generating unit to the user. For example, the providing unit notifies the user of the generated idea. This allows the behavioral design support system according to the embodiment to generate and provide behavioral design ideas based on the user's target and action.

[0076] The generation unit can learn behavioral design approaches using generative AI. The generation unit, for example, learns behavioral design approaches using generative AI. The generative AI learns behavioral design approaches using, for example, a specific machine learning model or neural network. The generative AI can learn the theory and practice of behavioral design and generate ideas that utilize the user's cognitive biases. For example, the generative AI generates ideas that encourage the user to take voluntary action based on behavioral design theory. In this way, the generative AI can learn the behavioral design approach and generate more effective ideas.

[0077] The behavioral design support system includes a feedback receiving unit that receives feedback from a user. The feedback receiving unit receives feedback from a user. The feedback includes, but is not limited to, user opinions, evaluations, and comments, for example. The feedback receiving unit inputs feedback on an idea provided by a user, for example. This allows the system to be improved by receiving feedback from the user.

[0078] The behavioral design support system includes an improvement unit that improves the generative AI based on feedback. The improvement unit improves the generative AI based on feedback. Improvements include, but are not limited to, algorithm adjustment and parameter optimization, for example. The improvement unit adjusts the algorithm of the generative AI based on feedback from the user, for example. This improves the generative AI based on feedback, thereby improving the accuracy of the system.

[0079] The generation unit can use the generation AI to propose a system for gamifying exercise and earning points in a health management app. For example, the generation unit uses the generation AI to propose a system for gamifying exercise and earning points in a health management app. Health management apps include, but are not limited to, functions such as exercise records, diet management, and health checkups. Examples of gamifying exercise include, but are not limited to, a point system and a level-up function. For example, the generation AI proposes ideas for users to continue exercising while having fun. This allows users to continue exercising while having fun in the health management app.

[0080] The generation unit can use the generative AI to propose mechanisms that encourage students to learn voluntarily in the field of education. The generation unit uses the generative AI to propose mechanisms that encourage students to learn voluntarily, for example, in the field of education. The field of education includes, but is not limited to, primary education, higher education, and vocational education. Mechanisms that encourage voluntary learning include, but are not limited to, incentive systems and gamification. The generative AI proposes, for example, ideas that allow students to continue learning while having fun. This makes it possible to propose mechanisms that encourage students to learn voluntarily in the field of education.

[0081] The generation unit can use the generative AI to propose behavioral designs for employees to work efficiently in the workplace. The generation unit, for example, uses the generative AI to propose behavioral designs for employees to work efficiently in the workplace. Workplaces include, for example, office environments and remote work environments, but are not limited to these examples. Behavioral designs for working efficiently include, for example, task management, time management, and communication optimization, but are not limited to these examples. The generative AI, for example, proposes ideas for employees to work efficiently. This makes it possible to propose behavioral designs for employees to work efficiently in the workplace.

[0082] The reception unit can estimate the user's emotion and adjust the timing of inputting the target and action based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of inputting the target and action based on the estimated user's emotion. Examples of emotion estimation include, but are not limited to, facial expression recognition, voice analysis, and text analysis. Examples of input timing adjustment include, but are not limited to, timing adjustment according to the user's state and real-time feedback. For example, if the user is feeling stressed, the system prompts the user to input the target and action at a time when the user can relax. If the user is concentrating, the system prompts the user to input the target and action by taking advantage of the user's concentration. If the user is tired, the system prompts the user to input the target and action after a break. In this way, by adjusting the input timing according to the user's emotion, the target and action can be input at a more appropriate time.

[0083] The reception unit can analyze the user's past input history and select the optimal input method. The reception unit, for example, analyzes the user's past input history and selects the optimal input method. The past input history includes, for example, but is not limited to, the type of input data and the analysis algorithm. The selection of the optimal input method includes, for example, but is not limited to, the user's operability and efficiency. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. Targets and actions that the user has previously input are automatically displayed as candidates. The reception unit predicts and suggests the input method to be used in a specific time period based on the user's past input history. In this way, the reception unit can suggest the optimal input method by analyzing the user's past input history.

[0084] The reception unit can perform filtering based on the user's current project and areas of interest when inputting a target and an action. For example, the reception unit performs filtering based on the user's current project and areas of interest when inputting a target and an action. Filtering includes, but is not limited to, selection criteria for highly relevant information and filtering algorithms, for example. For example, targets and actions related to a project the user is currently working on are preferentially displayed. Related targets and actions are suggested based on the user's areas of interest. Optimal targets and actions are suggested by referring to the user's past project history. This makes it possible to suggest related targets and actions based on the user's current project and areas of interest.

[0085] The reception unit can select the optimal input means according to the user's input method when inputting a target and an action. For example, when inputting a target and an action, the reception unit selects the optimal input means according to the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the reception unit preferentially suggests voice input. If the user prefers text input, the reception unit preferentially suggests text input. If the user prefers image input, the reception unit preferentially suggests image input. This improves input efficiency by selecting the optimal input means according to the user's input method.

[0086] The reception unit can estimate the user's emotions and determine the priority of targets and actions to be input based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of targets and actions to be input based on the estimated user emotions. The priority determination may include, but is not limited to, importance evaluation criteria and prioritization algorithms. For example, if the user is feeling stressed, simple targets and actions are preferentially suggested. If the user is relaxed, complex targets and actions are suggested. If the user is in a hurry, targets and actions that can be input quickly are suggested. In this way, by determining the priority of targets and actions according to the user's emotions, input can be performed in a more appropriate order.

[0087] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting a target and an action. For example, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information when inputting a target and an action. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the reception unit prioritizes suggesting targets and actions related to the user's current location. The reception unit suggests optimal targets and actions based on the user's geographical location information. The reception unit suggests targets and actions related to places the user has visited in the past. In this way, highly relevant targets and actions can be suggested by taking the user's geographical location information into consideration.

[0088] The reception unit can analyze the user's social media activity and input related information when the target and action are input. For example, the reception unit analyzes the user's social media activity and inputs related information when the target and action are input. Social media activity includes, but is not limited to, the content of posts, follower reactions, and engagement. For example, the reception unit can suggest targets and actions related to places where the user has checked in on social media. The reception unit can analyze the content of the user's social media posts and suggest related targets and actions. The reception unit can also refer to the activities of the user's friends on social media to suggest related targets and actions. In this way, the reception unit can suggest related targets and actions by analyzing the user's social media activity.

[0089] The reception unit can customize the input method by reflecting the user's past feedback when inputting a target and an action. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting a target and an action. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. A specific input method is preferentially suggested based on the user's past feedback. The input interface is customized by reflecting the user's feedback. In this way, the optimal input method can be suggested by reflecting the user's past feedback.

[0090] The generation unit can estimate the user's emotions and adjust the way in which the idea is expressed based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the way in which the idea is expressed based on the estimated user emotions. Idea expression methods include, but are not limited to, visual expression, text expression, interactive expression, etc. For example, if the user is relaxed, the idea is presented using gentle expression. If the user is in a hurry, the idea is presented using concise and to-the-point expression. If the user is excited, the idea is presented using visually stimulating expression. In this way, by adjusting the way in which the idea is expressed according to the user's emotions, more effective ideas can be provided.

[0091] The generation unit can adjust the level of detail of an idea based on the importance of the target and the action when generating an idea. For example, the generation unit adjusts the level of detail of an idea based on the importance of the target and the action when generating an idea. Adjustments to the level of detail include, but are not limited to, information depth, concreteness, and abstractness. For example, a detailed idea is generated for a target and an action with high importance. A concise idea is generated for a target and an action with low importance. The level of detail of an idea is gradually adjusted according to the importance of the target and the action. This makes it possible to provide more appropriate ideas by adjusting the level of detail of an idea according to the importance of the target and the action.

[0092] The generation unit can apply different generation algorithms depending on the target and action categories when generating ideas. For example, the generation unit applies different generation algorithms depending on the target and action categories when generating ideas. Examples of generation algorithms include, but are not limited to, specific machine learning models and rule-based algorithms. For example, a health-related generation algorithm is applied to targets and actions related to health management. An education-related generation algorithm is applied to targets and actions related to education. A workplace-related generation algorithm is applied to targets and actions related to the workplace. In this way, by applying different generation algorithms depending on the target and action categories, more appropriate ideas can be provided.

[0093] The generation unit can improve the accuracy of generation when generating ideas by referring to the user's past idea results. For example, the generation unit improves the accuracy of generation when generating ideas by referring to the user's past idea results. Past idea results include, but are not limited to, successful ideas, unsuccessful ideas, and user reactions, for example. For example, an optimal idea is generated based on ideas generated by the user in the past. The user's past idea results are analyzed to improve the accuracy of generation. The generation algorithm is adjusted by referring to the user's past idea results. In this way, the accuracy of generation is improved by referring to the user's past idea results.

[0094] The generation unit can estimate the user's emotions and adjust the length of the ideas based on the estimated user emotions. For example, the generation unit estimates the user's emotions and adjusts the length of the ideas based on the estimated user emotions. The length of the ideas includes, but is not limited to, the length of the sentences, the amount of information, and the degree of summary. For example, if the user is in a hurry, a short, to-the-point idea is generated. If the user is relaxed, a longer idea with detailed explanations is generated. If the user is excited, an idea with visually stimulating effects is generated. In this way, by adjusting the length of the ideas according to the user's emotions, more appropriate ideas can be provided.

[0095] The generation unit can determine the priority of ideas based on the submission times of targets and actions when generating ideas. For example, the generation unit determines the priority of ideas based on the submission times of targets and actions when generating ideas. The submission times include, but are not limited to, for example, a submission deadline, a timestamp, and prioritization according to time. For example, ideas are generated preferentially for targets and actions whose submission times are close. Ideas are generated later for targets and actions whose submission times are further away. The priority of ideas is gradually adjusted according to the submission times. In this way, by determining the priority of ideas based on the submission times of targets and actions, ideas can be provided in a more appropriate order.

[0096] The generation unit can adjust the order of ideas based on the relevance between targets and actions when generating ideas. For example, the generation unit adjusts the order of ideas based on the relevance between targets and actions when generating ideas. Relevance includes, but is not limited to, common themes, related keywords, and mutual dependencies. For example, ideas are generated preferentially for targets and actions that are highly related. Ideas are generated later for targets and actions that are less related. The order of ideas is adjusted in stages according to the relevance between targets and actions. In this way, by adjusting the order of ideas based on the relevance between targets and actions, more relevant ideas can be provided.

[0097] The generation unit can adjust the use of technical terms in an idea according to the user's level of expertise when generating an idea. For example, the generation unit adjusts the use of technical terms in an idea according to the user's level of expertise when generating an idea. Expertise levels include, but are not limited to, beginner, intermediate, and advanced. For example, if the user has expert knowledge, the generation unit generates an idea that uses a lot of technical terms. If the user does not have expert knowledge, the generation unit generates an idea that avoids technical terms. The generation unit adjusts the use of technical terms in stages according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide ideas that are easier to understand.

[0098] The providing unit can estimate the user's emotions and adjust the method of providing ideas based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing ideas based on the estimated user emotions. Examples of the providing method include, but are not limited to, email, notification, dashboard, etc. For example, if the user is relaxed, the idea is provided in a gentle manner. If the user is in a hurry, the idea is provided in a concise and to-the-point manner. If the user is excited, the idea is provided in a visually stimulating manner. In this way, by adjusting the method of providing ideas according to the user's emotions, more effective ideas can be provided.

[0099] The providing unit can select the optimal delivery method by referring to the user's past feedback when providing an idea. For example, the providing unit selects the optimal delivery method by referring to the user's past feedback when providing an idea. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal delivery method is suggested based on feedback provided by the user in the past. A specific delivery method is suggested with priority based on the user's past feedback. The delivery interface is customized by reflecting the user's feedback. In this way, the optimal delivery method can be suggested by referring to the user's past feedback.

[0100] The providing unit can customize the provided content according to the user's current task when providing an idea. For example, the providing unit customizes the provided content according to the user's current task when providing an idea. The current task includes, but is not limited to, for example, an ongoing project, current work content, task priority, etc. For example, ideas related to the task the user is currently working on are provided preferentially. The most suitable idea is suggested based on the user's current task. The most suitable idea is suggested by referring to the user's past task history. In this way, by customizing the provided content according to the user's current task, more relevant ideas can be provided.

[0101] The providing unit can select the optimal means of providing an idea by taking into consideration device information of the user when providing an idea. For example, the providing unit selects the optimal means of providing an idea by taking into consideration device information of the user when providing an idea. Device information includes, but is not limited to, for example, the type of device, OS, browser, etc. For example, if the user is using a smartphone, a providing method suited to the screen size is suggested. If the user is using a tablet, a providing method optimized for a large screen is suggested. If the user is using a desktop, a providing method including detailed information is suggested. In this way, the optimal means of providing an idea can be selected by taking into consideration device information of the user.

[0102] The providing unit can estimate the user's emotions and adjust the order in which ideas are provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the order in which ideas are provided based on the estimated user emotions. The order in which ideas are provided includes, but is not limited to, importance, relevance, and the user's state. For example, if the user is feeling stressed, simple ideas are provided preferentially. If the user is relaxed, complex ideas are provided. If the user is in a hurry, ideas that can be implemented quickly are provided preferentially. In this way, by adjusting the order in which ideas are provided according to the user's emotions, ideas can be provided in a more appropriate order.

[0103] The providing unit can provide highly relevant ideas preferentially in consideration of the user's geographical location information when providing ideas. For example, the providing unit provides highly relevant ideas preferentially in consideration of the user's geographical location information when providing ideas. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, ideas related to the user's current location are provided preferentially. The most suitable ideas are suggested based on the user's geographical location information. Ideas related to places the user has visited in the past are suggested. In this way, highly relevant ideas can be provided by taking the user's geographical location information into consideration.

[0104] The providing unit can analyze the user's social media activity when providing ideas and provide related ideas. For example, the providing unit analyzes the user's social media activity when providing ideas and provides related ideas. Social media activity includes, but is not limited to, for example, posted content, follower reactions, and engagement. For example, ideas related to places the user has checked in to on social media are provided. Related ideas are provided by analyzing the content of the user's social media posts. Related ideas are provided by taking into account the activities of the user's friends on social media. In this way, highly relevant ideas can be provided by analyzing the user's social media activity.

[0105] The providing unit can customize the delivery method by reflecting the user's past feedback when providing an idea. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing an idea. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal delivery method is proposed based on feedback provided by the user in the past. A specific delivery method is preferentially proposed based on the user's past feedback. The delivery interface is customized by reflecting the user's feedback. In this way, the optimal delivery method can be proposed by reflecting the user's past feedback.

[0106] The feedback receiving unit can estimate the user's emotion and adjust the feedback receiving method based on the estimated user's emotion. The feedback receiving unit, for example, estimates the user's emotion and adjusts the feedback receiving method based on the estimated user's emotion. Feedback receiving methods include, but are not limited to, online forms, emails, chats, etc. For example, if the user is relaxed, detailed feedback is requested. If the user is in a hurry, brief feedback is requested. If the user is excited, a visually stimulating feedback form is provided. In this way, by adjusting the feedback receiving method according to the user's emotion, more appropriate feedback can be received.

[0107] The feedback receiving unit can select the optimal reception method by referring to the user's past feedback history when receiving feedback. For example, the feedback receiving unit selects the optimal reception method by referring to the user's past feedback history when receiving feedback. The past feedback history includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal reception method is proposed based on feedback provided by the user in the past. A specific reception method is preferentially proposed based on the user's past feedback history. The reception interface is customized by reflecting the user's feedback history. In this way, the optimal reception method can be proposed by referring to the user's past feedback history.

[0108] The feedback receiving unit can customize the received content according to the user's current situation when receiving feedback. For example, the feedback receiving unit customizes the received content according to the user's current situation when receiving feedback. The current situation includes, but is not limited to, for example, ongoing projects, current work content, task priorities, etc. For example, feedback related to the task the user is currently working on is received preferentially. The most appropriate feedback is requested based on the user's current situation. The most appropriate feedback is requested by referring to the user's past situation history. In this way, by customizing the received content according to the user's current situation, it is possible to receive more relevant feedback.

[0109] The feedback receiving unit can select the optimal receiving means by taking into consideration device information of the user when receiving feedback. For example, the feedback receiving unit selects the optimal receiving means by taking into consideration device information of the user when receiving feedback. Device information includes, but is not limited to, for example, the type of device, OS, browser, etc. For example, if the user is using a smartphone, a feedback form tailored to the screen size is provided. If the user is using a tablet, a feedback form optimized for a large screen is provided. If the user is using a desktop, a feedback form including detailed information is provided. In this way, the optimal receiving means can be selected by taking into consideration device information of the user.

[0110] The feedback receiving unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. The feedback receiving unit, for example, estimates the user's emotions and determines the priority of feedback based on the estimated user's emotions. The priority determination may include, but is not limited to, importance evaluation criteria and prioritization algorithms. For example, if the user is feeling stressed, brief feedback is preferentially received. If the user is relaxed, detailed feedback is preferentially received. If the user is in a hurry, feedback that can be provided quickly is preferentially received. In this way, by determining the priority of feedback according to the user's emotions, it is possible to receive feedback in a more appropriate order.

[0111] The feedback receiving unit can preferentially receive highly relevant feedback in consideration of the user's geographical location information when receiving feedback. For example, the feedback receiving unit preferentially receives highly relevant feedback in consideration of the user's geographical location information when receiving feedback. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, feedback related to the user's current location is preferentially received. Optimal feedback is obtained based on the user's geographical location information. Feedback related to places the user has visited in the past is obtained. In this way, highly relevant feedback can be received by taking the user's geographical location information into consideration.

[0112] The feedback receiving unit can analyze the user's social media activity and receive relevant feedback when receiving feedback. For example, the feedback receiving unit analyzes the user's social media activity and receives relevant feedback when receiving feedback. Social media activity includes, but is not limited to, for example, the content of posts, follower reactions, and engagement. For example, feedback related to places where the user checked in on social media is received. The content of the user's social media posts is analyzed and relevant feedback is received. Related feedback is received based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive highly relevant feedback.

[0113] The feedback receiving unit can customize the reception method by reflecting the user's past feedback when receiving feedback. The feedback receiving unit, for example, customizes the reception method by reflecting the user's past feedback when receiving feedback. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal reception method is proposed based on feedback provided by the user in the past. A specific reception method is preferentially proposed based on the user's past feedback. The reception interface is customized by reflecting the user's feedback. In this way, the optimal reception method can be proposed by reflecting the user's past feedback.

[0114] The improvement unit can estimate the user's emotion and adjust the improvement method based on the estimated user's emotion. The improvement unit, for example, estimates the user's emotion and adjusts the improvement method based on the estimated user's emotion. The improvement method includes, for example, but is not limited to, algorithm adjustment and parameter optimization. For example, if the user is relaxed, the improvement method is presented using gentle language. If the user is in a hurry, the improvement method is presented using concise and to-the-point language. If the user is excited, the improvement method is presented using visually stimulating language. This enables more effective improvement by adjusting the improvement method according to the user's emotion.

[0115] The improvement unit can select the optimal improvement method by referring to the user's past feedback when making an improvement. For example, the improvement unit selects the optimal improvement method by referring to the user's past feedback when making an improvement. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the optimal improvement method is proposed based on feedback provided by the user in the past. A specific improvement method is proposed with priority from the user's past feedback. The improvement interface is customized by reflecting the user's feedback. In this way, the optimal improvement method can be proposed by referring to the user's past feedback.

[0116] The improvement unit can customize the improvement content according to the user's current situation when making an improvement. For example, the improvement unit customizes the improvement content according to the user's current situation when making an improvement. The current situation includes, but is not limited to, ongoing projects, current work content, task priorities, etc. For example, the improvement unit prioritizes suggesting improvement methods related to the task the user is currently working on. The unit suggests the optimal improvement method based on the user's current situation. The unit suggests the optimal improvement method by referring to the user's past situation history. In this way, by customizing the improvement content according to the user's current situation, more relevant improvements are possible.

[0117] The improvement unit can select the optimal improvement measure by taking into consideration the user's device information when making an improvement. For example, the improvement unit selects the optimal improvement measure by taking into consideration the user's device information when making an improvement. Device information includes, but is not limited to, the device type, OS, browser, etc. For example, if the user is using a smartphone, an improvement method suited to the screen size is suggested. If the user is using a tablet, an improvement method optimized for a large screen is suggested. If the user is using a desktop, an improvement method including detailed information is suggested. In this way, the optimal improvement measure can be selected by taking into consideration the user's device information.

[0118] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. The improvement unit, for example, estimates the user's emotions and determines the priority of improvements based on the estimated user emotions. The priority determination may include, but is not limited to, importance evaluation criteria and prioritization algorithms. For example, if the user is feeling stressed, a simple improvement method is preferentially suggested. If the user is relaxed, a detailed improvement method is suggested. If the user is in a hurry, a quick improvement method is preferentially suggested. In this way, by determining the priority of improvements according to the user's emotions, improvements can be made in a more appropriate order.

[0119] When making improvements, the improvement unit can prioritize highly relevant improvements by taking into account the user's geographical location information. For example, when making improvements, the improvement unit prioritizes highly relevant improvements by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location information services, for example. For example, the improvement unit prioritizes suggesting improvement methods related to the user's current location. The improvement unit suggests optimal improvement methods based on the user's geographical location information. The improvement unit suggests improvement methods related to places the user has visited in the past. In this way, by taking into account the user's geographical location information, highly relevant improvements are possible.

[0120] The improvement unit can analyze the user's social media activity during improvement and make related improvements. For example, the improvement unit analyzes the user's social media activity during improvement and makes related improvements. Social media activity includes, but is not limited to, the content of posts, follower reactions, and engagement. For example, the improvement unit may suggest improvement methods related to places where the user has checked in on social media. The content of the user's social media posts may be analyzed and related improvement methods may be suggested. Related improvement methods may be suggested based on the activities of the user's friends on social media. In this way, highly relevant improvements can be made by analyzing the user's social media activity.

[0121] The improvement unit can customize the improvement method by reflecting the user's past feedback when making an improvement. For example, the improvement unit customizes the improvement method by reflecting the user's past feedback when making an improvement. Past feedback includes, but is not limited to, user ratings, comments, improvement suggestions, etc. For example, the improvement unit proposes an optimal improvement method based on feedback provided by the user in the past. A specific improvement method is preferentially proposed based on the user's past feedback. The improvement interface is customized by reflecting the user's feedback. In this way, the optimal improvement method can be proposed by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, provision unit, and feedback reception unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive input of a target and an action from a user using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using the generation AI to generate ideas for behavioral design. The provision unit can provide the generated ideas to the user using the output device 40 of the smart device 14. The feedback reception unit can receive feedback from the user using the reception device 38 of the smart device 14. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the generation AI based on the feedback. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and feedback reception unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive input of a target and an action from a user using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to generate ideas for behavioral design. The provision unit can provide the generated ideas to the user using the speaker 240 of the smart glasses 214. The feedback reception unit can receive feedback from the user using the microphone 238 of the smart glasses 214. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the generation AI based on the feedback. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and feedback reception unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive input of a target and an action from a user using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to generate ideas for behavioral design. The provision unit can provide the generated ideas to the user using the speaker 240 of the headset-type terminal 314. The feedback reception unit can receive feedback from the user using the microphone 238 of the headset-type terminal 314. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the generation AI based on the feedback. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and feedback reception unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive input of a target and an action from a user using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to generate ideas for behavioral design. The provision unit can provide the generated ideas to the user using the speaker 240 of the robot 414. The feedback reception unit can receive feedback from the user using the microphone 238 of the robot 414. The improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the generation AI based on the feedback.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The reception unit can evaluate the relevance between targets and actions based on the user's input and prioritize analysis of highly relevant combinations. For example, if a user inputs "Have men in their 30s exercise for 30 minutes every day," the reception unit evaluates the relevance between this target and action and sends it to the generation unit. This allows the generation unit to generate ideas based on highly relevant information. Furthermore, the reception unit can also refer to the history of targets and actions previously input by the user and suggest similar combinations. This allows the user to efficiently input targets and actions.

[0124] The generator can estimate the user's emotions and adjust the idea generation algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the generator will prioritize generating ideas that have a relaxing effect. If the user is excited, the generator will generate challenging ideas. If the user is tired, the generator will generate simple and easy-to-implement ideas. This makes it possible to provide optimal ideas according to the user's emotions.

[0125] The feedback receiving unit can analyze user feedback in real time and immediately reflect it in the generation unit. For example, if a user provides feedback that a provided idea is "difficult to implement," the feedback receiving unit transmits this information to the generation unit, which then immediately generates a new idea. Furthermore, the feedback receiving unit can evaluate the importance of the feedback based on the content of the user's feedback and prioritize analysis of feedback with high importance. This allows user feedback to be quickly reflected and the system to be improved.

[0126] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. For example, if the user is feeling stressed, a simple and effective improvement method is preferentially suggested. If the user is relaxed, a detailed improvement method is suggested. If the user is in a hurry, a quick improvement method is suggested preferentially. In this way, it is possible to provide the optimal improvement method according to the user's emotions.

[0127] The generation unit can analyze the user's past behavioral data and generate ideas based on the behavioral patterns. For example, it can generate similar ideas based on behavioral patterns that the user has been successful in the past. It can generate ideas to help the user avoid behavioral patterns that have failed them in the past. It can provide ideas at the optimal time based on the user's behavioral data. This makes it possible to provide more effective ideas by utilizing the user's past behavioral data.

[0128] The generation unit can estimate the user's emotions and adjust the way ideas are expressed based on the estimated user emotions. For example, if the user is relaxed, the idea is presented in a gentle manner. If the user is in a hurry, the idea is presented in a concise and to-the-point manner. If the user is excited, the idea is presented in a visually stimulating manner. In this way, by adjusting the way ideas are expressed according to the user's emotions, more effective ideas can be provided.

[0129] The providing unit can customize the idea providing method according to the user's current task. For example, it can provide ideas related to the project the user is currently working on with priority. It can suggest the most suitable ideas based on the user's current work content. It can provide related ideas by referring to the user's past task history. This makes it possible to provide more relevant ideas according to the user's current task.

[0130] The providing unit can estimate the user's emotions and adjust the order in which ideas are provided based on the estimated user's emotions. For example, if the user is feeling stressed, simple ideas are provided preferentially. If the user is relaxed, complex ideas are provided. If the user is in a hurry, ideas that can be quickly implemented are provided preferentially. In this way, by adjusting the order in which ideas are provided according to the user's emotions, it is possible to provide ideas in a more appropriate order.

[0131] The providing unit can select the optimal providing means by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit proposes a providing method that matches the screen size. If the user is using a tablet, the providing unit proposes a providing method optimized for a large screen. If the user is using a desktop, the providing unit proposes a providing method that includes detailed information. In this way, the optimal providing means can be selected by taking into consideration the user's device information.

[0132] The feedback receiving unit can estimate the user's emotions and adjust the feedback receiving method based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback is requested. If the user is in a hurry, brief feedback is requested. If the user is excited, a visually stimulating feedback form is provided. In this way, by adjusting the feedback receiving method according to the user's emotions, more appropriate feedback can be received.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The reception unit receives input of a target and an action from the user. The target includes a specific user group or market segment, and the action includes user behavior, operation, selection, etc. For example, the user inputs the target and action of the service they want the service to perform. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate ideas for behavioral design. The generation AI learns behavioral design approaches using specific machine learning models and neural networks, and can, for example, come up with ideas that take advantage of the user's cognitive biases. For example, it can propose a system that turns exercise into a game and allows users to accumulate points. Step 3: The providing unit provides the idea generated by the generating unit to the user, for example, by notifying the user of the generated idea.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 receiving unit that receives input of a target and an action from a user; a generation unit that analyzes the information received by the reception unit and generates ideas for behavioral design; a providing unit that provides the idea generated by the generating unit to a user; Equipped with A system characterized by:

2. The generation unit Learning behavioral design approaches using generative AI 2. The system of claim 1.

3. A feedback receiving unit is provided to receive feedback from users.

2. The system of claim 1.

4. Equipped with an improvement department that improves the generated AI based on feedback 3. The system of claim 2.

5. The generation unit Proposing a system for gamifying exercise and earning points in a health management app using generative AI 2. The system of claim 1.

6. The generation unit In the field of education, we propose mechanisms to encourage students to learn independently using generative AI.

2. The system of claim 1.

7. The generation unit Using generative AI to propose behavioral designs that help employees work efficiently in the workplace 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the target and action input timing based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

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