system

Abstract

The system according to this embodiment aims to provide individualized feedback and intervention plans. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives feedback from the user. The analysis unit analyzes the feedback received by the reception unit. The generation unit generates feedback and an intervention plan based on the results analyzed by the analysis unit. The provision unit provides the feedback and intervention plan generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to provide individualized feedback and intervention plans, and there are problems in maintaining user motivation and improving self-affirmation.

[0005] The system according to the embodiment aims to provide individualized feedback and intervention plans.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives feedback from the user. The analysis unit analyzes the feedback received by the reception unit. The generation unit generates feedback and an intervention plan based on the results analyzed by the analysis unit. The provision unit provides the feedback and intervention plan generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide individualized feedback and intervention plans. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The support system according to an embodiment of the present invention is a system that solves the problems of people who have difficulty setting goals, people who cannot sustain effort toward achieving goals even if they have them, and people who have low self-esteem and cannot fully utilize their own abilities. This support system uses generative AI technology to create individualized feedback and intervention plans for the user. This system receives feedback from the user (motivation, tasks achieved, self-evaluation) and, based on that, presents support optimized for the individual's condition. It learns from past data and current inputs to learn the user's behavior patterns and fluctuations in motivation. This enables responses tailored to individual needs and effectively addresses the challenges that individuals face. For example, the user inputs feedback into the system. For example, information such as the current motivation level, tasks achieved, and self-evaluation is input. This information is analyzed by the generative AI. Next, the generative AI learns from past data and current inputs to understand the user's behavior patterns and fluctuations in motivation. This enables responses tailored to individual needs. The generative AI creates feedback and intervention plans optimized for the user's condition. For example, if the user reports low motivation, the generative AI identifies the cause and proposes specific countermeasures. It also provides positive feedback on tasks achieved by the user to support increased self-esteem. This system makes it easier for users to overcome difficulties in setting and achieving their own goals. Furthermore, the personalized support provided by the generative AI improves user productivity and reduces workplace dissatisfaction. For example, if a user reports that they "failed to complete today's tasks," the generative AI analyzes the reasons and provides specific advice for completing the tasks next time. Also, if a user reports that they "have low self-esteem," the generative AI reviews past successes and provides positive feedback to support increased self-esteem. In this way, systems utilizing generative AI can maximize individual potential by sustaining efforts toward goal achievement and boosting self-esteem.This allows the support system to analyze user feedback and provide personalized feedback and intervention plans, thereby effectively resolving user issues.

[0029] The support system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives feedback from the user. User feedback includes, but is not limited to, examples of motivation level, completed tasks, and self-evaluation. The reception unit stores the user's input feedback in a database, for example. The reception unit can also convert voice feedback into text data using speech recognition technology if the user provides it verbally. For example, the reception unit allows the user to input feedback through a smartphone application. The analysis unit analyzes the feedback received by the reception unit. The analysis unit learns the user's behavior patterns and motivation fluctuations based on past data and current input, for example. The analysis unit analyzes the user's behavior patterns using, for example, a machine learning algorithm. The analysis unit can also analyze the content of the feedback using natural language processing technology. For example, the analysis unit estimates emotions from the user's feedback and understands motivation fluctuations. The generation unit generates feedback and intervention plans based on the results analyzed by the analysis unit. The generation unit generates feedback and intervention plans optimized for the user's state using, for example, a generation AI. The generation unit, for example, identifies the cause of low motivation if the user reports it, and proposes specific countermeasures. The generation unit can also provide positive feedback on tasks the user has completed, supporting the enhancement of self-esteem. For example, the generation unit reviews the user's past successes and generates positive feedback. The delivery unit provides the user with the feedback and intervention plan generated by the generation unit. The delivery unit notifies the user of the generated feedback and intervention plan via their smartphone app, for example. The delivery unit can also send the generated feedback and intervention plan via email. For example, the delivery unit provides the feedback and intervention plan in the format preferred by the user. In this way, the support system according to the embodiment can effectively solve the user's problems by analyzing the user's feedback and providing personalized feedback and intervention plans.

[0030] The reception unit can receive information about the user's motivation level, completed tasks, and self-assessment. For example, the reception unit can store the user's entered motivation level in a database. For example, the reception unit can receive the user's completed tasks in a list format. The reception unit can also allow the user to enter their self-assessment in a score format. For example, the reception unit can allow the user to enter their motivation level through a smartphone app. This allows for more accurate analysis and feedback by receiving detailed user information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's entered motivation level into an AI and have the AI ​​perform an analysis of the motivation level.

[0031] The analysis unit can learn user behavior patterns and motivation fluctuations based on past data and current input. For example, the analysis unit can learn user behavior patterns based on past feedback history. For example, the analysis unit can analyze user behavior logs to identify behavior patterns. The analysis unit can also learn motivation fluctuations based on current input data. For example, the analysis unit can analyze real-time user feedback to understand motivation fluctuations. By learning user behavior patterns and motivation fluctuations, it is possible to provide personalized feedback and intervention plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past feedback history into AI and have the AI ​​perform behavior pattern learning.

[0032] The generation unit can generate feedback and intervention plans based on the user's state. For example, the generation unit can generate feedback based on the user's motivation level. For instance, if the user reports low motivation, the generation unit can identify the cause and propose specific countermeasures. The generation unit can also provide positive feedback on tasks the user has completed, supporting the enhancement of self-esteem. For example, the generation unit can review the user's past successes and generate positive feedback. This allows for the effective addressing of the user's challenges by generating feedback and intervention plans optimized for the user's state. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's motivation level into a generation AI and have the generation AI generate the feedback.

[0033] The service provider can provide the generated feedback and intervention plan to the user. For example, the service provider can notify the user of the generated feedback and intervention plan via their smartphone app. For example, the service provider can also send the generated feedback and intervention plan via email. The service provider can also display the generated feedback and intervention plan on a dashboard. For example, the service provider can provide the feedback and intervention plan in the format preferred by the user. In this way, by providing the user with the generated feedback and intervention plan, the service provider helps the user solve their problems. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated feedback and intervention plan into AI and have the AI ​​perform the delivery to the user.

[0034] The reception desk can analyze a user's past feedback history and select the optimal reception method. For example, the reception desk may prioritize suggesting feedback methods (text, voice, etc.) that the user has preferred in the past. For example, the reception desk may analyze the time periods when a user has previously provided feedback and accept it during those times. The reception desk can also set an appropriate reception frequency based on how often a user has previously provided feedback. For example, the reception desk may store and analyze a user's past feedback history in a database. This allows the reception desk to select the optimal reception method by analyzing the user's past feedback history. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk may input the user's past feedback history into an AI and have the AI ​​select the optimal reception method.

[0035] The reception unit can filter feedback based on the user's current life circumstances and areas of interest. For example, the reception unit can prioritize receiving feedback related to a project the user is currently working on. For example, the reception unit can receive feedback that is relevant to the user's current life circumstances (work, family, etc.). The reception unit can also prioritize receiving feedback related to the user's areas of interest (hobbies, interests, etc.). For example, the reception unit can store the user's current life circumstances and areas of interest in a database and perform filtering. This allows the reception unit to receive more relevant feedback by filtering based on the user's current life circumstances and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current life circumstances and areas of interest into an AI and have the AI ​​perform the filtering.

[0036] The reception desk can prioritize receiving highly relevant feedback based on the user's geographical location when receiving feedback. For example, if the user is in a specific location, the reception desk will prioritize receiving feedback related to that location. For example, if the user is traveling, the reception desk will prioritize receiving feedback related to the travel destination. The reception desk can also prioritize receiving feedback related to the workplace if the user is at work. For example, the reception desk can store the user's geographical location information in a database and prioritize receiving highly relevant feedback. This allows for the receipt of more appropriate feedback by prioritizing highly relevant feedback based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into an AI and have the AI ​​perform the task of receiving highly relevant feedback.

[0037] The reception unit can analyze a user's social media activity and receive relevant feedback when receiving feedback. For example, the reception unit can receive relevant feedback based on what the user has shared on social media. For example, the reception unit can receive feedback related to accounts the user follows on social media. The reception unit can also receive feedback related to groups the user participates in on social media. For example, the reception unit can store and analyze the user's social media activity in a database. This allows the reception unit to receive relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​perform the task of receiving relevant feedback.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the feedback during the analysis. For example, the analysis unit performs a detailed analysis for important feedback. For example, the analysis unit performs a concise analysis for general feedback. The analysis unit can also perform a rapid analysis for urgent feedback. For example, the analysis unit stores the importance of the feedback in a database and adjusts the level of detail of the analysis. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the feedback into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0039] The analysis unit can apply different analysis algorithms depending on the feedback category during analysis. For example, the analysis unit can apply an emotion analysis algorithm to feedback related to motivation. For example, the analysis unit can apply a task analysis algorithm to feedback related to completed tasks. The analysis unit can also apply a self-assessment analysis algorithm to feedback related to self-assessment. For example, the analysis unit can store the feedback categories in a database and apply different analysis algorithms. This allows for more appropriate analysis results by applying different analysis algorithms depending on the feedback category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the feedback categories into the AI ​​and have the AI ​​perform the application of the analysis algorithms.

[0040] The analysis unit can determine the priority of analysis based on the timing of feedback submissions during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted feedback. For example, the analysis unit may prioritize the analysis of urgent feedback. The analysis unit may also prioritize the analysis of regularly submitted feedback. For example, the analysis unit may store the timing of feedback submissions in a database and determine the analysis priority. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the timing of feedback submissions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the timing of feedback submissions into AI and have AI perform the determination of analysis priority.

[0041] The analysis unit can adjust the order of analysis based on the relevance of the feedback during the analysis. For example, the analysis unit may prioritize analyzing highly relevant feedback. For example, it may postpone analyzing less relevant feedback. The analysis unit can also adjust the order of analysis according to the content of the feedback. For example, the analysis unit may store the relevance of the feedback in a database and adjust the order of analysis. By adjusting the order of analysis based on the relevance of the feedback, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the feedback into AI and have AI perform the adjustment of the order of analysis.

[0042] The generation unit can adjust the level of detail of the generated feedback based on its importance. For example, it can generate detailed feedback for important feedback, or concise feedback for general feedback. It can also generate feedback quickly for urgent feedback. For example, it can store the importance of the feedback in a database and adjust the level of detail of the generated feedback. This allows for the provision of more appropriate feedback and intervention plans by adjusting the level of detail of the generated feedback based on its importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the feedback into the generation AI and have the generation AI adjust the level of detail of the generated feedback.

[0043] The generation unit can apply different generation algorithms depending on the feedback category during generation. For example, the generation unit can apply an emotion generation algorithm to motivation-related feedback. For example, the generation unit can apply a task generation algorithm to feedback on completed tasks. The generation unit can also apply a self-assessment generation algorithm to self-assessment feedback. For example, the generation unit can store the feedback categories in a database and apply different generation algorithms. This allows for the provision of more appropriate feedback and intervention plans by applying different generation algorithms depending on the feedback category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the feedback categories into a generation AI and have the generation AI perform the application of the generation algorithm.

[0044] The generation unit can determine the generation priority based on the timing of feedback submission during the generation process. For example, the generation unit may prioritize generating recently submitted feedback. For example, the generation unit may prioritize generating urgent feedback. The generation unit can also prioritize generating regularly submitted feedback. For example, the generation unit may store the timing of feedback submission in a database and determine the generation priority. This allows for the provision of more appropriate feedback and intervention plans by determining the generation priority based on the timing of feedback submission. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit may input the timing of feedback submission into a generation AI and have the generation AI determine the generation priority.

[0045] The generation unit can adjust the order of generation based on the relevance of the feedback during generation. For example, the generation unit can prioritize generating highly relevant feedback. For example, the generation unit can postpone generating less relevant feedback. The generation unit can also adjust the order of generation according to the content of the feedback. For example, the generation unit can store the relevance of the feedback in a database and adjust the order of generation. This allows for the provision of more appropriate feedback and intervention plans by adjusting the order of generation based on the relevance of the feedback. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the feedback into a generation AI and have the generation AI perform the adjustment of the generation order.

[0046] The delivery unit can adjust the level of detail provided based on the importance of the feedback. For example, it can provide detailed feedback for important feedback, and concise feedback for general feedback. It can also provide rapid feedback for urgent feedback. For example, the delivery unit can store the importance of the feedback in a database and adjust the level of detail based on that database. This allows for the provision of more appropriate feedback and intervention plans by adjusting the level of detail based on the importance of the feedback. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the importance of the feedback into the AI ​​and have the AI ​​adjust the level of detail.

[0047] The service provider can apply different service provision algorithms depending on the category of feedback at the time of provision. For example, the service provider can apply an emotion service provision algorithm to feedback related to motivation. For example, the service provider can apply a task service provision algorithm to feedback related to completed tasks. The service provider can also apply a self-assessment service provision algorithm to feedback related to self-assessment. For example, the service provider can store the feedback categories in a database and apply different service provision algorithms. This allows for the provision of more appropriate feedback and intervention plans by applying different service provision algorithms depending on the feedback category. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the feedback categories into AI and have the AI ​​perform the application of the service provision algorithms.

[0048] The service provider can determine the priority of feedback delivery based on the timing of feedback submission. For example, the service provider may prioritize recently submitted feedback. For example, the service provider may prioritize urgent feedback. The service provider may also prioritize regularly submitted feedback. For example, the service provider may store the timing of feedback submission in a database and determine the priority of delivery. This allows for the provision of more appropriate feedback and intervention plans by prioritizing delivery based on the timing of feedback submission. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the timing of feedback submission into AI and have the AI ​​perform the determination of delivery priorities.

[0049] The delivery unit can adjust the order of delivery based on the relevance of the feedback. For example, the delivery unit may prioritize providing highly relevant feedback. For example, it may postpone providing less relevant feedback. The delivery unit can also adjust the order of delivery according to the content of the feedback. For example, the delivery unit may store the relevance of the feedback in a database and adjust the order of delivery. This allows for the provision of more appropriate feedback and intervention plans by adjusting the order of delivery based on the relevance of the feedback. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit may input the relevance of the feedback into AI and have AI perform the adjustment of the order of delivery.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception desk can monitor the user's current health status when receiving user feedback and adjust the feedback collection method according to that status. For example, if the user is feeling fatigued, feedback can be collected in the form of simple questions. If the user is in good health, more detailed feedback can be requested. Furthermore, if the user is ill or injured, feedback collection can be temporarily suspended and resumed after recovery. This allows for flexible feedback collection tailored to the user's health status, reducing the burden on the user.

[0052] The analytics unit can analyze user feedback by considering not only the user's past feedback history but also their social media activity and online behavior history. For example, it can include positive posts shared by users on social media in its analysis. It can also utilize information about keywords users searched online and websites they visited. Furthermore, it can incorporate the user's activity history in online communities into the analysis. This allows for a more comprehensive understanding of user behavior patterns and provides more accurate analysis results.

[0053] The generation unit can take into account the user's current living environment and lifestyle when generating feedback and intervention plans based on user feedback. For example, if the user is working from home, it can provide feedback and intervention plans suitable for working from home. If the user has a busy schedule, it can propose intervention plans that can be implemented in a short amount of time. Furthermore, if the user lives with family, it can provide feedback that encourages cooperation with family members. This allows for the provision of support tailored to the user's living environment and enables effective intervention.

[0054] The system can take into account the user's device usage when providing generated feedback and intervention plans to the user. For example, if the user primarily uses a smartphone, the feedback can be provided in a format optimized for smartphones. If the user uses a computer, the feedback can be displayed in a format suitable for the computer screen. Furthermore, if the user uses a wearable device, it is possible to send notifications to that device. This enables flexible feedback delivery tailored to the user's device usage, thereby improving user convenience.

[0055] The service provider can deliver generated feedback and intervention plans to users in a format tailored to their learning style and preferences. For example, if a user prefers visual information, feedback can be provided using graphs and charts. If a user prefers text-based information, detailed written feedback can be provided. Furthermore, if a user prefers audio feedback, voice messages can be sent. This allows for flexible feedback delivery tailored to the user's learning style and preferences, deepening their understanding.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives feedback from users. This feedback includes motivation levels, completed tasks, and self-assessments. The reception desk stores the user-entered feedback in a database. It can also convert voice feedback into text data using speech recognition technology. For example, users can enter feedback via a smartphone app. Step 2: The analysis unit analyzes the feedback received by the reception unit. The analysis unit learns user behavior patterns and motivation fluctuations based on past data and current input. Using machine learning algorithms and natural language processing techniques, it analyzes user behavior patterns and feedback content to estimate emotions. Step 3: The generation unit generates feedback and intervention plans based on the results analyzed by the analysis unit. The generation unit uses generation AI to generate feedback and intervention plans optimized for the user's condition. For example, for a user who reports low motivation, it identifies the cause and proposes specific countermeasures. It also provides positive feedback for completed tasks to support increased self-esteem. Step 4: The provider unit provides the user with the feedback and intervention plan generated by the generator unit. The provider unit notifies the user of the generated feedback and intervention plan via their smartphone app. It can also send it via email. The provider unit provides the feedback and intervention plan in the format preferred by the user.

[0058] (Example of form 2) The support system according to an embodiment of the present invention is a system that solves the problems of people who have difficulty setting goals, people who cannot sustain effort toward achieving goals even if they have them, and people who have low self-esteem and cannot fully utilize their own abilities. This support system uses generative AI technology to create individualized feedback and intervention plans for the user. This system receives feedback from the user (motivation, tasks achieved, self-evaluation) and, based on that, presents support optimized for the individual's condition. It learns from past data and current inputs to learn the user's behavior patterns and fluctuations in motivation. This enables responses tailored to individual needs and effectively addresses the challenges that individuals face. For example, the user inputs feedback into the system. For example, information such as the current motivation level, tasks achieved, and self-evaluation is input. This information is analyzed by the generative AI. Next, the generative AI learns from past data and current inputs to understand the user's behavior patterns and fluctuations in motivation. This enables responses tailored to individual needs. The generative AI creates feedback and intervention plans optimized for the user's condition. For example, if the user reports low motivation, the generative AI identifies the cause and proposes specific countermeasures. It also provides positive feedback on tasks achieved by the user to support increased self-esteem. This system makes it easier for users to overcome difficulties in setting and achieving their own goals. Furthermore, the personalized support provided by the generative AI improves user productivity and reduces workplace dissatisfaction. For example, if a user reports that they "failed to complete today's tasks," the generative AI analyzes the reasons and provides specific advice for completing the tasks next time. Also, if a user reports that they "have low self-esteem," the generative AI reviews past successes and provides positive feedback to support increased self-esteem. In this way, systems utilizing generative AI can maximize individual potential by sustaining efforts toward goal achievement and boosting self-esteem.This allows the support system to analyze user feedback and provide personalized feedback and intervention plans, thereby effectively resolving user issues.

[0059] The support system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives feedback from the user. User feedback includes, but is not limited to, examples of motivation level, completed tasks, and self-evaluation. The reception unit stores the user's input feedback in a database, for example. The reception unit can also convert voice feedback into text data using speech recognition technology if the user provides it verbally. For example, the reception unit allows the user to input feedback through a smartphone application. The analysis unit analyzes the feedback received by the reception unit. The analysis unit learns the user's behavior patterns and motivation fluctuations based on past data and current input, for example. The analysis unit analyzes the user's behavior patterns using, for example, a machine learning algorithm. The analysis unit can also analyze the content of the feedback using natural language processing technology. For example, the analysis unit estimates emotions from the user's feedback and understands motivation fluctuations. The generation unit generates feedback and intervention plans based on the results analyzed by the analysis unit. The generation unit generates feedback and intervention plans optimized for the user's state using, for example, a generation AI. The generation unit, for example, identifies the cause of low motivation if the user reports it, and proposes specific countermeasures. The generation unit can also provide positive feedback on tasks the user has completed, supporting the enhancement of self-esteem. For example, the generation unit reviews the user's past successes and generates positive feedback. The delivery unit provides the user with the feedback and intervention plan generated by the generation unit. The delivery unit notifies the user of the generated feedback and intervention plan via their smartphone app, for example. The delivery unit can also send the generated feedback and intervention plan via email. For example, the delivery unit provides the feedback and intervention plan in the format preferred by the user. In this way, the support system according to the embodiment can effectively solve the user's problems by analyzing the user's feedback and providing personalized feedback and intervention plans.

[0060] The reception unit can receive information about the user's motivation level, completed tasks, and self-assessment. For example, the reception unit can store the user's entered motivation level in a database. For example, the reception unit can receive the user's completed tasks in a list format. The reception unit can also allow the user to enter their self-assessment in a score format. For example, the reception unit can allow the user to enter their motivation level through a smartphone app. This allows for more accurate analysis and feedback by receiving detailed user information. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's entered motivation level into an AI and have the AI ​​perform an analysis of the motivation level.

[0061] The analysis unit can learn user behavior patterns and motivation fluctuations based on past data and current input. For example, the analysis unit can learn user behavior patterns based on past feedback history. For example, the analysis unit can analyze user behavior logs to identify behavior patterns. The analysis unit can also learn motivation fluctuations based on current input data. For example, the analysis unit can analyze real-time user feedback to understand motivation fluctuations. By learning user behavior patterns and motivation fluctuations, it is possible to provide personalized feedback and intervention plans. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past feedback history into AI and have the AI ​​perform behavior pattern learning.

[0062] The generation unit can generate feedback and intervention plans based on the user's state. For example, the generation unit can generate feedback based on the user's motivation level. For instance, if the user reports low motivation, the generation unit can identify the cause and propose specific countermeasures. The generation unit can also provide positive feedback on tasks the user has completed, supporting the enhancement of self-esteem. For example, the generation unit can review the user's past successes and generate positive feedback. This allows for the effective addressing of the user's challenges by generating feedback and intervention plans optimized for the user's state. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the user's motivation level into a generation AI and have the generation AI generate the feedback.

[0063] The service provider can provide the generated feedback and intervention plan to the user. For example, the service provider can notify the user of the generated feedback and intervention plan via their smartphone app. For example, the service provider can also send the generated feedback and intervention plan via email. The service provider can also display the generated feedback and intervention plan on a dashboard. For example, the service provider can provide the feedback and intervention plan in the format preferred by the user. In this way, by providing the user with the generated feedback and intervention plan, the service provider helps the user solve their problems. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the generated feedback and intervention plan into AI and have the AI ​​perform the delivery to the user.

[0064] The reception unit can estimate the user's emotions and adjust the timing of feedback reception based on the estimated emotions. For example, if the user is stressed, the reception unit will accept feedback during a time when the user can relax. For example, if the user is highly motivated, the reception unit will accept feedback immediately and respond quickly. Also, if the user is tired, the reception unit can accept feedback after they have rested. For example, the reception unit can monitor the user's emotions in real time and accept feedback at the appropriate time. This allows for more appropriate timing of feedback reception by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user emotion data into AI and have the AI ​​adjust the timing of feedback reception.

[0065] The reception desk can analyze a user's past feedback history and select the optimal reception method. For example, the reception desk may prioritize suggesting feedback methods (text, voice, etc.) that the user has preferred in the past. For example, the reception desk may analyze the time periods when a user has previously provided feedback and accept it during those times. The reception desk can also set an appropriate reception frequency based on how often a user has previously provided feedback. For example, the reception desk may store and analyze a user's past feedback history in a database. This allows the reception desk to select the optimal reception method by analyzing the user's past feedback history. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk may input the user's past feedback history into an AI and have the AI ​​select the optimal reception method.

[0066] The reception unit can filter feedback based on the user's current life circumstances and areas of interest. For example, the reception unit can prioritize receiving feedback related to a project the user is currently working on. For example, the reception unit can receive feedback that is relevant to the user's current life circumstances (work, family, etc.). The reception unit can also prioritize receiving feedback related to the user's areas of interest (hobbies, interests, etc.). For example, the reception unit can store the user's current life circumstances and areas of interest in a database and perform filtering. This allows the reception unit to receive more relevant feedback by filtering based on the user's current life circumstances and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's current life circumstances and areas of interest into an AI and have the AI ​​perform the filtering.

[0067] The reception desk can estimate the user's emotions and determine the priority of feedback to receive based on the estimated emotions. For example, if the user is highly motivated, the reception desk will prioritize important feedback. For example, if the user is less motivated, the reception desk will prioritize simple feedback. The reception desk can also prioritize relaxing feedback if the user is stressed. For example, the reception desk can monitor the user's emotions in real time and receive feedback with appropriate priorities. This allows for the prioritization of more appropriate feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI and have the AI ​​determine the priority of feedback.

[0068] The reception desk can prioritize receiving highly relevant feedback based on the user's geographical location when receiving feedback. For example, if the user is in a specific location, the reception desk will prioritize receiving feedback related to that location. For example, if the user is traveling, the reception desk will prioritize receiving feedback related to the travel destination. The reception desk can also prioritize receiving feedback related to the workplace if the user is at work. For example, the reception desk can store the user's geographical location information in a database and prioritize receiving highly relevant feedback. This allows for the receipt of more appropriate feedback by prioritizing highly relevant feedback based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location information into an AI and have the AI ​​perform the task of receiving highly relevant feedback.

[0069] The reception unit can analyze a user's social media activity and receive relevant feedback when receiving feedback. For example, the reception unit can receive relevant feedback based on what the user has shared on social media. For example, the reception unit can receive feedback related to accounts the user follows on social media. The reception unit can also receive feedback related to groups the user participates in on social media. For example, the reception unit can store and analyze the user's social media activity in a database. This allows the reception unit to receive relevant feedback by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity into AI and have the AI ​​perform the task of receiving relevant feedback.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. The analysis unit can also emphasize positive analysis results if the user is stressed. For example, the analysis unit monitors the user's emotions in real time and provides analysis results in an appropriate presentation. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the feedback during the analysis. For example, the analysis unit performs a detailed analysis for important feedback. For example, the analysis unit performs a concise analysis for general feedback. The analysis unit can also perform a rapid analysis for urgent feedback. For example, the analysis unit stores the importance of the feedback in a database and adjusts the level of detail of the analysis. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the feedback. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the feedback into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the feedback category during analysis. For example, the analysis unit can apply an emotion analysis algorithm to feedback related to motivation. For example, the analysis unit can apply a task analysis algorithm to feedback related to completed tasks. The analysis unit can also apply a self-assessment analysis algorithm to feedback related to self-assessment. For example, the analysis unit can store the feedback categories in a database and apply different analysis algorithms. This allows for more appropriate analysis results by applying different analysis algorithms depending on the feedback category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the feedback categories into the AI ​​and have the AI ​​perform the application of the analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will perform a short, concise analysis. For example, if the user is relaxed, the analysis unit will perform a detailed analysis. Furthermore, if the user is stressed, the analysis unit can also perform an analysis that emphasizes positive elements. For example, the analysis unit can monitor the user's emotions in real time and provide analysis results of an appropriate length. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the length of the analysis.

[0074] The analysis unit can determine the priority of analysis based on the timing of feedback submissions during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted feedback. For example, the analysis unit may prioritize the analysis of urgent feedback. The analysis unit may also prioritize the analysis of regularly submitted feedback. For example, the analysis unit may store the timing of feedback submissions in a database and determine the analysis priority. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the timing of feedback submissions. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the timing of feedback submissions into AI and have AI perform the determination of analysis priority.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the feedback during the analysis. For example, the analysis unit may prioritize analyzing highly relevant feedback. For example, it may postpone analyzing less relevant feedback. The analysis unit can also adjust the order of analysis according to the content of the feedback. For example, the analysis unit may store the relevance of the feedback in a database and adjust the order of analysis. By adjusting the order of analysis based on the relevance of the feedback, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input the relevance of the feedback into AI and have AI perform the adjustment of the order of analysis.

[0076] The generation unit can estimate the user's emotions and adjust the way it presents the feedback and intervention plan based on the estimated emotions. For example, if the user is relaxed, the generation unit generates detailed feedback and an intervention plan. For example, if the user is in a hurry, the generation unit generates concise feedback and an intervention plan. The generation unit can also generate feedback and an intervention plan that emphasizes positive elements if the user is stressed. For example, the generation unit can monitor the user's emotions in real time and generate feedback and an intervention plan in an appropriate manner. This allows for the provision of more appropriate feedback and an intervention plan by adjusting the way it presents the feedback and intervention plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way feedback and intervention plans are expressed.

[0077] The generation unit can adjust the level of detail of the generated feedback based on its importance. For example, it can generate detailed feedback for important feedback, or concise feedback for general feedback. It can also generate feedback quickly for urgent feedback. For example, it can store the importance of the feedback in a database and adjust the level of detail of the generated feedback. This allows for the provision of more appropriate feedback and intervention plans by adjusting the level of detail of the generated feedback based on its importance. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the feedback into the generation AI and have the generation AI adjust the level of detail of the generated feedback.

[0078] The generation unit can apply different generation algorithms depending on the feedback category during generation. For example, the generation unit can apply an emotion generation algorithm to motivation-related feedback. For example, the generation unit can apply a task generation algorithm to feedback on completed tasks. The generation unit can also apply a self-assessment generation algorithm to self-assessment feedback. For example, the generation unit can store the feedback categories in a database and apply different generation algorithms. This allows for the provision of more appropriate feedback and intervention plans by applying different generation algorithms depending on the feedback category. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the feedback categories into a generation AI and have the generation AI perform the application of the generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the length of the feedback and intervention plan it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit generates short, concise feedback and intervention plans. For example, if the user is relaxed, the generation unit generates detailed feedback and intervention plans. The generation unit can also generate feedback and intervention plans that emphasize positive elements if the user is stressed. For example, the generation unit can monitor the user's emotions in real time and generate feedback and intervention plans of appropriate length. This allows for the provision of more appropriate feedback and intervention plans by adjusting the length of the feedback and intervention plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, for example, or without a generative AI. For example, the generation unit can input user emotion data into the generation AI, and have the generation AI perform feedback and adjust the length of the intervention plan.

[0080] The generation unit can determine the generation priority based on the timing of feedback submission during the generation process. For example, the generation unit may prioritize generating recently submitted feedback. For example, the generation unit may prioritize generating urgent feedback. The generation unit can also prioritize generating regularly submitted feedback. For example, the generation unit may store the timing of feedback submission in a database and determine the generation priority. This allows for the provision of more appropriate feedback and intervention plans by determining the generation priority based on the timing of feedback submission. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit may input the timing of feedback submission into a generation AI and have the generation AI determine the generation priority.

[0081] The generation unit can adjust the order of generation based on the relevance of the feedback during generation. For example, the generation unit can prioritize generating highly relevant feedback. For example, the generation unit can postpone generating less relevant feedback. The generation unit can also adjust the order of generation according to the content of the feedback. For example, the generation unit can store the relevance of the feedback in a database and adjust the order of generation. This allows for the provision of more appropriate feedback and intervention plans by adjusting the order of generation based on the relevance of the feedback. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the feedback into a generation AI and have the generation AI perform the adjustment of the generation order.

[0082] The service provider can estimate the user's emotions and adjust the way it presents the feedback and intervention plan based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed feedback and an intervention plan. For example, if the user is in a hurry, the service provider can provide concise feedback and an intervention plan. The service provider can also provide feedback and an intervention plan that emphasizes positive elements if the user is stressed. For example, the service provider can monitor the user's emotions in real time and provide feedback and an intervention plan in an appropriate manner. This allows for the provision of more appropriate feedback and an intervention plan by adjusting the way it presents the feedback and intervention plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the way it presents the feedback and intervention plan.

[0083] The delivery unit can adjust the level of detail provided based on the importance of the feedback. For example, it can provide detailed feedback for important feedback, and concise feedback for general feedback. It can also provide rapid feedback for urgent feedback. For example, the delivery unit can store the importance of the feedback in a database and adjust the level of detail based on that database. This allows for the provision of more appropriate feedback and intervention plans by adjusting the level of detail based on the importance of the feedback. Some or all of the above processes in the delivery unit may be performed using AI, for example, or not. For example, the delivery unit can input the importance of the feedback into the AI ​​and have the AI ​​adjust the level of detail.

[0084] The service provider can apply different service provision algorithms depending on the category of feedback at the time of provision. For example, the service provider can apply an emotion service provision algorithm to feedback related to motivation. For example, the service provider can apply a task service provision algorithm to feedback related to completed tasks. The service provider can also apply a self-assessment service provision algorithm to feedback related to self-assessment. For example, the service provider can store the feedback categories in a database and apply different service provision algorithms. This allows for the provision of more appropriate feedback and intervention plans by applying different service provision algorithms depending on the feedback category. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the feedback categories into AI and have the AI ​​perform the application of the service provision algorithms.

[0085] The service provider can estimate the user's emotions and adjust the length of the feedback and intervention plan based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise feedback and intervention plans. For example, if the user is relaxed, the service provider can provide detailed feedback and intervention plans. Furthermore, if the user is stressed, the service provider can provide feedback and intervention plans that emphasize positive elements. For example, the service provider can monitor the user's emotions in real time and provide feedback and intervention plans of appropriate length. This allows for the provision of more appropriate feedback and intervention plans by adjusting the length of the feedback and intervention plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the length of the feedback and intervention plan.

[0086] The service provider can determine the priority of feedback delivery based on the timing of feedback submission. For example, the service provider may prioritize recently submitted feedback. For example, the service provider may prioritize urgent feedback. The service provider may also prioritize regularly submitted feedback. For example, the service provider may store the timing of feedback submission in a database and determine the priority of delivery. This allows for the provision of more appropriate feedback and intervention plans by prioritizing delivery based on the timing of feedback submission. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input the timing of feedback submission into AI and have the AI ​​perform the determination of delivery priorities.

[0087] The delivery unit can adjust the order of delivery based on the relevance of the feedback. For example, the delivery unit may prioritize providing highly relevant feedback. For example, it may postpone providing less relevant feedback. The delivery unit can also adjust the order of delivery according to the content of the feedback. For example, the delivery unit may store the relevance of the feedback in a database and adjust the order of delivery. This allows for the provision of more appropriate feedback and intervention plans by adjusting the order of delivery based on the relevance of the feedback. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit may input the relevance of the feedback into AI and have AI perform the adjustment of the order of delivery. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives feedback from the user using the reception device 38 of the smart device 14. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the user's behavior patterns and motivation fluctuations based on past data and current input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates feedback and intervention plans optimized for the user's state using generation AI. The provision unit provides the generated feedback and intervention plans to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives feedback from the user using the microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and learns the user's behavior patterns and motivation fluctuations based on past data and current input. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and generates feedback and intervention plans optimized for the user's state using generation AI. The provision unit provides the generated feedback and intervention plans to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives feedback from the user using the microphone 238 of the headset terminal 314. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and learns the user's behavior patterns and motivation fluctuations based on past data and current input. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates feedback and intervention plans optimized for the user's state using generation AI. The provision unit provides the generated feedback and intervention plans to the user using, for example, the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives feedback from the user using the microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the user's behavior patterns and motivation fluctuations based on past data and current input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates feedback and intervention plans optimized for the user's state using a generation AI. The provision unit provides the generated feedback and intervention plans to the user using, for example, the speaker 240 of the robot 414.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The reception desk can monitor the user's current health status when receiving user feedback and adjust the feedback collection method according to that status. For example, if the user is feeling fatigued, feedback can be collected in the form of simple questions. If the user is in good health, more detailed feedback can be requested. Furthermore, if the user is ill or injured, feedback collection can be temporarily suspended and resumed after recovery. This allows for flexible feedback collection tailored to the user's health status, reducing the burden on the user.

[0090] The analytics unit can analyze user feedback by considering not only the user's past feedback history but also their social media activity and online behavior history. For example, it can include positive posts shared by users on social media in its analysis. It can also utilize information about keywords users searched online and websites they visited. Furthermore, it can incorporate the user's activity history in online communities into the analysis. This allows for a more comprehensive understanding of user behavior patterns and provides more accurate analysis results.

[0091] The generation unit can take into account the user's current living environment and lifestyle when generating feedback and intervention plans based on user feedback. For example, if the user is working from home, it can provide feedback and intervention plans suitable for working from home. If the user has a busy schedule, it can propose intervention plans that can be implemented in a short amount of time. Furthermore, if the user lives with family, it can provide feedback that encourages cooperation with family members. This allows for the provision of support tailored to the user's living environment and enables effective intervention.

[0092] The system can take into account the user's device usage when providing generated feedback and intervention plans to the user. For example, if the user primarily uses a smartphone, the feedback can be provided in a format optimized for smartphones. If the user uses a computer, the feedback can be displayed in a format suitable for the computer screen. Furthermore, if the user uses a wearable device, it is possible to send notifications to that device. This enables flexible feedback delivery tailored to the user's device usage, thereby improving user convenience.

[0093] The service provider can deliver generated feedback and intervention plans to users in a format tailored to their learning style and preferences. For example, if a user prefers visual information, feedback can be provided using graphs and charts. If a user prefers text-based information, detailed written feedback can be provided. Furthermore, if a user prefers audio feedback, voice messages can be sent. This allows for flexible feedback delivery tailored to the user's learning style and preferences, deepening their understanding.

[0094] The reception desk can estimate the user's emotions and adjust the feedback process based on those estimates. For example, if a user is stressed, relaxing music can be played while feedback is received. If a user is highly motivated, challenging questions can be posed. Furthermore, if a user is tired, feedback can be received in the form of simple questions. This allows for flexible feedback processing tailored to the user's emotions, reducing the user's burden.

[0095] The analysis unit can estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user is highly motivated, it can prioritize analyzing important feedback. If the user is less motivated, it can prioritize analyzing simpler feedback. Furthermore, if the user is stressed, it can prioritize analyzing relaxing feedback. This allows for flexible analysis tailored to the user's emotions, reducing the user's burden.

[0096] The generation unit can estimate the user's emotions and adjust the content of the feedback and intervention plan based on the estimated emotions. For example, if the user is relaxed, it can generate detailed feedback and an intervention plan. If the user is in a hurry, it can generate concise feedback and an intervention plan. Furthermore, if the user is stressed, it can generate feedback and an intervention plan that emphasizes positive elements. This enables the generation of flexible feedback and intervention plans that respond to the user's emotions, thereby reducing the user's burden.

[0097] The service provider can estimate the user's emotions and adjust the timing of the feedback and intervention plans based on those estimates. For example, if the user is relaxed, detailed feedback and intervention plans can be provided. If the user is in a hurry, concise feedback and intervention plans can be provided. Furthermore, if the user is stressed, feedback and intervention plans emphasizing positive elements can be provided. This enables the provision of flexible feedback and intervention plans tailored to the user's emotions, thereby reducing the user's burden.

[0098] The service provider can estimate the user's emotions and adjust the format of the feedback and intervention plan based on those estimated emotions. For example, if the user is relaxed, detailed feedback and an intervention plan can be provided. If the user is in a hurry, concise feedback and an intervention plan can be provided. Furthermore, if the user is stressed, feedback and an intervention plan emphasizing positive elements can be provided. This allows for the provision of flexible feedback and intervention plans tailored to the user's emotions, thereby reducing the user's burden.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk receives feedback from users. This feedback includes motivation levels, completed tasks, and self-assessments. The reception desk stores the user-entered feedback in a database. It can also convert voice feedback into text data using speech recognition technology. For example, users can enter feedback via a smartphone app. Step 2: The analysis unit analyzes the feedback received by the reception unit. The analysis unit learns user behavior patterns and motivation fluctuations based on past data and current input. Using machine learning algorithms and natural language processing techniques, it analyzes user behavior patterns and feedback content to estimate emotions. Step 3: The generation unit generates feedback and intervention plans based on the results analyzed by the analysis unit. The generation unit uses generation AI to generate feedback and intervention plans optimized for the user's condition. For example, for a user who reports low motivation, it identifies the cause and proposes specific countermeasures. It also provides positive feedback for completed tasks to support increased self-esteem. Step 4: The provider unit provides the user with the feedback and intervention plan generated by the generator unit. The provider unit notifies the user of the generated feedback and intervention plan via their smartphone app. It can also send it via email. The provider unit provides the feedback and intervention plan in the format preferred by the user.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 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.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk for receiving user feedback, An analysis unit analyzes the feedback received by the reception unit, A generation unit generates feedback and an intervention plan based on the results of the analysis performed by the aforementioned analysis unit, A providing unit that provides the user with the feedback and intervention plan generated by the generation unit, Equipped with A system characterized by the following features.

2. The aforementioned reception unit is The system accepts information on the user's motivation level, completed tasks, and self-assessment. The system according to feature 1.

3. The aforementioned analysis unit, It learns user behavior patterns and motivation fluctuations based on past data and current input. The system according to feature 1.

4. The generating unit is Generate feedback and intervention plans based on the user's condition. The system according to feature 1.

5. The aforementioned supply unit is, Provide the user with the generated feedback and intervention plan. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of feedback requests based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the user's past feedback history and select the optimal method for receiving it. The system according to feature 1.

8. The aforementioned reception unit is When receiving feedback, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

Images