system

Abstract

The system according to this embodiment aims to provide the optimal output based on the user's request. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives user requests. The generation unit analyzes the requests received by the reception unit and generates the optimal output based on the user's attributes and behavioral information. The provision unit provides the output generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method 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 has not been fully achieved to provide an optimal output for a user request, and there is room for improvement.

[0005] The system according to an embodiment aims to provide an optimal output based on a user request.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives a user request. The generation unit analyzes the request received by the reception unit and generates an optimal output based on user attributes and behavior information. The provision unit provides the output generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide the optimal output based on the user's request. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 utilizes generative AI to support the acquisition of qualifications, hobbies, and special skills. In this support system, the user inputs a request regarding qualifications, hobbies, and special skills, and the generative AI generates the optimal output based on the user's attributes and behavioral information. This output includes books and study time required to obtain a qualification, skills and procedures required to start a business, and practice methods to improve dancing skills. This allows the user to easily obtain information and get started. For example, the user inputs a request regarding qualifications, hobbies, and special skills. For example, the user inputs a request such as, "I would like to know the books and study time required to obtain a medical office administration qualification." This information is input to the generative AI. Next, the generative AI analyzes the input request and generates the optimal output based on the user's attributes and behavioral information. The generative AI provides information such as books and study time required to obtain a qualification, skills and procedures required to start a business, and practice methods to improve dancing skills. For example, it provides specific advice such as, "To obtain a medical office administration qualification, it is recommended that you purchase the following books and study for 2 hours every day for 3 months." Furthermore, the generating AI provides relevant web content, book and product information, success stories, schedules, costs, and other related information based on user requests. This allows users to efficiently prepare towards their goals. The support system makes it easy for users to obtain information and get started. For example, users aiming to obtain qualifications can efficiently study by knowing the necessary books and study time. Users aiming to start a business can smoothly prepare by knowing the necessary skills and procedures. In addition, users with hobbies or special skills can improve their skills while having fun by knowing specific methods for improvement. The support system is currently targeted at women in their 20s to 40s, and aims to expand to men and women aged 18 to 65 in the future. The market size is estimated at approximately 250 billion yen, and significant growth is expected in the overall market size trend of the education industry.Furthermore, because information can be acquired in spare time with simple operations, it is extremely convenient for busy modern people. In addition, it is possible to develop a reward system by contributing to someone's "start" through information provision. For example, a user who has successfully obtained a qualification can earn a reward by providing advice to other users. In this way, a community is formed in which users support each other. As a result, the support system fully utilizes the generated AI and the information assets of users to help people acquire qualifications, hobbies, and special skills, and will be useful in various situations such as employment, entrepreneurship, and community building in local areas and in old age. By fusing technology with the wisdom and experience of our predecessors, we can support the first steps of people living in the present.

[0029] The support system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives user requests. For example, users can input requests regarding qualifications, hobbies, and special skills. The reception unit receives requests, for example, through web forms or mobile applications. The reception unit can also receive requests using voice input or chatbots. The generation unit analyzes the requests received by the reception unit and generates optimal output based on the user's attributes and behavioral information. The generation unit analyzes requests, for example, using generation AI, and provides the user with the most suitable information. The generation unit generates information such as books and study time required to obtain qualifications, skills and procedures required to start a business, and practice methods to improve dance skills. For example, based on the user's request, the generation unit provides a list of books required to obtain qualifications and a method for calculating study time. The generation unit can also provide the skill set and procedural steps required to start a business. Furthermore, the generation unit can also provide recommended practice menus and frequencies for improving dance skills. The provision unit provides the output generated by the generation unit. The provision unit provides information, for example, through a website or mobile application. Furthermore, the service provider can also provide information via email and push notifications. In addition, the service provider can provide relevant web content, book and product information, success stories, schedules, costs, and other related information. For example, the service provider can provide information from reliable sources based on user requests. The service provider can also provide a user-friendly UI / UX. For example, the service provider can design an intuitive interface based on usability test results. Furthermore, the service provider can implement a reward system that contributes to someone's "start" through information provision. For example, the service provider can provide a mechanism where users who have successfully obtained qualifications can earn rewards by providing advice to other users. This allows the support system according to the embodiment to generate and provide optimal output based on user requests.

[0030] The reception desk receives user requests. For example, users can enter requests regarding qualifications, hobbies, or special skills. The reception desk accepts requests through web forms, mobile applications, and other means. It can also accept requests using voice input or chatbots. Specifically, with web forms, users enter their request into a text box and press a submit button to send the request to the reception desk. With mobile applications, users can enter their request into a dedicated form within the app and submit it in the same way. With voice input, users speak their request into a microphone, and speech recognition technology converts it into text. Chatbots use natural language processing technology to understand user requests and send them to the reception desk in the appropriate format. This allows the reception desk to provide diverse input methods, enabling users to submit requests in the most convenient way. Furthermore, the reception desk also has the function to automatically categorize the content of requests and route them to the appropriate processing department. For example, requests regarding qualifications are routed to the qualification acquisition support department, and requests regarding hobbies are routed to the hobby support department. This allows the reception desk to process requests efficiently and respond quickly to user needs.

[0031] The generation unit analyzes requests received by the reception unit and generates optimal output based on user attributes and behavioral information. For example, the generation unit uses a generation AI to analyze requests and provide the user with the most relevant information. Specifically, the generation AI analyzes the user's request using natural language processing technology and extracts relevant information. Next, it generates optimal output based on the user's past behavioral history and attribute information. For example, in the case of a request regarding certification, the generation AI considers the user's learning history and current skill level to propose an optimal learning plan. Specifically, it provides a list of books required for certification and a method for calculating study time. In the case of a request regarding a company, the generation AI provides the necessary skill set and procedural steps based on the user's business experience and interests. Furthermore, in the case of a request to improve dancing skills, the generation AI considers the user's current skill level and practice frequency to provide recommended practice menus and frequencies. The generation unit can generate and provide this information to the user in real time. In addition, the generation unit collects user feedback to continuously improve the accuracy of the generated output and improves the generation AI's algorithm. This allows the generation unit to provide information best suited to the user's needs and support the user in achieving their goals.

[0032] The provider provides the output generated by the generator. The provider provides information, for example, through websites and mobile applications. They can also provide information via email and push notifications. Specifically, on a website, users can log in, access a dashboard, and view the generated output. On mobile applications, users receive in-app notifications and can view the generated information. For email, the generated output is sent to the user's email address, and for push notifications, information is provided in real time using the smartphone's notification function. Furthermore, the provider provides reference web content, book and product information, success stories, schedules, costs, and other relevant information. For example, when providing information on obtaining qualifications, they provide book lists and study plans from reliable sources. When providing information on companies, they provide interviews with successful entrepreneurs and business plan templates. When providing information on dance, they provide practice menus and video tutorials by professional dancers. The provider provides a user-friendly UI / UX. For example, the provider designs an intuitive interface based on the results of usability testing. Furthermore, the provider implements a reward system that contributes to someone's "start" through information provision. For example, the service provider could offer a system where users who have successfully obtained qualifications can earn rewards by providing advice to other users. This would allow the service provider to quickly and reliably provide information to users and support them in achieving their goals.

[0033] The generation unit can generate information such as books and study time required to obtain qualifications, skills and procedures necessary to start a business, and practice methods to improve dance skills. For example, the generation unit can provide a list of books required to obtain qualifications. For example, the generation unit can list recommended books for obtaining a medical office administration qualification. The generation unit can also calculate the study time required to obtain qualifications. For example, the generation unit can recommend studying for 2 hours every day for 3 months. Furthermore, the generation unit can also provide skills and procedures necessary to start a business. For example, the generation unit can list how to create a business plan and the necessary procedures. The generation unit can also provide practice methods to improve dance skills. For example, the generation unit can recommend 30 minutes of practice every day and provide a specific practice menu. In this way, the generation unit can provide specific information that meets the user's request. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the user's request into the generation AI, and the generation AI can generate the optimal output.

[0034] The service provider can provide reference web content, book and product information, success stories, schedules, costs, and other relevant information. For example, the service provider can provide web content from reliable sources. For example, the service provider can provide links to reliable websites related to certification. The service provider can also provide recommended book and product information. For example, the service provider can provide information on books and learning tools that are helpful for certification. Furthermore, the service provider can provide success stories. For example, the service provider can provide interview articles with users who have successfully obtained certification. The service provider can also provide information on schedules and costs. For example, the service provider can provide estimated learning schedules and costs required for certification. In this way, the service provider can provide users with a variety of information they need. 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 user requests into AI, and the AI ​​can provide the most suitable information.

[0035] The service provider can provide a user-friendly UI / UX. For example, the service provider can design an intuitive interface based on the results of usability tests. For example, the service provider can adopt a simple and highly visible design. The service provider can also improve the UI / UX based on user feedback. For example, the service provider can analyze the user's operation history and make improvements to enhance usability. Furthermore, the service provider can provide new interfaces such as voice input and gesture control. For example, the service provider can provide a function to accept requests using voice commands. This allows the service provider to provide an intuitive interface for users. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the user's operation history into AI, which can then design the optimal UI / UX.

[0036] The service provider can develop a reward system that contributes to someone's "start" by providing information. For example, the service provider can provide a system where users who have successfully obtained qualifications can earn rewards by providing advice to other users. For example, the service provider can provide reviews and advice written by users who have successfully obtained qualifications to other users and pay them a reward in return. The service provider can also form a community where users support each other. For example, the service provider can provide a forum where users can post questions and consultations, and a system where other users can earn rewards by answering them. Furthermore, the service provider can clarify how the reward system is operated. For example, the service provider can define the types of rewards and payment conditions and operate the system transparently to users. This allows the service provider to form a community where users support each other. 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 operation of the reward system into AI, and the AI ​​can design an optimal reward system.

[0037] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently made in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest reception methods to be used during specific time periods based on the user's past request history. In this way, the reception desk can provide the optimal reception method based on the user's past request history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past request history into AI, and the AI ​​can select the optimal reception method.

[0038] The reception unit can filter requests based on the user's current lifestyle and areas of interest. For example, the reception unit can prioritize requests that are highly relevant to the user's current lifestyle. For example, the reception unit can filter the content of requests based on the user's areas of interest and provide appropriate information. The reception unit can also determine the priority of requests based on the user's lifestyle and areas of interest. This allows the reception unit to filter requests based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into the AI, which can then perform optimal filtering.

[0039] The reception unit can prioritize requests based on their relevance, taking into account the user's geographical location. For example, the reception unit can prioritize requests based on the user's current location. For example, the reception unit can filter the content of requests based on the user's geographical location and provide appropriate information. The reception unit can also determine the priority of requests based on the user's geographical location. This allows the reception unit to prioritize requests based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into AI, which can then select the most appropriate requests.

[0040] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can prioritize requests that are highly relevant based on the user's social media activity. For example, the reception unit can analyze the user's social media activity, filter the content of requests, and provide appropriate information. The reception unit can also determine the priority of requests based on the user's social media activity. This allows the reception unit to accept requests based on 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, which can then select the most appropriate requests.

[0041] The generation unit can adjust the level of detail of the output based on the importance of the request when generating the output. For example, the generation unit generates an output with detailed information for high-importance requests, and an output with concise information for low-importance requests. The generation unit can also dynamically adjust the level of detail of the output according to the importance of the request. This allows the generation unit to adjust the level of detail of the output according to the importance of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request importance data into the AI, and the AI ​​can determine the optimal level of detail.

[0042] The generation unit can apply different generation algorithms depending on the request category when generating output. For example, for a request related to obtaining a qualification, the generation unit can apply an algorithm that generates a learning plan. For example, for a request related to a company, the generation unit can apply an algorithm that generates a business plan. The generation unit can also apply an algorithm that generates practice methods for requests related to hobbies or special skills. This allows the generation unit to apply the most suitable generation algorithm depending on the request 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 request category data into the generation AI, which can then apply the most suitable generation algorithm.

[0043] The generation unit can determine the priority of outputs based on the submission timing of requests when generating outputs. For example, the generation unit will prioritize output generation for requests submitted earlier. For example, the generation unit will postpone output generation for requests submitted later. The generation unit can also dynamically adjust the output priority according to the submission timing of requests. This allows the generation unit to adjust the output priority according to the submission timing of requests. 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 request submission timing data into a generation AI, which can then determine the optimal priority.

[0044] The generation unit can adjust the order of outputs based on the relevance of the requests when generating outputs. For example, the generation unit will prioritize generating outputs for highly relevant requests. For example, the generation unit will postpone generating outputs for less relevant requests. The generation unit can also dynamically adjust the order of outputs according to the relevance of the requests. This allows the generation unit to adjust the order of outputs according to the relevance of the requests. 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 request relevance data into a generation AI, which can then determine the optimal order.

[0045] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may predict and provide the optimal display method based on the user's past operation history. The service provider may also analyze the user's past operation history and provide a display method with high visibility. In this way, the service provider can provide the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history into AI, and the AI ​​may select the optimal display method.

[0046] The information provider can customize the information provided based on the user's current living situation. For example, the provider can prioritize providing highly relevant information based on the user's current living situation. For example, the provider can customize how the information is displayed according to the user's living situation. The provider can also determine the priority of information based on the user's living situation. This allows the provider to customize the information according to the user's living situation. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the user's living situation data into AI, which can then customize the information to be optimal.

[0047] The information provider can provide optimal information by considering the user's geographical location information at the time of delivery. For example, the information provider can prioritize providing highly relevant information based on the user's current location. For example, the information provider can customize how information is displayed based on the user's geographical location information. The information provider can also determine the priority of information based on the user's geographical location information. In this way, the information provider can provide optimal information based on the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into AI, and the AI ​​can provide optimal information.

[0048] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing highly relevant information based on the user's social media activity. For example, the service provider can analyze the user's social media activity and customize how the information is displayed. The service provider can also determine the priority of information based on the user's social media activity. This allows the service provider to provide relevant information based on the user's social media activity. 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 user's social media activity into AI, which can then provide the most relevant information.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The reception desk can prioritize user requests by referencing the user's past successes. For example, if a user has previously obtained a qualification, that success can be used to prioritize new requests. Similarly, if a user has achieved success in a particular hobby or skill, requests related to that area can be prioritized. Furthermore, the reception desk can analyze the user's past successes and provide optimal advice based on the content of the request. This allows the reception desk to adjust request priorities based on the user's past successes and provide more effective support.

[0051] The service provider can provide region-specific information by taking into account the user's geographical location. For example, if a user lives in a specific region, it can provide information on certification seminars and workshops held in that region. If a user is aiming to start a business in a specific region, it can provide information on the local business environment and support systems. Furthermore, if a user wants to hone their hobbies or skills in a specific region, it can provide information on clubs and circles active in that area. This allows the service provider to deliver more relevant information based on the user's geographical location.

[0052] The information delivery unit can determine the optimal timing for providing information by referring to the user's past operation history. For example, if the user has previously obtained information during a specific time period, the information can be provided at that time. Similarly, if the user has previously obtained information on a specific day of the week, the information can be provided at that day. Furthermore, if the user has previously obtained information during a specific event or situation, the information can be provided at that event or situation. This allows the information delivery unit to determine the optimal timing for providing information based on the user's past operation history, enabling them to provide more effective support.

[0053] The generation unit can analyze the user's past request history and generate the optimal output according to the content of the request. For example, if a user has made many requests related to obtaining qualifications in the past, it can generate detailed output specialized in that field. Also, if a user has made many requests related to hobbies or special skills in the past, it can provide specific advice related to those fields. Furthermore, if a user has made many requests related to companies in the past, it can generate output that includes the latest information and trends in that field. In this way, the generation unit can provide more personalized output based on the user's past request history.

[0054] The reception desk can customize how requests are processed, taking into account the user's geographical location. For example, if a user lives in a specific region, the request processing method can be adjusted based on the services and resources available in that region. Similarly, if a user is traveling, a request processing method tailored to the specific needs of that region can be provided. Furthermore, if a user is aiming to establish a business in a specific region, the request processing method can be customized based on the local business environment and support systems. This allows the reception desk to provide more relevant request processing methods based on the user's geographical location.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk receives user requests. For example, users can enter requests regarding qualifications, hobbies, or special skills. The reception desk accepts requests via web forms or mobile applications. It can also accept requests using voice input or chatbots. Step 2: The generation unit analyzes the request received by the reception unit and generates the optimal output based on the user's attributes and behavioral information. The generation unit uses generation AI to analyze the request and generate information such as books and study time required to obtain qualifications, skills and procedures required to start a business, and practice methods to improve dance skills. Step 3: The delivery unit provides the output generated by the generation unit. The delivery unit provides information through websites and mobile applications. It can also provide information via email and push notifications. Furthermore, it provides reference web content, book and product information, success stories, schedules, costs, and other relevant information.

[0057] (Example of form 2) The support system according to an embodiment of the present invention is a system that utilizes generative AI to support the acquisition of qualifications, hobbies, and special skills. In this support system, the user inputs a request regarding qualifications, hobbies, and special skills, and the generative AI generates the optimal output based on the user's attributes and behavioral information. This output includes books and study time required to obtain a qualification, skills and procedures required to start a business, and practice methods to improve dancing skills. This allows the user to easily obtain information and get started. For example, the user inputs a request regarding qualifications, hobbies, and special skills. For example, the user inputs a request such as, "I would like to know the books and study time required to obtain a medical office administration qualification." This information is input to the generative AI. Next, the generative AI analyzes the input request and generates the optimal output based on the user's attributes and behavioral information. The generative AI provides information such as books and study time required to obtain a qualification, skills and procedures required to start a business, and practice methods to improve dancing skills. For example, it provides specific advice such as, "To obtain a medical office administration qualification, it is recommended that you purchase the following books and study for 2 hours every day for 3 months." Furthermore, the generating AI provides relevant web content, book and product information, success stories, schedules, costs, and other related information based on user requests. This allows users to efficiently prepare towards their goals. The support system makes it easy for users to obtain information and get started. For example, users aiming to obtain qualifications can efficiently study by knowing the necessary books and study time. Users aiming to start a business can smoothly prepare by knowing the necessary skills and procedures. In addition, users with hobbies or special skills can improve their skills while having fun by knowing specific methods for improvement. The support system is currently targeted at women in their 20s to 40s, and aims to expand to men and women aged 18 to 65 in the future. The market size is estimated at approximately 250 billion yen, and significant growth is expected in the overall market size trend of the education industry.Furthermore, because information can be acquired in spare time with simple operations, it is extremely convenient for busy modern people. In addition, it is possible to develop a reward system by contributing to someone's "start" through information provision. For example, a user who has successfully obtained a qualification can earn a reward by providing advice to other users. In this way, a community is formed in which users support each other. As a result, the support system fully utilizes the generated AI and the information assets of users to help people acquire qualifications, hobbies, and special skills, and will be useful in various situations such as employment, entrepreneurship, and community building in local areas and in old age. By fusing technology with the wisdom and experience of our predecessors, we can support the first steps of people living in the present.

[0058] The support system according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives user requests. For example, users can input requests regarding qualifications, hobbies, and special skills. The reception unit receives requests, for example, through web forms or mobile applications. The reception unit can also receive requests using voice input or chatbots. The generation unit analyzes the requests received by the reception unit and generates optimal output based on the user's attributes and behavioral information. The generation unit analyzes requests, for example, using generation AI, and provides the user with the most suitable information. The generation unit generates information such as books and study time required to obtain qualifications, skills and procedures required to start a business, and practice methods to improve dance skills. For example, based on the user's request, the generation unit provides a list of books required to obtain qualifications and a method for calculating study time. The generation unit can also provide the skill set and procedural steps required to start a business. Furthermore, the generation unit can also provide recommended practice menus and frequencies for improving dance skills. The provision unit provides the output generated by the generation unit. The provision unit provides information, for example, through a website or mobile application. Furthermore, the service provider can also provide information via email and push notifications. In addition, the service provider can provide relevant web content, book and product information, success stories, schedules, costs, and other related information. For example, the service provider can provide information from reliable sources based on user requests. The service provider can also provide a user-friendly UI / UX. For example, the service provider can design an intuitive interface based on usability test results. Furthermore, the service provider can implement a reward system that contributes to someone's "start" through information provision. For example, the service provider can provide a mechanism where users who have successfully obtained qualifications can earn rewards by providing advice to other users. This allows the support system according to the embodiment to generate and provide optimal output based on user requests.

[0059] The reception desk receives user requests. For example, users can enter requests regarding qualifications, hobbies, or special skills. The reception desk accepts requests through web forms, mobile applications, and other means. It can also accept requests using voice input or chatbots. Specifically, with web forms, users enter their request into a text box and press a submit button to send the request to the reception desk. With mobile applications, users can enter their request into a dedicated form within the app and submit it in the same way. With voice input, users speak their request into a microphone, and speech recognition technology converts it into text. Chatbots use natural language processing technology to understand user requests and send them to the reception desk in the appropriate format. This allows the reception desk to provide diverse input methods, enabling users to submit requests in the most convenient way. Furthermore, the reception desk also has the function to automatically categorize the content of requests and route them to the appropriate processing department. For example, requests regarding qualifications are routed to the qualification acquisition support department, and requests regarding hobbies are routed to the hobby support department. This allows the reception desk to process requests efficiently and respond quickly to user needs.

[0060] The generation unit analyzes requests received by the reception unit and generates optimal output based on user attributes and behavioral information. For example, the generation unit uses a generation AI to analyze requests and provide the user with the most relevant information. Specifically, the generation AI analyzes the user's request using natural language processing technology and extracts relevant information. Next, it generates optimal output based on the user's past behavioral history and attribute information. For example, in the case of a request regarding certification, the generation AI considers the user's learning history and current skill level to propose an optimal learning plan. Specifically, it provides a list of books required for certification and a method for calculating study time. In the case of a request regarding a company, the generation AI provides the necessary skill set and procedural steps based on the user's business experience and interests. Furthermore, in the case of a request to improve dancing skills, the generation AI considers the user's current skill level and practice frequency to provide recommended practice menus and frequencies. The generation unit can generate and provide this information to the user in real time. In addition, the generation unit collects user feedback to continuously improve the accuracy of the generated output and improves the generation AI's algorithm. This allows the generation unit to provide information best suited to the user's needs and support the user in achieving their goals.

[0061] The provider provides the output generated by the generator. The provider provides information, for example, through websites and mobile applications. They can also provide information via email and push notifications. Specifically, on a website, users can log in, access a dashboard, and view the generated output. On mobile applications, users receive in-app notifications and can view the generated information. For email, the generated output is sent to the user's email address, and for push notifications, information is provided in real time using the smartphone's notification function. Furthermore, the provider provides reference web content, book and product information, success stories, schedules, costs, and other relevant information. For example, when providing information on obtaining qualifications, they provide book lists and study plans from reliable sources. When providing information on companies, they provide interviews with successful entrepreneurs and business plan templates. When providing information on dance, they provide practice menus and video tutorials by professional dancers. The provider provides a user-friendly UI / UX. For example, the provider designs an intuitive interface based on the results of usability testing. Furthermore, the provider implements a reward system that contributes to someone's "start" through information provision. For example, the service provider could offer a system where users who have successfully obtained qualifications can earn rewards by providing advice to other users. This would allow the service provider to quickly and reliably provide information to users and support them in achieving their goals.

[0062] The generation unit can generate information such as books and study time required to obtain qualifications, skills and procedures required to start a business, and practice methods to improve dance skills. For example, the generation unit can provide a list of books required to obtain qualifications. For example, the generation unit can list recommended books for obtaining a medical office administration qualification. The generation unit can also calculate the study time required to obtain qualifications. For example, the generation unit can recommend studying for 2 hours every day for 3 months. Furthermore, the generation unit can provide skills and procedures required to start a business. For example, the generation unit can list how to create a business plan and the necessary procedures. The generation unit can also provide practice methods to improve dance skills. For example, the generation unit can recommend 30 minutes of practice every day and provide a specific practice menu. In this way, the generation unit can provide specific information that meets the user's request. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can input the user's request into a generation AI, and the generation AI can generate the optimal output.

[0063] The service provider can provide reference web content, book and product information, success stories, schedules, costs, and other relevant information. For example, the service provider can provide web content from reliable sources. For example, the service provider can provide links to reliable websites related to certification. The service provider can also provide recommended book and product information. For example, the service provider can provide information on books and learning tools that are helpful for certification. Furthermore, the service provider can provide success stories. For example, the service provider can provide interview articles with users who have successfully obtained certification. The service provider can also provide information on schedules and costs. For example, the service provider can provide estimated learning schedules and costs required for certification. In this way, the service provider can provide users with a variety of information they need. 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 user requests into AI, and the AI ​​can provide the most suitable information.

[0064] The service provider can provide a user-friendly UI / UX. For example, the service provider can design an intuitive interface based on the results of usability tests. For example, the service provider can adopt a simple and highly visible design. The service provider can also improve the UI / UX based on user feedback. For example, the service provider can analyze the user's operation history and make improvements to enhance usability. Furthermore, the service provider can provide new interfaces such as voice input and gesture control. For example, the service provider can provide a function to accept requests using voice commands. This allows the service provider to provide an intuitive interface for users. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the user's operation history into AI, which can then design the optimal UI / UX.

[0065] The service provider can develop a reward system that contributes to someone's "start" by providing information. For example, the service provider can provide a system where users who have successfully obtained qualifications can earn rewards by providing advice to other users. For example, the service provider can provide reviews and advice written by users who have successfully obtained qualifications to other users and pay them a reward in return. The service provider can also form a community where users support each other. For example, the service provider can provide a forum where users can post questions and consultations, and a system where other users can earn rewards by answering them. Furthermore, the service provider can clarify how the reward system is operated. For example, the service provider can define the types of rewards and payment conditions and operate the system transparently to users. This allows the service provider to form a community where users support each other. 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 operation of the reward system into AI, and the AI ​​can design an optimal reward system.

[0066] The reception desk can estimate the user's emotions and adjust the timing of request acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk may temporarily delay accepting the request to provide a relaxing environment. For example, if the user is relaxed, the reception desk may accept the request immediately and begin processing it quickly. The reception desk may also prioritize accepting the request if the user is in a hurry, postponing other processing. In this way, the reception desk can adjust the timing of request acceptance 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 desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into the AI, which can determine the optimal timing for acceptance.

[0067] The reception desk can analyze the user's past request history and select the optimal reception method. For example, the reception desk can automatically display requests that the user has frequently made in the past as candidates. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest reception methods to be used during specific time periods based on the user's past request history. In this way, the reception desk can provide the optimal reception method based on the user's past request history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past request history into AI, and the AI ​​can select the optimal reception method.

[0068] The reception unit can filter requests based on the user's current lifestyle and areas of interest. For example, the reception unit can prioritize requests that are highly relevant to the user's current lifestyle. For example, the reception unit can filter the content of requests based on the user's areas of interest and provide appropriate information. The reception unit can also determine the priority of requests based on the user's lifestyle and areas of interest. This allows the reception unit to filter requests based on the user's lifestyle and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not. For example, the reception unit can input data on the user's lifestyle and areas of interest into the AI, which can then perform optimal filtering.

[0069] The reception desk can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if the user is stressed, the reception desk can lower the priority of the request and provide a relaxing environment. For example, if the user is relaxed, the reception desk can raise the priority of the request and start processing it quickly. The reception desk can also give the highest priority to requests when the user is in a hurry, postponing other processing. In this way, the reception desk can adjust the priority of requests 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 desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into an AI, which can determine the optimal priority.

[0070] The reception unit can prioritize requests based on their relevance, taking into account the user's geographical location. For example, the reception unit can prioritize requests based on the user's current location. For example, the reception unit can filter the content of requests based on the user's geographical location and provide appropriate information. The reception unit can also determine the priority of requests based on the user's geographical location. This allows the reception unit to prioritize requests based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location into AI, which can then select the most appropriate requests.

[0071] The reception unit can analyze the user's social media activity when receiving a request and accept relevant requests. For example, the reception unit can prioritize requests that are highly relevant based on the user's social media activity. For example, the reception unit can analyze the user's social media activity, filter the content of requests, and provide appropriate information. The reception unit can also determine the priority of requests based on the user's social media activity. This allows the reception unit to accept requests based on 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, which can then select the most appropriate requests.

[0072] The generation unit can estimate the user's emotions and adjust the output's presentation based on the estimated emotions. For example, if the user is relaxed, the generation unit generates output that includes detailed explanations. For example, if the user is in a hurry, the generation unit generates concise output that gets straight to the point. The generation unit can also generate output with visually stimulating effects if the user is excited. In this way, the generation unit can adjust the output's presentation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation 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 AI, or not using AI. For example, the generation unit can input user emotion data into an AI, which can then determine the optimal presentation method.

[0073] The generation unit can adjust the level of detail of the output based on the importance of the request when generating the output. For example, the generation unit generates an output with detailed information for high-importance requests, and an output with concise information for low-importance requests. The generation unit can also dynamically adjust the level of detail of the output according to the importance of the request. This allows the generation unit to adjust the level of detail of the output according to the importance of the request. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input request importance data into the AI, and the AI ​​can determine the optimal level of detail.

[0074] The generation unit can apply different generation algorithms depending on the request category when generating output. For example, for a request related to obtaining a qualification, the generation unit can apply an algorithm that generates a learning plan. For example, for a request related to a company, the generation unit can apply an algorithm that generates a business plan. The generation unit can also apply an algorithm that generates practice methods for requests related to hobbies or special skills. This allows the generation unit to apply the most suitable generation algorithm depending on the request 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 request category data into the generation AI, which can then apply the most suitable generation algorithm.

[0075] The generation unit can estimate the user's emotions and adjust the length of the output based on the estimated emotions. For example, if the user is in a hurry, the generation unit will produce a short, concise output. For example, if the user is relaxed, the generation unit will produce a longer output that includes detailed explanations. The generation unit can also produce an output with visually stimulating effects if the user is excited. In this way, the generation unit can adjust the length of the output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit can input user emotion data into an AI, which can determine the optimal output length.

[0076] The generation unit can determine the priority of outputs based on the submission timing of requests when generating outputs. For example, the generation unit will prioritize output generation for requests submitted earlier. For example, the generation unit will postpone output generation for requests submitted later. The generation unit can also dynamically adjust the output priority according to the submission timing of requests. This allows the generation unit to adjust the output priority according to the submission timing of requests. 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 request submission timing data into a generation AI, which can then determine the optimal priority.

[0077] The generation unit can adjust the order of outputs based on the relevance of the requests when generating outputs. For example, the generation unit will prioritize generating outputs for highly relevant requests. For example, the generation unit will postpone generating outputs for less relevant requests. The generation unit can also dynamically adjust the order of outputs according to the relevance of the requests. This allows the generation unit to adjust the order of outputs according to the relevance of the requests. 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 request relevance data into a generation AI, which can then determine the optimal order.

[0078] The service provider can estimate the user's emotions and adjust the way information is displayed based on the estimated emotions. For example, if the user is nervous, the service provider may provide a simple and highly visible display method. For example, if the user is relaxed, the service provider may provide a display method that includes detailed information. The service provider may also provide a concise display method if the user is in a hurry. In this way, the service provider can adjust the way information is displayed 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 may be, 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 an AI, which can then determine the optimal display method.

[0079] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods previously used by the user. For example, the service provider may predict and provide the optimal display method based on the user's past operation history. The service provider may also analyze the user's past operation history and provide a display method with high visibility. In this way, the service provider can provide the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history into AI, and the AI ​​may select the optimal display method.

[0080] The information provider can customize the information provided based on the user's current living situation. For example, the provider can prioritize providing highly relevant information based on the user's current living situation. For example, the provider can customize how the information is displayed according to the user's living situation. The provider can also determine the priority of information based on the user's living situation. This allows the provider to customize the information according to the user's living situation. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the user's living situation data into AI, which can then customize the information to be optimal.

[0081] The service provider can estimate the user's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing relaxing information. For example, if the user is relaxed, the service provider will prioritize providing detailed information. The service provider can also prioritize providing concise information if the user is in a hurry. In this way, the service provider can adjust the priority of information 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, for example, or not using AI. For example, the service provider can input user emotion data into an AI, which can determine the optimal priority.

[0082] The information provider can provide optimal information by considering the user's geographical location information at the time of delivery. For example, the information provider can prioritize providing highly relevant information based on the user's current location. For example, the information provider can customize how information is displayed based on the user's geographical location information. The information provider can also determine the priority of information based on the user's geographical location information. In this way, the information provider can provide optimal information based on the user's geographical location information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the user's geographical location information into AI, and the AI ​​can provide optimal information.

[0083] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can prioritize providing highly relevant information based on the user's social media activity. For example, the service provider can analyze the user's social media activity and customize how the information is displayed. The service provider can also determine the priority of information based on the user's social media activity. This allows the service provider to provide relevant information based on the user's social media activity. 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 user's social media activity into AI, which can then provide the most relevant information.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The reception desk can prioritize user requests by referencing the user's past successes. For example, if a user has previously obtained a qualification, that success can be used to prioritize new requests. Similarly, if a user has achieved success in a particular hobby or skill, requests related to that area can be prioritized. Furthermore, the reception desk can analyze the user's past successes and provide optimal advice based on the content of the request. This allows the reception desk to adjust request priorities based on the user's past successes and provide more effective support.

[0086] The generation unit can estimate the user's emotions and customize the content of the output based on those emotions. For example, if the user is feeling anxious, the generation unit can generate output that includes encouraging messages to provide reassurance. If the user is excited, the generation unit can generate output that includes energetic expressions to boost motivation. Furthermore, if the user is relaxed, the generation unit can generate output that includes detailed information. This allows the generation unit to customize the content of the output according to the user's emotions and provide more personalized support.

[0087] The service provider can provide region-specific information by taking into account the user's geographical location. For example, if a user lives in a specific region, it can provide information on certification seminars and workshops held in that region. If a user is aiming to start a business in a specific region, it can provide information on the local business environment and support systems. Furthermore, if a user wants to hone their hobbies or skills in a specific region, it can provide information on clubs and circles active in that area. This allows the service provider to deliver more relevant information based on the user's geographical location.

[0088] The information provider can estimate the user's emotions and adjust how information is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. In this way, the information provider can adjust how information is displayed according to the user's emotions, enabling more effective information delivery.

[0089] The information delivery unit can determine the optimal timing for providing information by referring to the user's past operation history. For example, if the user has previously obtained information during a specific time period, the information can be provided at that time. Similarly, if the user has previously obtained information on a specific day of the week, the information can be provided at that day. Furthermore, if the user has previously obtained information during a specific event or situation, the information can be provided at that event or situation. This allows the information delivery unit to determine the optimal timing for providing information based on the user's past operation history, enabling them to provide more effective support.

[0090] The reception desk can estimate the user's emotions and adjust the request processing method based on those emotions. For example, if the user is stressed, the request processing can be simplified and processed quickly. If the user is relaxed, an interface for entering detailed information can be provided. Furthermore, if the user is in a hurry, quick processing methods such as voice input or gesture control can be provided. In this way, the reception desk can adjust the request processing method according to the user's emotions, providing a smoother user experience.

[0091] The generation unit can analyze the user's past request history and generate the optimal output according to the content of the request. For example, if a user has made many requests related to obtaining qualifications in the past, it can generate detailed output specialized in that field. Also, if a user has made many requests related to hobbies or special skills in the past, it can provide specific advice related to those fields. Furthermore, if a user has made many requests related to companies in the past, it can generate output that includes the latest information and trends in that field. In this way, the generation unit can provide more personalized output based on the user's past request history.

[0092] The information delivery unit can estimate the user's emotions and prioritize information based on those emotions. For example, if a user is stressed, it can prioritize providing information that promotes relaxation. If a user is relaxed, it can prioritize providing detailed information. Furthermore, if a user is in a hurry, it can prioritize providing concise information. In this way, the information delivery unit can adjust the priority of information according to the user's emotions, enabling more effective information delivery.

[0093] The reception desk can customize how requests are processed, taking into account the user's geographical location. For example, if a user lives in a specific region, the request processing method can be adjusted based on the services and resources available in that region. Similarly, if a user is traveling, a request processing method tailored to the specific needs of that region can be provided. Furthermore, if a user is aiming to establish a business in a specific region, the request processing method can be customized based on the local business environment and support systems. This allows the reception desk to provide more relevant request processing methods based on the user's geographical location.

[0094] The generation unit can estimate the user's emotions and adjust the length of the output based on those emotions. For example, if the user is in a hurry, it can generate a short, concise output. If the user is relaxed, it can generate a longer output that includes detailed explanations. Furthermore, if the user is excited, it can generate an output with visually stimulating effects. In this way, the generation unit can adjust the length of the output according to the user's emotions, providing more effective information.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk receives user requests. For example, users can enter requests regarding qualifications, hobbies, or special skills. The reception desk accepts requests via web forms or mobile applications. It can also accept requests using voice input or chatbots. Step 2: The generation unit analyzes the request received by the reception unit and generates the optimal output based on the user's attributes and behavioral information. The generation unit uses generation AI to analyze the request and generate information such as books and study time required to obtain qualifications, skills and procedures required to start a business, and practice methods to improve dance skills. Step 3: The delivery unit provides the output generated by the generation unit. The delivery unit provides information through websites and mobile applications. It can also provide information via email and push notifications. Furthermore, it provides reference web content, book and product information, success stories, schedules, costs, and other relevant information.

[0097] 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.

[0098] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0099] 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.

[0100] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and emotion estimation function, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates the optimal output based on user attributes and behavioral information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated output to the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of request acceptance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates the optimal output based on the user's attributes and behavioral information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated output to the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing device 12 and estimates the user's emotions and adjusts the timing of request acceptance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and emotion estimation function, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal output based on user attributes and behavioral information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated output to the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the timing of request acceptance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0147] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, and emotion estimation function, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user requests. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates the optimal output based on the user's attributes and behavioral information. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated output to the user. The emotion estimation function is implemented by the specific processing unit 290 of the data processing unit 12 and estimates the user's emotions and adjusts the timing of request acceptance. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A reception desk that accepts user requests, A generation unit analyzes the requests received by the reception unit and generates the optimal output based on user attributes and behavioral information, The system comprises a providing unit that provides the output generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is This tool generates information such as the books and study time required to obtain qualifications, the skills and procedures needed to start a business, and practice methods to improve dance skills. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide helpful web content, book and product information, success stories, schedules, costs, and other related information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides a user-friendly UI / UX. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We will develop a reward system that contributes to someone's "start" by providing information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of request acceptance based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When a request is received, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a request is received, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the way the output is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating output, adjust the level of detail of the output based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating output, different generation algorithms are applied depending on the request category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the output based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating output, prioritize the output based on when the request was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating output, the order of the output is adjusted based on the relevance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, the information will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing information, we will consider the user's geographical location to provide the most suitable information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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 that accepts user requests, A generation unit analyzes the requests received by the reception unit and generates the optimal output based on user attributes and behavioral information, The system comprises a providing unit that provides the output generated by the generation unit. A system characterized by the following features.

2. The generating unit is This tool generates information such as the books and study time required to obtain qualifications, the skills and procedures needed to start a business, and practice methods to improve dance skills. The system according to feature 1.

3. The aforementioned supply unit is, We provide helpful web content, book and product information, success stories, schedules, costs, and other related information. The system according to feature 1.

4. The aforementioned supply unit is, Provides a user-friendly UI / UX. The system according to feature 1.

5. The aforementioned supply unit is, We will develop a reward system that contributes to someone's start by providing information. The system according to feature 1.

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

7. The aforementioned reception unit is Analyze the user's past request history and select the optimal method of processing requests. The system according to feature 1.

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

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving a request, the system prioritizes requests that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

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