system

Abstract

The system according to this embodiment aims to streamline a company's customer service functions and quickly resolve user problems. [Solution] The system according to the embodiment comprises a registration unit, a resolution unit, and a learning unit. The registration unit is used by companies to register product information and support information. The resolution unit solves user problems based on the information registered by the registration unit. The learning unit learns user feedback on the solutions provided by the resolution unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that the customer service function of a company is not sufficiently efficient, and it takes time to solve users' problems.

[0005] The system according to the embodiment aims to improve the customer service function of a company and quickly solve users' problems.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, a solution unit, and a learning unit. The registration unit registers product information and support information of a company. The solution unit solves users' problems based on the information registered by the registration unit. The learning unit learns users' feedback on the solutions provided by the solution unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline a company's customer service functions and quickly resolve user problems. [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) An AI support system according to an embodiment of the present invention is a system that improves and streamlines a company's customer service functions. This AI support system allows companies to register product and support information with the AI, enabling users to solve problems using the AI ​​system. Companies do not have their own systems and rely on the AI ​​system provided by SB. Because the AI ​​is constantly learning, SB can focus solely on providing the system. For example, a company registers product and support information with the AI. In this process, the company inputs its product information, FAQs, and past problem occurrence and resolution information into the AI. For example, by registering information on product usage and troubleshooting with the AI, users can obtain this information through the AI. Next, users solve problems using the AI ​​system. When a user has questions or problems with a product, they contact the AI ​​system. For example, if a user inputs a problem such as "the product isn't working" into the AI, the AI ​​provides a solution based on the registered information. This allows users to solve problems quickly and accurately. Furthermore, because the AI ​​is constantly learning, it can incorporate user feedback and provide more advanced support. For example, by providing feedback on the provided solution, the AI ​​learns this information and can reflect it in future support. This system eliminates the need for companies to maintain separate support departments, thus enabling cost reductions. Furthermore, users can enjoy a unified service experience. For example, they can receive support for products from different companies through a single AI system, eliminating the need to contact multiple support departments. In this way, AI-powered customer service systems improve and streamline a company's support functions and provide a highly convenient system for users. Thus, AI support systems can improve and streamline a company's customer service functions and provide a highly convenient system for users.

[0029] The AI ​​support system according to this embodiment comprises a registration unit, a resolution unit, and a learning unit. The registration unit is used by companies to register product information and support information. The product information registered by companies includes, but is not limited to, product specifications, usage instructions, and troubleshooting information. For example, companies can input their own product information, FAQs, and past problem occurrence and resolution information into the registration unit. For example, companies can input information on product usage and troubleshooting into the registration unit, allowing users to obtain this information through the AI. The resolution unit solves user problems based on the information registered by the registration unit. For example, when a user inputs a question or problem regarding a product, the resolution unit provides a solution based on the registered information. For example, if a user inputs a problem such as "the product is not working," the resolution unit can provide a solution. The resolution unit can also provide a solution if a user has a question about how to set up the product. Furthermore, the resolution unit can provide a solution if a user asks a question about troubleshooting the product. The learning unit learns user feedback on the solutions provided by the resolution unit. The learning unit can learn from user feedback on the provided solutions and incorporate that information into future support. The learning unit can also collect user feedback and incorporate it into future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. As a result, the AI ​​support system according to this embodiment can improve and streamline a company's customer service functions and provide a highly convenient system for users.

[0030] The registration section allows companies to register product and support information. This information includes, but is not limited to, product specifications, usage instructions, and troubleshooting information. Specifically, companies can input their product information, FAQs, and past problem occurrences and solutions. For example, a company can input information on product usage and troubleshooting into the registration section, making this information accessible to users through AI. The registration section centrally manages the information provided by companies, allowing for updates and additions as needed. Companies can register and update product information through a dedicated management screen, ensuring that users always receive the latest information. Furthermore, the registration section features a database for unified management of different data formats, efficiently storing and managing text data, image data, video data, and more. For example, videos explaining product usage and images showing troubleshooting steps can also be stored in the registration section for easy user access. The registration section also includes verification functions to ensure the quality of information provided by companies, including a process to verify the accuracy of registered information. This allows users to obtain reliable information and contributes to improving the quality of customer service for companies.

[0031] The Resolution Unit solves user problems based on information registered by the Registration Unit. For example, when a user inputs a question or problem regarding a product, the Resolution Unit provides a solution based on the registered information. Specifically, if a user inputs a problem such as "the product is not working," the Resolution Unit can provide a solution. The Resolution Unit can also provide a solution if a user has a question about how to set up the product. Furthermore, the Resolution Unit can provide a solution if a user asks about troubleshooting the product. The Resolution Unit analyzes user input using natural language processing technology and searches for and provides the optimal solution. For example, it analyzes the text entered by the user, extracts relevant keywords, and searches for the optimal solution from the information stored in the Registration Unit. In addition, the Resolution Unit can refer to the user's past inquiry history and provide solutions optimized for each individual user. For example, for users who have reported similar problems in the past, it provides customized advice based on previously provided solutions. Moreover, when real-time support is required, the Resolution Unit can assist in problem solving by interacting with the user through a chatbot function. This allows the Resolution Unit to solve user problems quickly and accurately, improving user satisfaction.

[0032] The learning unit learns from user feedback on solutions provided by the solution unit. For example, the learning unit can learn from user feedback on provided solutions and reflect that information in future support. Specifically, when users rate solutions as "helpful" or "not helpful," the learning unit uses that evaluation to improve the accuracy of the solutions. The learning unit can also collect user feedback and reflect it in future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. For example, for solutions that users rate as "not helpful," the learning unit analyzes the reasons and makes improvements to provide more appropriate solutions. In addition, the learning unit can use machine learning algorithms to analyze user feedback data and extract patterns and trends in solutions. This allows the learning unit to continuously improve the solution delivery process and provide higher quality support to users. Moreover, the learning unit can work in conjunction with the solution unit to reflect feedback in real time, enabling a rapid response to user needs. This allows the learning unit to improve the overall support quality of the system and increase user satisfaction.

[0033] The resolution unit can provide solutions based on registered information when a user inputs questions or problems regarding the product. For example, if a user inputs a problem such as "the product is not working," the resolution unit will provide a solution. The resolution unit can also provide solutions if a user has questions about how to set up the product. Furthermore, the resolution unit can provide solutions if a user asks about troubleshooting the product. In this way, problems can be resolved quickly and accurately by providing solutions when a user inputs questions or problems regarding the product. Some or all of the above processing in the resolution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the resolution unit can input the problem entered by the user into a generative AI, and the generative AI can provide a solution.

[0034] The learning unit can collect user feedback and incorporate it into future support. For example, the learning unit learns from user feedback on the provided solutions and incorporates that information into future support. The learning unit can also collect user feedback and incorporate it into future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. This allows for the provision of more advanced support by collecting user feedback and incorporating it into future support. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user feedback into a generative AI, which can then learn that information.

[0035] The registration section allows companies to input their product information, FAQs, and past problem occurrence and resolution information. For example, the registration section allows companies to input their product information, FAQs, and past problem occurrence and resolution information. The product information registered by companies includes, but is not limited to, product specifications, usage instructions, and troubleshooting information. FAQs include, but are not limited to, frequently asked questions and their answers, and categorization. Past problem occurrence and resolution information includes, but is not limited to, past trouble cases and their solutions. This allows users to obtain this information by companies inputting their product information, FAQs, and past problem occurrence and resolution information. Some or all of the above processing in the registration section may be performed using, for example, a generating AI, or without a generating AI. For example, the registration section can input the information entered by the company into a generating AI, which can then register that information.

[0036] The solution unit can provide solutions when a user inputs a problem such as "the product is not working." For example, the solution unit can provide solutions when a user inputs a problem such as "the product is not working." The solution unit can also provide solutions when a user has questions about how to set up the product. Furthermore, the solution unit can provide solutions when a user asks questions about troubleshooting the product. This allows for quick problem resolution by providing solutions when a user inputs a problem such as "the product is not working." Some or all of the above-described processes in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's input problem into a generative AI, and the generative AI can provide a solution.

[0037] The learning unit can learn from user feedback and incorporate it into future support. For example, the learning unit learns from user feedback on the provided solutions and incorporates that information into future support. The learning unit can also collect user feedback and incorporate it into future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. In this way, the accuracy of support is improved by learning from user feedback and incorporating it into future support. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user feedback into a generative AI, which can then learn that information.

[0038] The registration unit can adjust the frequency of information updates based on the product lifecycle when registering a company's product information. For example, the registration unit updates information frequently immediately after the launch of a new product. For example, the registration unit can perform periodic updates during the product's maturity stage. Furthermore, during the product's end-of-life stage, the registration unit can update only essential information. This allows for the provision of always up-to-date information by adjusting the frequency of information updates based on the product lifecycle. Some or all of the above processing in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input product lifecycle data into a generative AI, which can then adjust the frequency of information updates.

[0039] The registration unit can automatically complete the most relevant information when entering registration information by referring to the company's past support history. For example, the registration unit can automatically complete frequently asked questions from past support history. The registration unit can also automatically complete solutions based on past troubleshooting information. Furthermore, the registration unit can automatically complete important information based on past user feedback. This improves the accuracy of the information by automatically completing the most relevant information by referring to the company's past support history. Some or all of the above processes in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input past support history data into a generative AI, which can then automatically complete the most relevant information.

[0040] The registration unit can customize registration information by region, taking into account the company's geographical market information. For example, the registration unit can customize product information based on regional demand. The registration unit can also customize support information based on regional culture and customs. Furthermore, the registration unit can customize product information based on regional laws and regulations. This allows for the provision of information tailored to each region by customizing information by taking into account the company's geographical market information. Some or all of the above processes in the registration unit may be performed using, for example, a generative AI, or not. For example, the registration unit can input geographical market information into a generative AI, which can then perform the region-specific customization.

[0041] The registration unit can analyze a company's social media activity and automatically add relevant information when registration information is entered. For example, the registration unit can automatically add FAQs based on user questions on social media. The registration unit can also automatically add product information based on product reviews on social media. Furthermore, the registration unit can automatically add support information based on social media trends. This improves the accuracy of the information by analyzing a company's social media activity and automatically adding relevant information. Some or all of the above processing in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input social media activity data into a generative AI, which can then automatically add relevant information.

[0042] The solution unit can propose the optimal solution by referring to the user's past inquiry history when providing a solution. For example, the solution unit can propose a solution to a problem similar to one the user has previously inquired about. For example, the solution unit can also propose the most effective solution based on the user's past inquiry history. Furthermore, the solution unit can propose a customized solution based on the user's past inquiry history. This improves the accuracy of the solution by proposing the optimal solution by referring to the user's past inquiry history. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's past inquiry history data into a generative AI, which can then propose the optimal solution.

[0043] The solution unit can provide customized solutions based on the product's usage environment. For example, if the product is used indoors, the solution unit can provide a solution suitable for the indoor environment. If the product is used outdoors, the solution unit can also provide a solution suitable for the outdoor environment. Furthermore, if the product is used in a specific industry, the solution unit can provide a solution suitable for that industry. By providing customized solutions based on the product's usage environment, a more appropriate solution can be provided. Some or all of the above-described processes in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input product usage environment data into a generative AI, and the generative AI can provide a customized solution.

[0044] The solution unit can provide region-specific solutions by considering the user's geographical location when providing solutions. For example, if the user is in a specific region, the solution unit can provide a solution suitable for that region. For example, if the user is traveling, the solution unit can also provide a solution suitable for the travel destination. Furthermore, if the user is in a different country, the solution unit can provide a solution suitable for the laws and regulations of that country. In this way, by providing region-specific solutions that consider the user's geographical location, it is possible to provide solutions suitable for the region. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's geographical location information into a generative AI, and the generative AI can provide a region-specific solution.

[0045] The solution unit can analyze the user's social media activity and propose relevant solutions when providing solutions. For example, the solution unit can propose solutions to problems shared by the user on social media. The solution unit can also propose relevant solutions based on the user's social media activity. Furthermore, the solution unit can propose solutions based on information about companies the user follows on social media. This improves the accuracy of the solutions by analyzing the user's social media activity and proposing relevant solutions. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's social media activity data into a generative AI, which can then propose relevant solutions.

[0046] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective algorithm from past learning data. The learning unit can also adjust the algorithm parameters based on past learning data. Furthermore, the learning unit can analyze past learning data and identify areas for improvement in the algorithm. By optimizing the learning algorithm by referring to past learning data, the accuracy of learning is improved. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI, which can then optimize the algorithm.

[0047] The learning unit can adjust the accuracy of learning based on the level of detail of user feedback during the learning process. For example, the learning unit can increase the accuracy of learning if the user feedback is detailed. It can also adjust the accuracy of learning if the user feedback is brief. Furthermore, the learning unit can optimize the accuracy of learning based on the content of the user feedback. This improves the accuracy of learning by adjusting the accuracy of learning based on the level of detail of user feedback. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user feedback data into a generative AI, which can then adjust the accuracy of learning based on the level of detail of the feedback.

[0048] The learning unit can prioritize region-specific learning data during training, taking into account the user's geographical location. For example, if the user is in a specific region, the learning unit will prioritize learning data for that region. If the user is traveling, the learning unit can also prioritize learning data for the travel destination. Furthermore, if the user is in a different country, the learning unit can prioritize learning data for that country. This enables region-appropriate learning by prioritizing region-specific learning data while considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI, which can then prioritize region-specific learning data.

[0049] The learning unit can analyze the user's social media activity during training and add relevant feedback to the training data. For example, the learning unit can add feedback shared by the user on social media to the training data. The learning unit can also add relevant feedback from the user's social media activity to the training data. Furthermore, the learning unit can add feedback to the training data based on information about companies that the user follows on social media. This improves the accuracy of learning by analyzing the user's social media activity and adding relevant feedback to the training data. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI, and the generative AI can add relevant feedback to the training data.

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

[0051] The AI ​​support system can further refer to the user's past purchase history to provide relevant support information. For example, it can prioritize displaying support information related to products the user has previously purchased. If the user frequently purchases products from a particular brand, it can also provide support information related to that brand. It can also provide information on how to use and troubleshoot products the user has previously purchased. By referring to the user's past purchase history and providing relevant support information, the system can provide the best possible support for the user. Some or all of the above processing in referring to purchase history may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, purchase history data can be input into a generative AI, and the generative AI can provide the relevant support information.

[0052] The AI ​​support system can further consider the user's geographical location to provide region-specific support information. For example, if the user is in a specific region, it can provide support information appropriate for that region. If the user is traveling, it can also provide support information appropriate for their destination. Furthermore, if the user is in a different country, it can provide support information appropriate for the laws and regulations of that country. In this way, by providing region-specific support information that takes the user's geographical location into consideration, it is possible to provide support appropriate for the region. Some or all of the above processing in referencing geographical location information may be performed using, for example, a generative AI, or without using a generative AI. For example, geographical location data can be input into a generative AI, and the generative AI can provide region-specific support information.

[0053] The AI ​​support system can further provide optimal support information by referring to the user's past support history. For example, it can provide support information for issues similar to those the user has previously inquired about. It can also provide the most effective support information based on the user's past support history. Furthermore, it can provide customized support information based on the user's past support history. This improves the accuracy of support by providing optimal support information by referring to the user's past support history. Some or all of the above processes in referring to the support history may be performed using, for example, a generative AI, or without a generative AI. For example, support history data can be input into a generative AI, and the generative AI can provide optimal support information.

[0054] The AI ​​support system can further analyze the user's social media activity and provide relevant support information. For example, it can provide support information for issues the user has shared on social media. It can also provide relevant support information based on the user's social media activity. Furthermore, it can provide support information based on information about companies the user follows on social media. This improves the accuracy of support by analyzing the user's social media activity and providing relevant support information. Some or all of the above-described processes in the analysis of social media activity may be performed using, for example, generative AI, or not using generative AI. For example, social media activity data can be input into generative AI, and the generative AI can provide relevant support information.

[0055] The AI ​​support system can further provide customized support information by taking into account the user's product usage environment. For example, if the product is used indoors, it can provide support information suitable for the indoor environment. If the product is used outdoors, it can also provide support information suitable for the outdoor environment. Furthermore, if the product is used in a specific industry, it can provide support information suitable for that industry. This allows for more appropriate support to be provided by providing customized support information based on the product's usage environment. Some or all of the above processing in referencing the product usage environment may be performed using, for example, a generative AI, or without using a generative AI. For example, product usage environment data can be input into a generative AI, and the generative AI can provide customized support information.

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

[0057] Step 1: The registration section allows companies to register product and support information. This information includes product specifications, usage instructions, and troubleshooting information. For example, companies can input their own product information, FAQs, and past problem occurrences and solutions. This allows users to access this information through AI. Step 2: The resolution unit solves the user's problem based on the information registered by the registration unit. When a user enters a question or problem regarding the product, the resolution unit provides a solution based on the registered information. For example, if a user enters a problem such as "the product is not working," the resolution unit can provide a solution. It can also provide solutions if the user has questions about how to set up the product or asks for troubleshooting advice. Step 3: The learning unit learns from user feedback on the solutions provided by the solution unit. By providing feedback on the solutions offered by users, the learning unit can learn from that information and reflect it in future support. This improves the accuracy of solutions, enhances and streamlines the company's customer service functions, and provides a more convenient system for users.

[0058] (Example of form 2) An AI support system according to an embodiment of the present invention is a system that improves and streamlines a company's customer service functions. This AI support system allows companies to register product and support information with the AI, enabling users to solve problems using the AI ​​system. Companies do not have their own systems and rely on the AI ​​system provided by SB. Because the AI ​​is constantly learning, SB can focus solely on providing the system. For example, a company registers product and support information with the AI. In this process, the company inputs its product information, FAQs, and past problem occurrence and resolution information into the AI. For example, by registering information on product usage and troubleshooting with the AI, users can obtain this information through the AI. Next, users solve problems using the AI ​​system. When a user has questions or problems with a product, they contact the AI ​​system. For example, if a user inputs a problem such as "the product isn't working" into the AI, the AI ​​provides a solution based on the registered information. This allows users to solve problems quickly and accurately. Furthermore, because the AI ​​is constantly learning, it can incorporate user feedback and provide more advanced support. For example, by providing feedback on the provided solution, the AI ​​learns this information and can reflect it in future support. This system eliminates the need for companies to maintain separate support departments, thus enabling cost reductions. Furthermore, users can enjoy a unified service experience. For example, they can receive support for products from different companies through a single AI system, eliminating the need to contact multiple support departments. In this way, AI-powered customer service systems improve and streamline a company's support functions and provide a highly convenient system for users. Thus, AI support systems can improve and streamline a company's customer service functions and provide a highly convenient system for users.

[0059] The AI ​​support system according to this embodiment comprises a registration unit, a resolution unit, and a learning unit. The registration unit is used by companies to register product information and support information. The product information registered by companies includes, but is not limited to, product specifications, usage instructions, and troubleshooting information. For example, companies can input their own product information, FAQs, and past problem occurrence and resolution information into the registration unit. For example, companies can input information on product usage and troubleshooting into the registration unit, allowing users to obtain this information through the AI. The resolution unit solves user problems based on the information registered by the registration unit. For example, when a user inputs a question or problem regarding a product, the resolution unit provides a solution based on the registered information. For example, if a user inputs a problem such as "the product is not working," the resolution unit can provide a solution. The resolution unit can also provide a solution if a user has a question about how to set up the product. Furthermore, the resolution unit can provide a solution if a user asks a question about troubleshooting the product. The learning unit learns user feedback on the solutions provided by the resolution unit. The learning unit can learn from user feedback on the provided solutions and incorporate that information into future support. The learning unit can also collect user feedback and incorporate it into future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. As a result, the AI ​​support system according to this embodiment can improve and streamline a company's customer service functions and provide a highly convenient system for users.

[0060] The registration section allows companies to register product and support information. This information includes, but is not limited to, product specifications, usage instructions, and troubleshooting information. Specifically, companies can input their product information, FAQs, and past problem occurrences and solutions. For example, a company can input information on product usage and troubleshooting into the registration section, making this information accessible to users through AI. The registration section centrally manages the information provided by companies, allowing for updates and additions as needed. Companies can register and update product information through a dedicated management screen, ensuring that users always receive the latest information. Furthermore, the registration section features a database for unified management of different data formats, efficiently storing and managing text data, image data, video data, and more. For example, videos explaining product usage and images showing troubleshooting steps can also be stored in the registration section for easy user access. The registration section also includes verification functions to ensure the quality of information provided by companies, including a process to verify the accuracy of registered information. This allows users to obtain reliable information and contributes to improving the quality of customer service for companies.

[0061] The Resolution Unit solves user problems based on information registered by the Registration Unit. For example, when a user inputs a question or problem regarding a product, the Resolution Unit provides a solution based on the registered information. Specifically, if a user inputs a problem such as "the product is not working," the Resolution Unit can provide a solution. The Resolution Unit can also provide a solution if a user has a question about how to set up the product. Furthermore, the Resolution Unit can provide a solution if a user asks about troubleshooting the product. The Resolution Unit analyzes user input using natural language processing technology and searches for and provides the optimal solution. For example, it analyzes the text entered by the user, extracts relevant keywords, and searches for the optimal solution from the information stored in the Registration Unit. In addition, the Resolution Unit can refer to the user's past inquiry history and provide solutions optimized for each individual user. For example, for users who have reported similar problems in the past, it provides customized advice based on previously provided solutions. Moreover, when real-time support is required, the Resolution Unit can assist in problem solving by interacting with the user through a chatbot function. This allows the Resolution Unit to solve user problems quickly and accurately, improving user satisfaction.

[0062] The learning unit learns from user feedback on solutions provided by the solution unit. For example, the learning unit can learn from user feedback on provided solutions and reflect that information in future support. Specifically, when users rate solutions as "helpful" or "not helpful," the learning unit uses that evaluation to improve the accuracy of the solutions. The learning unit can also collect user feedback and reflect it in future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. For example, for solutions that users rate as "not helpful," the learning unit analyzes the reasons and makes improvements to provide more appropriate solutions. In addition, the learning unit can use machine learning algorithms to analyze user feedback data and extract patterns and trends in solutions. This allows the learning unit to continuously improve the solution delivery process and provide higher quality support to users. Moreover, the learning unit can work in conjunction with the solution unit to reflect feedback in real time, enabling a rapid response to user needs. This allows the learning unit to improve the overall support quality of the system and increase user satisfaction.

[0063] The resolution unit can provide solutions based on registered information when a user inputs questions or problems regarding the product. For example, if a user inputs a problem such as "the product is not working," the resolution unit will provide a solution. The resolution unit can also provide solutions if a user has questions about how to set up the product. Furthermore, the resolution unit can provide solutions if a user asks about troubleshooting the product. In this way, problems can be resolved quickly and accurately by providing solutions when a user inputs questions or problems regarding the product. Some or all of the above processing in the resolution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the resolution unit can input the problem entered by the user into a generative AI, and the generative AI can provide a solution.

[0064] The learning unit can collect user feedback and incorporate it into future support. For example, the learning unit learns from user feedback on the provided solutions and incorporates that information into future support. The learning unit can also collect user feedback and incorporate it into future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. This allows for the provision of more advanced support by collecting user feedback and incorporating it into future support. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user feedback into a generative AI, which can then learn that information.

[0065] The registration section allows companies to input their product information, FAQs, and past problem occurrence and resolution information. For example, the registration section allows companies to input their product information, FAQs, and past problem occurrence and resolution information. The product information registered by companies includes, but is not limited to, product specifications, usage instructions, and troubleshooting information. FAQs include, but are not limited to, frequently asked questions and their answers, and categorization. Past problem occurrence and resolution information includes, but is not limited to, past trouble cases and their solutions. This allows users to obtain this information by companies inputting their product information, FAQs, and past problem occurrence and resolution information. Some or all of the above processing in the registration section may be performed using, for example, a generating AI, or without a generating AI. For example, the registration section can input the information entered by the company into a generating AI, which can then register that information.

[0066] The solution unit can provide solutions when a user inputs a problem such as "the product is not working." For example, the solution unit can provide solutions when a user inputs a problem such as "the product is not working." The solution unit can also provide solutions when a user has questions about how to set up the product. Furthermore, the solution unit can provide solutions when a user asks questions about troubleshooting the product. This allows for quick problem resolution by providing solutions when a user inputs a problem such as "the product is not working." Some or all of the above-described processes in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's input problem into a generative AI, and the generative AI can provide a solution.

[0067] The learning unit can learn from user feedback and incorporate it into future support. For example, the learning unit learns from user feedback on the provided solutions and incorporates that information into future support. The learning unit can also collect user feedback and incorporate it into future support. Furthermore, the learning unit can improve the accuracy of solutions based on user feedback. In this way, the accuracy of support is improved by learning from user feedback and incorporating it into future support. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user feedback into a generative AI, which can then learn that information.

[0068] The registration unit can estimate the user's emotions and prioritize registration information based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize displaying important support information. If the user is relaxed, the registration unit may also prioritize displaying detailed product information. Furthermore, if the user is in a hurry, the registration unit may prioritize displaying the simplest solutions. This allows the system to provide the user with the most relevant information by prioritizing registration information based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the registration unit may be performed using or without a generative AI. For example, the registration unit can input user emotion data into a generative AI, which will estimate the emotions and prioritize information based on the results.

[0069] The registration unit can adjust the frequency of information updates based on the product lifecycle when registering a company's product information. For example, the registration unit updates information frequently immediately after the launch of a new product. For example, the registration unit can perform periodic updates during the product's maturity stage. Furthermore, during the product's end-of-life stage, the registration unit can update only essential information. This allows for the provision of always up-to-date information by adjusting the frequency of information updates based on the product lifecycle. Some or all of the above processing in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input product lifecycle data into a generative AI, which can then adjust the frequency of information updates.

[0070] The registration unit can automatically complete the most relevant information when entering registration information by referring to the company's past support history. For example, the registration unit can automatically complete frequently asked questions from past support history. The registration unit can also automatically complete solutions based on past troubleshooting information. Furthermore, the registration unit can automatically complete important information based on past user feedback. This improves the accuracy of the information by automatically completing the most relevant information by referring to the company's past support history. Some or all of the above processes in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input past support history data into a generative AI, which can then automatically complete the most relevant information.

[0071] The registration unit can estimate the user's emotions and adjust the display method of the registration information based on the estimated emotions. For example, if the user is nervous, the registration unit can provide a simple and highly visible display method. If the user is relaxed, the registration unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the registration unit can provide a concise display method. In this way, by adjusting the display method of the registration information based on the user's emotions, the optimal display method can be provided to the user. 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 registration unit may be performed using a generative AI, or not using a generative AI. For example, the registration unit can input the user's emotion data into a generative AI, the generative AI can estimate the emotion, and the display method can be adjusted based on the result.

[0072] The registration unit can customize registration information by region, taking into account the company's geographical market information. For example, the registration unit can customize product information based on regional demand. The registration unit can also customize support information based on regional culture and customs. Furthermore, the registration unit can customize product information based on regional laws and regulations. This allows for the provision of information tailored to each region by customizing information by taking into account the company's geographical market information. Some or all of the above processes in the registration unit may be performed using, for example, a generative AI, or not. For example, the registration unit can input geographical market information into a generative AI, which can then perform the region-specific customization.

[0073] The registration unit can analyze a company's social media activity and automatically add relevant information when registration information is entered. For example, the registration unit can automatically add FAQs based on user questions on social media. The registration unit can also automatically add product information based on product reviews on social media. Furthermore, the registration unit can automatically add support information based on social media trends. This improves the accuracy of the information by analyzing a company's social media activity and automatically adding relevant information. Some or all of the above processing in the registration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the registration unit can input social media activity data into a generative AI, which can then automatically add relevant information.

[0074] The solution unit can estimate the user's emotions and adjust the way the solution is presented based on the estimated emotions. For example, if the user is stressed, the solution unit can provide a concise and easy-to-understand solution. If the user is relaxed, the solution unit can also provide a detailed solution. Furthermore, if the user is in a hurry, the solution unit can provide a solution that can be implemented quickly. In this way, by adjusting the way the solution is presented based on the user's emotions, the optimal solution for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the solution unit may be performed using a generative AI, or not using a generative AI. For example, the solution unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the way the solution is presented can be adjusted based on the result.

[0075] The solution unit can propose the optimal solution by referring to the user's past inquiry history when providing a solution. For example, the solution unit can propose a solution to a problem similar to one the user has previously inquired about. For example, the solution unit can also propose the most effective solution based on the user's past inquiry history. Furthermore, the solution unit can propose a customized solution based on the user's past inquiry history. This improves the accuracy of the solution by proposing the optimal solution by referring to the user's past inquiry history. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's past inquiry history data into a generative AI, which can then propose the optimal solution.

[0076] The solution unit can provide customized solutions based on the product's usage environment. For example, if the product is used indoors, the solution unit can provide a solution suitable for the indoor environment. If the product is used outdoors, the solution unit can also provide a solution suitable for the outdoor environment. Furthermore, if the product is used in a specific industry, the solution unit can provide a solution suitable for that industry. By providing customized solutions based on the product's usage environment, a more appropriate solution can be provided. Some or all of the above-described processes in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input product usage environment data into a generative AI, and the generative AI can provide a customized solution.

[0077] The solution unit can estimate the user's emotions and prioritize solutions based on those emotions. For example, if the user is stressed, the solution unit will prioritize providing the simplest solution. If the user is relaxed, the solution unit may also prioritize providing a more detailed solution. Furthermore, if the user is in a hurry, the solution unit may prioritize providing a solution that can be implemented quickly. This allows the solution unit to provide the optimal solution for the user by prioritizing solutions based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the solution unit may be performed using a generative AI, or not. For example, the solution unit can input user emotion data into a generative AI, which will estimate the emotions and determine the priority of solutions based on the results.

[0078] The solution unit can provide region-specific solutions by considering the user's geographical location when providing solutions. For example, if the user is in a specific region, the solution unit can provide a solution suitable for that region. For example, if the user is traveling, the solution unit can also provide a solution suitable for the travel destination. Furthermore, if the user is in a different country, the solution unit can provide a solution suitable for the laws and regulations of that country. In this way, by providing region-specific solutions that consider the user's geographical location, it is possible to provide solutions suitable for the region. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's geographical location information into a generative AI, and the generative AI can provide a region-specific solution.

[0079] The solution unit can analyze the user's social media activity and propose relevant solutions when providing solutions. For example, the solution unit can propose solutions to problems shared by the user on social media. The solution unit can also propose relevant solutions based on the user's social media activity. Furthermore, the solution unit can propose solutions based on information about companies the user follows on social media. This improves the accuracy of the solutions by analyzing the user's social media activity and proposing relevant solutions. Some or all of the above processing in the solution unit may be performed using, for example, a generative AI, or without a generative AI. For example, the solution unit can input the user's social media activity data into a generative AI, which can then propose relevant solutions.

[0080] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit may prioritize simple solutions as training data. If the user is relaxed, the learning unit may also prioritize detailed solutions as training data. Furthermore, if the user is in a hurry, the learning unit may prioritize solutions that can be quickly implemented as training data. This improves the accuracy of learning by selecting training data based on 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 learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the learning unit can select training data based on the results.

[0081] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the most effective algorithm from past learning data. The learning unit can also adjust the algorithm parameters based on past learning data. Furthermore, the learning unit can analyze past learning data and identify areas for improvement in the algorithm. By optimizing the learning algorithm by referring to past learning data, the accuracy of learning is improved. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input past learning data into a generative AI, which can then optimize the algorithm.

[0082] The learning unit can adjust the accuracy of learning based on the level of detail of user feedback during the learning process. For example, the learning unit can increase the accuracy of learning if the user feedback is detailed. It can also adjust the accuracy of learning if the user feedback is brief. Furthermore, the learning unit can optimize the accuracy of learning based on the content of the user feedback. This improves the accuracy of learning by adjusting the accuracy of learning based on the level of detail of user feedback. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input user feedback data into a generative AI, which can then adjust the accuracy of learning based on the level of detail of the feedback.

[0083] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit can increase the learning frequency if the user is stressed. It can also adjust the learning frequency if the user is relaxed. Furthermore, the learning unit can optimize the learning frequency if the user is in a hurry. This improves learning efficiency by adjusting the learning frequency based on 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 learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the learning frequency can be adjusted based on the result.

[0084] The learning unit can prioritize region-specific learning data during training, taking into account the user's geographical location. For example, if the user is in a specific region, the learning unit will prioritize learning data for that region. If the user is traveling, the learning unit can also prioritize learning data for the travel destination. Furthermore, if the user is in a different country, the learning unit can prioritize learning data for that country. This enables region-appropriate learning by prioritizing region-specific learning data while considering the user's geographical location. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI, which can then prioritize region-specific learning data.

[0085] The learning unit can analyze the user's social media activity during training and add relevant feedback to the training data. For example, the learning unit can add feedback shared by the user on social media to the training data. The learning unit can also add relevant feedback from the user's social media activity to the training data. Furthermore, the learning unit can add feedback to the training data based on information about companies that the user follows on social media. This improves the accuracy of learning by analyzing the user's social media activity and adding relevant feedback to the training data. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI, and the generative AI can add relevant feedback to the training data.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The AI ​​support system can further estimate the user's emotions and adjust the timing of support based on those emotions. For example, if the user is stressed, support can be provided immediately. If the user is relaxed, support can be provided with a slight delay. Also, if the user is in a hurry, the system can provide the most prompt support available. In this way, by adjusting the timing of support based on the user's emotions, the system can provide the optimal support for the user. 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-described processes in adjusting the timing of support may be performed using a generative AI, or not using a generative AI. For example, the support timing adjustment unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the timing of support can be adjusted based on the result.

[0088] The AI ​​support system can further refer to the user's past purchase history to provide relevant support information. For example, it can prioritize displaying support information related to products the user has previously purchased. If the user frequently purchases products from a particular brand, it can also provide support information related to that brand. It can also provide information on how to use and troubleshoot products the user has previously purchased. By referring to the user's past purchase history and providing relevant support information, the system can provide the best possible support for the user. Some or all of the above processing in referring to purchase history may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, purchase history data can be input into a generative AI, and the generative AI can provide the relevant support information.

[0089] The AI ​​support system can further estimate the user's emotions and customize the support content based on the estimated emotions. For example, if the user is stressed, it can provide concise and easy-to-understand support information. If the user is relaxed, it can provide detailed support information. If the user is in a hurry, it can provide support information that can be acted upon quickly. In this way, by customizing the support content based on the user's emotions, the system can provide the optimal support for the user. 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-described processes in customizing the support content may be performed using a generative AI, or not using a generative AI. For example, the support content customization unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the support content can be customized based on the result.

[0090] The AI ​​support system can further consider the user's geographical location to provide region-specific support information. For example, if the user is in a specific region, it can provide support information appropriate for that region. If the user is traveling, it can also provide support information appropriate for their destination. Furthermore, if the user is in a different country, it can provide support information appropriate for the laws and regulations of that country. In this way, by providing region-specific support information that takes the user's geographical location into consideration, it is possible to provide support appropriate for the region. Some or all of the above processing in referencing geographical location information may be performed using, for example, a generative AI, or without using a generative AI. For example, geographical location data can be input into a generative AI, and the generative AI can provide region-specific support information.

[0091] The AI ​​support system can further estimate the user's emotions and adjust the frequency of support based on the estimated emotions. For example, if the user is stressed, support can be provided more frequently. If the user is relaxed, the frequency of support can also be adjusted. If the user is in a hurry, support can be provided quickly. In this way, by adjusting the frequency of support based on the user's emotions, the system can provide the optimal support for the user. 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-described processes in adjusting the frequency of support may be performed using a generative AI, or not using a generative AI. For example, the support frequency adjustment unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the frequency of support can be adjusted based on the result.

[0092] The AI ​​support system can further provide optimal support information by referring to the user's past support history. For example, it can provide support information for issues similar to those the user has previously inquired about. It can also provide the most effective support information based on the user's past support history. Furthermore, it can provide customized support information based on the user's past support history. This improves the accuracy of support by providing optimal support information by referring to the user's past support history. Some or all of the above processes in referring to the support history may be performed using, for example, a generative AI, or without a generative AI. For example, support history data can be input into a generative AI, and the generative AI can provide optimal support information.

[0093] The AI ​​support system can further estimate the user's emotions and prioritize support based on those emotions. For example, if the user is stressed, the most important support information can be provided first. If the user is relaxed, detailed support information can be provided first. If the user is in a hurry, support information that can be acted on quickly can be provided first. In this way, by prioritizing support based on the user's emotions, the system can provide the best possible support for the user. 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 processes in determining support prioritization may be performed using a generative AI, or not using a generative AI. For example, the support prioritization unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the support prioritization can be determined based on the result.

[0094] The AI ​​support system can further analyze the user's social media activity and provide relevant support information. For example, it can provide support information for issues the user has shared on social media. It can also provide relevant support information based on the user's social media activity. Furthermore, it can provide support information based on information about companies the user follows on social media. This improves the accuracy of support by analyzing the user's social media activity and providing relevant support information. Some or all of the above-described processes in the analysis of social media activity may be performed using, for example, generative AI, or not using generative AI. For example, social media activity data can be input into generative AI, and the generative AI can provide relevant support information.

[0095] The AI ​​support system can further estimate the user's emotions and adjust the way support is presented based on those emotions. For example, if the user is stressed, it can provide concise and easy-to-understand support information. If the user is relaxed, it can provide detailed support information. If the user is in a hurry, it can provide support information that can be acted upon quickly. In this way, by adjusting the way support is presented based on the user's emotions, the system can provide the optimal support for the user. 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-described processes in adjusting the way support is presented may be performed using a generative AI or not. For example, the support presentation adjustment unit can input user emotion data into a generative AI, the generative AI can estimate the emotion, and the support presentation can be adjusted based on the result.

[0096] The AI ​​support system can further provide customized support information by taking into account the user's product usage environment. For example, if the product is used indoors, it can provide support information suitable for the indoor environment. If the product is used outdoors, it can also provide support information suitable for the outdoor environment. Furthermore, if the product is used in a specific industry, it can provide support information suitable for that industry. This allows for more appropriate support to be provided by providing customized support information based on the product's usage environment. Some or all of the above processing in referencing the product usage environment may be performed using, for example, a generative AI, or without using a generative AI. For example, product usage environment data can be input into a generative AI, and the generative AI can provide customized support information.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The registration section allows companies to register product and support information. This information includes product specifications, usage instructions, and troubleshooting information. For example, companies can input their own product information, FAQs, and past problem occurrences and solutions. This allows users to access this information through AI. Step 2: The resolution unit solves the user's problem based on the information registered by the registration unit. When a user enters a question or problem regarding the product, the resolution unit provides a solution based on the registered information. For example, if a user enters a problem such as "the product is not working," the resolution unit can provide a solution. It can also provide solutions if the user has questions about how to set up the product or asks for troubleshooting advice. Step 3: The learning unit learns from user feedback on the solutions provided by the solution unit. By providing feedback on the solutions offered by users, the learning unit can learn from that information and reflect it in future support. This improves the accuracy of solutions, enhances and streamlines the company's customer service functions, and provides a more convenient system for users.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the registration unit, resolution unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14, allowing companies to input product information and support information. The resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, solving user problems based on the registered information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, learning user feedback and reflecting it in subsequent support. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the registration unit, resolution unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214, allowing companies to input product information and support information. The resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, solving user problems based on the registered information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, learning user feedback and reflecting it in subsequent support. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the registration unit, resolution unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314, allowing companies to input product information and support information. The resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, solving user problems based on the registered information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, learning user feedback and reflecting it in subsequent support. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the registration unit, resolution unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414, allowing companies to input product information and support information. The resolution unit is implemented by the specific processing unit 290 of the data processing unit 12, solving user problems based on the registered information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12, learning user feedback and reflecting it in subsequent support. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A registration section where companies register product information and support information, A solution unit that solves user problems based on the information registered by the registration unit, The system includes a learning unit that learns user feedback on the solutions provided by the aforementioned solution unit. A system characterized by the following features. (Note 2) The aforementioned solution unit is When a user enters a question or problem regarding the product, the system provides a solution based on the registered information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, We collect user feedback and incorporate it into future support. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned registration unit is Companies enter their product information, FAQs, and past problem occurrences and solutions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned solution unit is When a user enters a problem such as "the product isn't working," solutions are provided. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, We learn from user feedback and incorporate it into future support. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is The system estimates the user's emotions and prioritizes registration information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is When registering a company's product information, adjust the frequency of information updates based on the product lifecycle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is When entering registration information, the system automatically fills in the most relevant information by referring to the company's past support history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is The system estimates the user's emotions and adjusts how registration information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When entering registration information, the system is customized for each region, taking into account the company's geographical market information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned registration unit is When you enter your registration information, the system analyzes your company's social media activity and automatically adds relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned solution unit is It estimates the user's emotions and adjusts how the solution is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned solution unit is When providing solutions, we refer to the user's past inquiry history to suggest the most suitable solution. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned solution unit is When providing solutions, we provide customized solutions based on the product's usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned solution unit is It estimates the user's emotions and determines the priority of solutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned solution unit is When providing solutions, we take the user's geographical location into consideration to provide region-specific solutions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned solution unit is When providing solutions, we analyze the user's social media activity and propose relevant solutions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During training, the accuracy of the learning process is adjusted based on the level of detail in the user's feedback. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During training, the system prioritizes region-specific training data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During training, the system analyzes users' social media activity and adds relevant feedback to the training data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 registration section where companies register product information and support information, A solution unit that solves user problems based on the information registered by the registration unit, The system includes a learning unit that learns user feedback on the solutions provided by the aforementioned solution unit. A system characterized by the following features.

2. The aforementioned solution unit is When a user enters a question or problem regarding the product, the system provides a solution based on the registered information. The system according to feature 1.

3. The aforementioned learning unit, We collect user feedback and incorporate it into future support. The system according to feature 1.

4. The aforementioned registration unit is Companies enter their product information, FAQs, and past problem occurrences and solutions. The system according to feature 1.

5. The aforementioned solution unit is When a user reports a problem such as the product not working, solutions are provided. The system according to feature 1.

6. The aforementioned learning unit, We learn from user feedback and incorporate it into future support. The system according to feature 1.

7. The aforementioned registration unit is The system estimates the user's emotions and prioritizes registration information based on those estimated emotions. The system according to feature 1.

8. The aforementioned registration unit is When registering a company's product information, adjust the frequency of information updates based on the product lifecycle. The system according to feature 1.

Images