system

Abstract

The system according to this embodiment aims to identify the most suitable vendor for the customer's challenges and provide specific advice. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit inputs the customer's problem. The analysis unit analyzes the problem input by the reception unit and identifies an appropriate vendor. The provision unit provides specific advice based on the vendor information identified by the analysis unit.

Description

Technical Field

[0006] , , ,

[0005] , , ,

[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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [[ID=十四]]

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 process of identifying the optimal vendor for the customer's problem and providing specific advice is not efficient.

[0005] The system according to the embodiment aims to identify the optimal vendor for the customer's problem and provide specific advice.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit inputs the customer's problem. The analysis unit analyzes the problem input by the reception unit and identifies an appropriate vendor. The provision unit provides specific advice based on the information of the vendor identified by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify the most suitable vendor for the customer's challenges and provide specific advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The matching platform according to an embodiment of the present invention is a system that efficiently matches customers seeking AI solutions with corporate vendors capable of providing those solutions. In this system, the customer inputs their challenges, a generating AI analyzes the input challenges, and identifies corporate vendors capable of providing appropriate AI solutions. The generating AI determines the degree of match between the challenges and solutions based on past data and the vendors' track record. Based on the identified vendors, the generating AI provides specific advice to the customer, enabling the customer to make the optimal choice. Furthermore, the system provides vendors with information to streamline their reach to customers. This creates a system that ensures success for both companies and vendors. Through this mechanism, customers can find the AI ​​solution best suited to their challenges, and vendors can efficiently reach customers. For example, if a customer has a technical problem, the generating AI identifies the most suitable vendor for that problem and proposes a specific solution. Similarly, for business challenges, the generating AI selects the optimal vendor based on past data and provides detailed advice to the customer. This allows customers to solve problems quickly and effectively. Furthermore, vendors can efficiently reach customers by being provided with information tailored to customer needs. For example, vendors can take the optimal approach based on past customer feedback and evaluations. This allows vendors to strengthen their relationships with customers and promote business success. As a result, the matching platform can provide the best solution for both customers and vendors.

[0029] The matching platform according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives customer challenges. Customer challenges include, but are not limited to, technical problems or business challenges. The reception unit accepts customer challenges, for example, through an online form. The reception unit can also accept challenges through telephone or face-to-face consultations. Furthermore, the reception unit can estimate the customer's emotions and adjust the timing of challenge input based on the estimated emotions. For example, if a customer is feeling stressed, it may encourage them to input their challenge during a time when they can relax. The analysis unit uses generative AI to analyze the challenges entered by the reception unit and identify appropriate vendors. The analysis unit determines the degree of match between the challenge and the solution, for example, based on past data and the vendor's track record. The generative AI uses text generation AI (e.g., LLM) to analyze customer challenges. The analysis unit can also use multimodal generative AI to analyze customer challenges. For example, the generative AI identifies the optimal vendor based on past success stories and customer feedback. The provision unit provides specific advice to the customer based on the vendor information identified by the analysis unit. The service provider can, for example, provide advice in the form of a report. Alternatively, the service provider can provide advice through proposals or verbal explanations. Furthermore, the service provider can estimate the customer's emotions and adjust the way the advice is presented based on those emotions. For example, if the customer is relaxed, detailed advice can be provided. This allows the matching platform, according to the embodiment, to efficiently analyze the customer's challenges, identify appropriate vendors, and provide specific advice.

[0030] The reception desk receives customer inquiries. These inquiries may include, but are not limited to, technical or business problems. The reception desk accepts customer inquiries through online forms, for example. These online forms have a user-friendly interface and are designed to allow customers to easily enter their inquiries. The forms include text boxes for entering details of the inquiry and dropdown menus for selecting options. The reception desk can also accept inquiries through telephone or in-person consultations. With telephone inquiries, a professional operator listens to the customer and records the inquiry in detail. With in-person consultations, the customer can speak directly with a representative and explain the inquiry in detail. Furthermore, the reception desk can estimate the customer's emotions and adjust the timing of inquiry submission based on the estimated emotions. For example, if a customer is feeling stressed, the reception desk may encourage them to submit the inquiry during a time when they can relax. Emotion estimation utilizes AI-powered emotion recognition technology. The AI ​​analyzes the customer's tone of voice, facial expressions, and input content to determine the appropriate timing. This allows the reception desk to support customers in entering their inquiries in the most relaxed state and to collect more accurate information.

[0031] The analysis unit uses generative AI to analyze the issues entered by the reception unit and identify appropriate vendors. The analysis unit determines the degree of match between the issue and the solution based on, for example, past data and the vendor's track record. The generative AI uses text generation AI (e.g., LLM) to analyze the customer's issue. Specifically, LLM analyzes the customer's input using natural language processing technology to understand the essence of the issue. Furthermore, the analysis unit can also analyze the customer's issue using multimodal generative AI. Multimodal generative AI has the ability to integrate and analyze different data formats such as images and audio, not just text. For example, the generative AI identifies the optimal vendor based on past success stories and customer feedback. The database of past success stories records solutions and results for similar issues, and the generative AI selects the best vendor by referring to this data. Furthermore, the generative AI analyzes customer feedback and takes into account the vendor's evaluation and reliability. This allows the analysis unit to quickly and accurately identify the vendor best suited to the customer's issue.

[0032] The service provider provides specific advice to customers based on vendor information identified by the analysis department. For example, the service provider may provide advice in the form of a report. The report may include detailed vendor information, past performance, and an overview of the proposed solution. The service provider can also provide advice through proposals and verbal explanations. Proposals detail specific solutions, implementation methods, and expected outcomes. Verbal explanations allow the service provider to deepen the customer's understanding by having a representative explain directly and answer questions. Furthermore, the service provider can estimate the customer's emotions and adjust the way advice is presented based on that estimation. For example, if the customer is relaxed, detailed advice will be provided. Conversely, if the customer is tense, concise and easy-to-understand advice will be provided. Emotion estimation utilizes AI-based emotion recognition technology. The AI ​​analyzes the customer's facial expressions, tone of voice, and reactions to select the most appropriate way to present advice. This allows the service provider to provide optimal advice tailored to the customer's situation, increasing customer satisfaction.

[0033] The service provider can provide specific advice to customers. For example, the service provider can create detailed reports on customer issues and propose solutions. The service provider can also create proposals and present concrete action plans to customers. Furthermore, the service provider can provide oral explanations and propose solutions through dialogue with customers. For example, the service provider can provide customized advice according to customer needs, enabling customers to make the best choices. Some or all of the above processes in the service provider may be performed using generative AI, or not. For example, the service provider can provide specific advice using a generative AI model that takes customer issues as input and outputs solutions.

[0034] The service provider can provide vendors with information to streamline their customer reach. For example, the service provider can provide target market data and propose strategies for vendors to efficiently reach customers. The service provider can also analyze customer purchase history and propose optimal approaches to vendors. Furthermore, the service provider can suggest improvements to vendors based on customer feedback. For example, the service provider can provide information tailored to customer needs and support vendors in efficiently reaching customers. This enables vendors to reach customers efficiently. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide information to vendors using a generative AI model that takes customer data as input and outputs the optimal approach.

[0035] The analysis unit can determine the degree of match between a problem and a solution based on past data and vendor performance. For example, the analysis unit collects past customer feedback and sales data to evaluate the degree of match between the problem and the solution. The analysis unit can also identify the optimal vendor based on vendor success stories and customer satisfaction. Furthermore, the analysis unit can determine the degree of match between a problem and a solution based on the vendor's area of ​​expertise and evaluation. For example, the analysis unit uses past data to identify the vendor best suited to the customer's problem. This allows for the identification of a more appropriate vendor. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can identify the optimal vendor using a generative AI model that takes past data as input and outputs the degree of match between the problem and the solution.

[0036] The reception desk can analyze a customer's past assignment submission history and select the optimal input method. For example, if a customer has preferred using text input in the past, the reception desk will prioritize suggesting text input. Similarly, if a customer has used voice input in the past, the reception desk can prioritize suggesting voice input. Furthermore, if a customer has previously submitted assignments during a specific time slot, the reception desk can prompt them to submit their assignments during that time slot. For example, the reception desk analyzes a customer's past submission history and selects the optimal input method, allowing the customer to submit their assignments in the most efficient way. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can select an input method using an AI model that takes the customer's past submission history data as input and outputs the optimal input method.

[0037] The reception desk can filter the input of tasks based on the customer's current projects and areas of interest. For example, the reception desk may prioritize displaying tasks related to projects the customer is currently working on. The reception desk can also filter and display relevant tasks based on the customer's areas of interest. Furthermore, the reception desk may prioritize displaying tasks related to areas the customer has shown interest in in the past. For example, the reception desk may prioritize displaying relevant tasks based on the progress of the customer's current projects. This allows the customer to prioritize inputting highly relevant tasks. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use an AI model that takes customer project data as input and outputs relevant tasks to perform filtering.

[0038] The reception desk can prioritize inputting highly relevant tasks based on the customer's geographical location information when a task is entered. For example, if the customer is in a specific region, the reception desk will prioritize inputting tasks related to that region. Furthermore, if the customer is on the move, the reception desk can prioritize inputting tasks related to their destination. Additionally, if the customer is in a specific city, the reception desk can prioritize inputting tasks related to that city. For example, the reception desk prioritizes inputting relevant tasks based on the customer's geographical location information. This allows the customer to input the most relevant task. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input tasks using an AI model that takes the customer's geographical location data as input and outputs relevant tasks.

[0039] The reception desk can analyze a customer's social media activity when they submit a request and input relevant requests. For example, the reception desk can prioritize inputting requests that the customer has mentioned on social media. It can also prioritize inputting requests related to topics the customer follows on social media. Furthermore, it can prioritize inputting requests related to groups the customer participates in on social media. For example, the reception desk analyzes the customer's social media activity and inputs relevant requests. This allows the customer to prioritize inputting highly relevant requests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input requests using an AI model that takes the customer's social media data as input and outputs relevant requests.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the issues during the analysis. For example, the analysis unit will perform a detailed analysis for issues of high importance. It can also perform a simplified analysis for issues of low importance. Furthermore, it can perform an analysis with an appropriate level of detail for issues of moderate importance. For example, the analysis unit will evaluate the importance of the customer's issues and adjust the level of detail of the analysis accordingly. This allows the customer to receive analysis results with the optimal level of detail. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can adjust the level of detail of the analysis using a generative AI model that takes customer issue data as input and outputs the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the problem during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical problems. It can also apply a business analysis algorithm to business-related problems. Furthermore, it can apply a marketing analysis algorithm to marketing-related problems. For example, the analysis unit selects the optimal analysis algorithm based on the category of the problem. This allows the customer to receive the optimal analysis results. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes customer problem data as input and outputs the optimal analysis algorithm.

[0042] The analysis unit can determine the priority of analysis based on the submission date of the assignments during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted assignments. It can also prioritize the analysis of assignments with approaching deadlines. Furthermore, it can postpone the analysis of assignments whose submission deadlines have passed. For example, the analysis unit may determine the priority of analysis based on the submission date of the customer's assignments. This allows the customer to receive the analysis results in the optimal order. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit may determine the priority of analysis using a generative AI model that takes customer assignment data as input and outputs the priority of analysis.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the issues during the analysis. For example, the analysis unit may prioritize analyzing issues related to the customer's current project. It may also prioritize analyzing issues related to the customer's areas of interest. Furthermore, the analysis unit may prioritize analyzing issues with high relevance based on the customer's past history. For example, the analysis unit may prioritize analyzing relevant issues based on the customer's current project data. This allows the customer to receive analysis results in the optimal order. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can adjust the order of analysis using a generative AI model that takes customer project data as input and outputs relevant issues.

[0044] The service provider can adjust the level of detail of advice based on the vendor's track record when providing advice. For example, the service provider may provide detailed advice to vendors with a proven track record. Conversely, it may provide concise advice to vendors with limited experience. Furthermore, it may provide advice of an appropriate level of detail to vendors with a moderate level of experience. For example, the service provider may adjust the level of detail of advice based on the vendor's success stories and customer satisfaction. This ensures that customers receive advice with the optimal level of detail. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can adjust the level of detail of advice using a generative AI model that takes vendor performance data as input and outputs the level of detail of the advice.

[0045] The service provider can provide optimal advice by referring to the customer's past feedback when providing advice. For example, the service provider may prioritize providing advice to customers who have previously given positive feedback on it. It can also avoid providing advice to customers who have previously given negative feedback on it. Furthermore, the service provider can analyze the customer's past feedback to provide optimal advice. For example, the service provider may select the optimal advice based on the customer's past feedback data. This ensures that the customer receives the most appropriate advice. Some or all of the above processes in the service provider may be performed using generative AI, or not. For example, the service provider may provide advice using a generative AI model that takes customer feedback data as input and outputs the optimal advice.

[0046] The service provider can provide optimal advice based on the customer's geographical location information when providing advice. For example, if the customer is in a specific region, the service provider can provide advice relevant to that region. Furthermore, if the customer is on the move, the service provider can provide advice relevant to their destination. Additionally, if the customer is in a specific city, the service provider can provide advice relevant to that city. For example, the service provider provides optimal advice based on the customer's geographical location information. This allows the customer to receive the most appropriate advice. Some or all of the above processing in the service provider may be performed using generative AI, or without generative AI. For example, the service provider can provide advice using a generative AI model that takes the customer's geographical location data as input and outputs optimal advice.

[0047] The service provider can analyze a customer's social media activity and provide relevant advice when offering advice. For example, the service provider can provide advice related to topics the customer has mentioned on social media. It can also provide advice related to vendors the customer follows on social media. Furthermore, it can provide advice related to groups the customer participates in on social media. For example, the service provider analyzes a customer's social media activity and provides relevant advice, allowing the customer to receive optimal advice. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide advice using a generative AI model that takes customer social media data as input and outputs relevant advice.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The reception desk can automatically suggest similar issues by referencing the customer's past problem-solving history when they input their issues. For example, if the current issue is similar to an issue the customer previously resolved, the reception desk will prompt them to refer to the past solution. The reception desk can also suggest solutions for new issues based on the customer's past successful solutions. Furthermore, the reception desk can suggest solutions to avoid solutions that the customer has failed at in the past. This allows the customer to efficiently solve issues by leveraging past experience. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can suggest issues using an AI model that takes the customer's past problem-solving history data as input and outputs similar issues.

[0050] The analysis unit can analyze customer challenges by referencing market trend data in real time and proposing the latest solutions. For example, the analysis unit can identify the optimal solution to a customer's challenge based on technologies and solutions currently attracting attention in the market. It can also analyze the activities of competitors and propose competitive solutions to customers. Furthermore, the analysis unit can refer to industry best practices to provide effective solutions to customers. This allows customers to select the optimal solution based on the latest information. Some or all of the above processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can perform analysis using a generative AI model that takes market trend data as input and outputs the latest solutions.

[0051] The service provider can adjust the format of advice given to customers according to their learning style. For example, if a customer is a visual learner, the service provider can provide advice using graphs and charts. If a customer is an auditory learner, the service provider can provide advice in the form of audio messages or podcasts. Furthermore, if a customer is an experiential learner, the service provider can provide advice in the form of interactive simulations or workshops. This allows customers to receive advice in a format that is best suited to their learning style. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can adjust the format of advice using an AI model that takes customer learning style data as input and outputs the optimal advice format.

[0052] The analysis unit can perform analyses based on the regulations and standards specific to the customer's industry when analyzing customer challenges. For example, the analysis unit can propose solutions compliant with medical regulations and standards to customers in the medical industry. It can also propose solutions compliant with financial regulations and standards to customers in the financial industry. Furthermore, it can propose solutions compliant with manufacturing regulations and standards to customers in the manufacturing industry. This allows customers to select solutions that conform to industry-specific regulations and standards. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can perform analyses using a generative AI model that takes industry regulatory data as input and outputs regulatory-compliant solutions.

[0053] The service provider can adjust the content of advice given to customers according to their cultural background. For example, if a customer has a different cultural background, the service provider will provide advice that takes that culture into consideration. The service provider can also adjust the way advice is expressed according to the customer's language and communication style. Furthermore, the service provider can provide appropriate advice based on the customer's cultural values ​​and customs. This allows customers to receive advice in a way that is appropriate to their own cultural background. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can adjust the content of advice using an AI model that takes the customer's cultural background data as input and outputs optimal advice.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception desk enters the customer's issue. This issue can include technical problems, business challenges, and more. The reception desk can receive the issue via online forms, phone calls, or in-person consultations. The reception desk can also estimate the customer's emotions and adjust the timing of issue submission based on those estimates. For example, if the customer is feeling stressed, they might be encouraged to submit the issue during a time when they can relax. Step 2: The analysis unit uses generative AI to analyze the issues entered by the reception unit and identify appropriate vendors. The analysis unit determines the degree of match between the issue and the solution based on past data and the vendor's track record. The generative AI analyzes the customer's issue using text generation AI (e.g., LLM) or multimodal generation AI. For example, it identifies the optimal vendor based on past success stories and customer feedback. Step 3: The service provider provides specific advice to the customer based on the vendor information identified by the analysis team. The service provider can provide advice through reports, proposals, or verbal explanations. The service provider can also estimate the customer's emotions and adjust the way the advice is presented based on those emotions. For example, if the customer is relaxed, they might provide more detailed advice.

[0056] (Example of form 2) The matching platform according to an embodiment of the present invention is a system that efficiently matches customers seeking AI solutions with corporate vendors capable of providing those solutions. In this system, the customer inputs their challenges, a generating AI analyzes the input challenges, and identifies corporate vendors capable of providing appropriate AI solutions. The generating AI determines the degree of match between the challenges and solutions based on past data and the vendors' track record. Based on the identified vendors, the generating AI provides specific advice to the customer, enabling the customer to make the optimal choice. Furthermore, the system provides vendors with information to streamline their reach to customers. This creates a system that ensures success for both companies and vendors. Through this mechanism, customers can find the AI ​​solution best suited to their challenges, and vendors can efficiently reach customers. For example, if a customer has a technical problem, the generating AI identifies the most suitable vendor for that problem and proposes a specific solution. Similarly, for business challenges, the generating AI selects the optimal vendor based on past data and provides detailed advice to the customer. This allows customers to solve problems quickly and effectively. Furthermore, vendors can efficiently reach customers by being provided with information tailored to customer needs. For example, vendors can take the optimal approach based on past customer feedback and evaluations. This allows vendors to strengthen their relationships with customers and promote business success. As a result, the matching platform can provide the best solution for both customers and vendors.

[0057] The matching platform according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives customer challenges. Customer challenges include, but are not limited to, technical problems or business challenges. The reception unit accepts customer challenges, for example, through an online form. The reception unit can also accept challenges through telephone or face-to-face consultations. Furthermore, the reception unit can estimate the customer's emotions and adjust the timing of challenge input based on the estimated emotions. For example, if a customer is feeling stressed, it may encourage them to input their challenge during a time when they can relax. The analysis unit uses generative AI to analyze the challenges entered by the reception unit and identify appropriate vendors. The analysis unit determines the degree of match between the challenge and the solution, for example, based on past data and the vendor's track record. The generative AI uses text generation AI (e.g., LLM) to analyze customer challenges. The analysis unit can also use multimodal generative AI to analyze customer challenges. For example, the generative AI identifies the optimal vendor based on past success stories and customer feedback. The provision unit provides specific advice to the customer based on the vendor information identified by the analysis unit. The service provider can, for example, provide advice in the form of a report. Alternatively, the service provider can provide advice through proposals or verbal explanations. Furthermore, the service provider can estimate the customer's emotions and adjust the way the advice is presented based on those emotions. For example, if the customer is relaxed, detailed advice can be provided. This allows the matching platform, according to the embodiment, to efficiently analyze the customer's challenges, identify appropriate vendors, and provide specific advice.

[0058] The reception desk receives customer inquiries. These inquiries may include, but are not limited to, technical or business problems. The reception desk accepts customer inquiries through online forms, for example. These online forms have a user-friendly interface and are designed to allow customers to easily enter their inquiries. The forms include text boxes for entering details of the inquiry and dropdown menus for selecting options. The reception desk can also accept inquiries through telephone or in-person consultations. With telephone inquiries, a professional operator listens to the customer and records the inquiry in detail. With in-person consultations, the customer can speak directly with a representative and explain the inquiry in detail. Furthermore, the reception desk can estimate the customer's emotions and adjust the timing of inquiry submission based on the estimated emotions. For example, if a customer is feeling stressed, the reception desk may encourage them to submit the inquiry during a time when they can relax. Emotion estimation utilizes AI-powered emotion recognition technology. The AI ​​analyzes the customer's tone of voice, facial expressions, and input content to determine the appropriate timing. This allows the reception desk to support customers in entering their inquiries in the most relaxed state and to collect more accurate information.

[0059] The analysis unit uses generative AI to analyze the issues entered by the reception unit and identify appropriate vendors. The analysis unit determines the degree of match between the issue and the solution based on, for example, past data and the vendor's track record. The generative AI uses text generation AI (e.g., LLM) to analyze the customer's issue. Specifically, LLM analyzes the customer's input using natural language processing technology to understand the essence of the issue. Furthermore, the analysis unit can also analyze the customer's issue using multimodal generative AI. Multimodal generative AI has the ability to integrate and analyze different data formats such as images and audio, not just text. For example, the generative AI identifies the optimal vendor based on past success stories and customer feedback. The database of past success stories records solutions and results for similar issues, and the generative AI selects the best vendor by referring to this data. Furthermore, the generative AI analyzes customer feedback and takes into account the vendor's evaluation and reliability. This allows the analysis unit to quickly and accurately identify the vendor best suited to the customer's issue.

[0060] The service provider provides specific advice to customers based on vendor information identified by the analysis department. For example, the service provider may provide advice in the form of a report. The report may include detailed vendor information, past performance, and an overview of the proposed solution. The service provider can also provide advice through proposals and verbal explanations. Proposals detail specific solutions, implementation methods, and expected outcomes. Verbal explanations allow the service provider to deepen the customer's understanding by having a representative explain directly and answer questions. Furthermore, the service provider can estimate the customer's emotions and adjust the way advice is presented based on that estimation. For example, if the customer is relaxed, detailed advice will be provided. Conversely, if the customer is tense, concise and easy-to-understand advice will be provided. Emotion estimation utilizes AI-based emotion recognition technology. The AI ​​analyzes the customer's facial expressions, tone of voice, and reactions to select the most appropriate way to present advice. This allows the service provider to provide optimal advice tailored to the customer's situation, increasing customer satisfaction.

[0061] The service provider can provide specific advice to customers. For example, the service provider can create detailed reports on customer issues and propose solutions. The service provider can also create proposals and present concrete action plans to customers. Furthermore, the service provider can provide oral explanations and propose solutions through dialogue with customers. For example, the service provider can provide customized advice according to customer needs, enabling customers to make the best choices. Some or all of the above processes in the service provider may be performed using generative AI, or not. For example, the service provider can provide specific advice using a generative AI model that takes customer issues as input and outputs solutions.

[0062] The service provider can provide vendors with information to streamline their customer reach. For example, the service provider can provide target market data and propose strategies for vendors to efficiently reach customers. The service provider can also analyze customer purchase history and propose optimal approaches to vendors. Furthermore, the service provider can suggest improvements to vendors based on customer feedback. For example, the service provider can provide information tailored to customer needs and support vendors in efficiently reaching customers. This enables vendors to reach customers efficiently. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide information to vendors using a generative AI model that takes customer data as input and outputs the optimal approach.

[0063] The analysis unit can determine the degree of match between a problem and a solution based on past data and vendor performance. For example, the analysis unit collects past customer feedback and sales data to evaluate the degree of match between the problem and the solution. The analysis unit can also identify the optimal vendor based on vendor success stories and customer satisfaction. Furthermore, the analysis unit can determine the degree of match between a problem and a solution based on the vendor's area of ​​expertise and evaluation. For example, the analysis unit uses past data to identify the vendor best suited to the customer's problem. This allows for the identification of a more appropriate vendor. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can identify the optimal vendor using a generative AI model that takes past data as input and outputs the degree of match between the problem and the solution.

[0064] The reception desk can estimate the customer's emotions and adjust the timing of task input based on the estimated emotions. For example, if the customer is feeling stressed, the reception desk can prompt them to input the task during a time when they can relax. The reception desk can also prompt the customer to input the task immediately if they are concentrating. Furthermore, if the customer is tired, the reception desk can prompt them to input the task after a break. For example, the reception desk can capture the customer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows the customer to input the task at the optimal time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can adjust the timing of task input using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0065] The reception desk can analyze a customer's past assignment submission history and select the optimal input method. For example, if a customer has preferred using text input in the past, the reception desk will prioritize suggesting text input. Similarly, if a customer has used voice input in the past, the reception desk can prioritize suggesting voice input. Furthermore, if a customer has previously submitted assignments during a specific time slot, the reception desk can prompt them to submit their assignments during that time slot. For example, the reception desk analyzes a customer's past submission history and selects the optimal input method, allowing the customer to submit their assignments in the most efficient way. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can select an input method using an AI model that takes the customer's past submission history data as input and outputs the optimal input method.

[0066] The reception desk can filter the input of tasks based on the customer's current projects and areas of interest. For example, the reception desk may prioritize displaying tasks related to projects the customer is currently working on. The reception desk can also filter and display relevant tasks based on the customer's areas of interest. Furthermore, the reception desk may prioritize displaying tasks related to areas the customer has shown interest in in the past. For example, the reception desk may prioritize displaying relevant tasks based on the progress of the customer's current projects. This allows the customer to prioritize inputting highly relevant tasks. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use an AI model that takes customer project data as input and outputs relevant tasks to perform filtering.

[0067] The reception desk can estimate the customer's emotions and determine the priority of tasks to be entered based on the estimated emotions. For example, if the customer is stressed, the reception desk may prioritize simple tasks. It may also prioritize important tasks if the customer is focused. Furthermore, if the customer is relaxed, it may prioritize long-term tasks. For example, the reception desk may capture the customer's facial expression with a camera and estimate their emotions using an emotion estimation algorithm. This allows the customer to enter tasks in the optimal order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may determine task priorities using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0068] The reception desk can prioritize inputting highly relevant tasks based on the customer's geographical location information when a task is entered. For example, if the customer is in a specific region, the reception desk will prioritize inputting tasks related to that region. Furthermore, if the customer is on the move, the reception desk can prioritize inputting tasks related to their destination. Additionally, if the customer is in a specific city, the reception desk can prioritize inputting tasks related to that city. For example, the reception desk prioritizes inputting relevant tasks based on the customer's geographical location information. This allows the customer to input the most relevant task. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input tasks using an AI model that takes the customer's geographical location data as input and outputs relevant tasks.

[0069] The reception desk can analyze a customer's social media activity when they submit a request and input relevant requests. For example, the reception desk can prioritize inputting requests that the customer has mentioned on social media. It can also prioritize inputting requests related to topics the customer follows on social media. Furthermore, it can prioritize inputting requests related to groups the customer participates in on social media. For example, the reception desk analyzes the customer's social media activity and inputs relevant requests. This allows the customer to prioritize inputting highly relevant requests. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input requests using an AI model that takes the customer's social media data as input and outputs relevant requests.

[0070] The analysis unit can estimate the customer's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the customer is relaxed, the analysis unit can provide detailed analysis results. If the customer is in a hurry, the analysis unit can also provide concise analysis results. Furthermore, if the customer is excited, the analysis unit can provide visually appealing analysis results. For example, the analysis unit can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the customer to receive the analysis results in the most optimal way. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the presentation of the analysis using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0071] The analysis unit can adjust the level of detail of the analysis based on the importance of the issues during the analysis. For example, the analysis unit will perform a detailed analysis for issues of high importance. It can also perform a simplified analysis for issues of low importance. Furthermore, it can perform an analysis with an appropriate level of detail for issues of moderate importance. For example, the analysis unit will evaluate the importance of the customer's issues and adjust the level of detail of the analysis accordingly. This allows the customer to receive analysis results with the optimal level of detail. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can adjust the level of detail of the analysis using a generative AI model that takes customer issue data as input and outputs the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of the problem during analysis. For example, the analysis unit can apply a technical analysis algorithm to technical problems. It can also apply a business analysis algorithm to business-related problems. Furthermore, it can apply a marketing analysis algorithm to marketing-related problems. For example, the analysis unit selects the optimal analysis algorithm based on the category of the problem. This allows the customer to receive the optimal analysis results. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without using generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes customer problem data as input and outputs the optimal analysis algorithm.

[0073] The analysis unit can estimate the customer's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the customer is in a hurry, the analysis unit can provide a short, concise analysis. If the customer is relaxed, the analysis unit can also provide a detailed analysis. Furthermore, if the customer is excited, the analysis unit can provide a visually engaging analysis. For example, the analysis unit can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the customer to receive an analysis result of the optimal length. 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 processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the length of the analysis using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0074] The analysis unit can determine the priority of analysis based on the submission date of the assignments during the analysis process. For example, the analysis unit may prioritize the analysis of recently submitted assignments. It can also prioritize the analysis of assignments with approaching deadlines. Furthermore, it can postpone the analysis of assignments whose submission deadlines have passed. For example, the analysis unit may determine the priority of analysis based on the submission date of the customer's assignments. This allows the customer to receive the analysis results in the optimal order. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit may determine the priority of analysis using a generative AI model that takes customer assignment data as input and outputs the priority of analysis.

[0075] The analysis unit can adjust the order of analysis based on the relevance of the issues during the analysis. For example, the analysis unit may prioritize analyzing issues related to the customer's current project. It may also prioritize analyzing issues related to the customer's areas of interest. Furthermore, the analysis unit may prioritize analyzing issues with high relevance based on the customer's past history. For example, the analysis unit may prioritize analyzing relevant issues based on the customer's current project data. This allows the customer to receive analysis results in the optimal order. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can adjust the order of analysis using a generative AI model that takes customer project data as input and outputs relevant issues.

[0076] The service provider can estimate the customer's emotions and adjust the way advice is presented based on those emotions. For example, if the customer is relaxed, the service provider can provide detailed advice. If the customer is in a hurry, the service provider can provide concise advice. Furthermore, if the customer is excited, the service provider can provide visually appealing advice. For example, the service provider can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the customer to receive advice in the most appropriate way. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can adjust the way advice is presented using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0077] The service provider can adjust the level of detail of advice based on the vendor's track record when providing advice. For example, the service provider may provide detailed advice to vendors with a proven track record. Conversely, it may provide concise advice to vendors with limited experience. Furthermore, it may provide advice of an appropriate level of detail to vendors with a moderate level of experience. For example, the service provider may adjust the level of detail of advice based on the vendor's success stories and customer satisfaction. This ensures that customers receive advice with the optimal level of detail. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can adjust the level of detail of advice using a generative AI model that takes vendor performance data as input and outputs the level of detail of the advice.

[0078] The service provider can provide optimal advice by referring to the customer's past feedback when providing advice. For example, the service provider may prioritize providing advice to customers who have previously given positive feedback on it. It can also avoid providing advice to customers who have previously given negative feedback on it. Furthermore, the service provider can analyze the customer's past feedback to provide optimal advice. For example, the service provider may select the optimal advice based on the customer's past feedback data. This ensures that the customer receives the most appropriate advice. Some or all of the above processes in the service provider may be performed using generative AI, or not. For example, the service provider may provide advice using a generative AI model that takes customer feedback data as input and outputs the optimal advice.

[0079] The service provider can estimate the customer's emotions and prioritize advice based on those emotions. For example, if the customer is stressed, the service provider may prioritize simple advice. It may also prioritize important advice if the customer is focused. Furthermore, if the customer is relaxed, it may prioritize long-term advice. For example, the service provider can capture the customer's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the customer to receive advice in the most optimal order. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can prioritize advice using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0080] The service provider can provide optimal advice based on the customer's geographical location information when providing advice. For example, if the customer is in a specific region, the service provider can provide advice relevant to that region. Furthermore, if the customer is on the move, the service provider can provide advice relevant to their destination. Additionally, if the customer is in a specific city, the service provider can provide advice relevant to that city. For example, the service provider provides optimal advice based on the customer's geographical location information. This allows the customer to receive the most appropriate advice. Some or all of the above processing in the service provider may be performed using generative AI, or without generative AI. For example, the service provider can provide advice using a generative AI model that takes the customer's geographical location data as input and outputs optimal advice.

[0081] The service provider can analyze a customer's social media activity and provide relevant advice when offering advice. For example, the service provider can provide advice related to topics the customer has mentioned on social media. It can also provide advice related to vendors the customer follows on social media. Furthermore, it can provide advice related to groups the customer participates in on social media. For example, the service provider analyzes a customer's social media activity and provides relevant advice, allowing the customer to receive optimal advice. Some or all of the above processing in the service provider may be performed using generative AI, or not. For example, the service provider can provide advice using a generative AI model that takes customer social media data as input and outputs relevant advice.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The reception desk can automatically suggest similar issues by referencing the customer's past problem-solving history when they input their issues. For example, if the current issue is similar to an issue the customer previously resolved, the reception desk will prompt them to refer to the past solution. The reception desk can also suggest solutions for new issues based on the customer's past successful solutions. Furthermore, the reception desk can suggest solutions to avoid solutions that the customer has failed at in the past. This allows the customer to efficiently solve issues by leveraging past experience. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can suggest issues using an AI model that takes the customer's past problem-solving history data as input and outputs similar issues.

[0084] The analysis unit can analyze customer challenges by referencing market trend data in real time and proposing the latest solutions. For example, the analysis unit can identify the optimal solution to a customer's challenge based on technologies and solutions currently attracting attention in the market. It can also analyze the activities of competitors and propose competitive solutions to customers. Furthermore, the analysis unit can refer to industry best practices to provide effective solutions to customers. This allows customers to select the optimal solution based on the latest information. Some or all of the above processes in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can perform analysis using a generative AI model that takes market trend data as input and outputs the latest solutions.

[0085] The service provider can adjust the format of advice given to customers according to their learning style. For example, if a customer is a visual learner, the service provider can provide advice using graphs and charts. If a customer is an auditory learner, the service provider can provide advice in the form of audio messages or podcasts. Furthermore, if a customer is an experiential learner, the service provider can provide advice in the form of interactive simulations or workshops. This allows customers to receive advice in a format that is best suited to their learning style. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can adjust the format of advice using an AI model that takes customer learning style data as input and outputs the optimal advice format.

[0086] The analysis unit can perform analyses based on the regulations and standards specific to the customer's industry when analyzing customer challenges. For example, the analysis unit can propose solutions compliant with medical regulations and standards to customers in the medical industry. It can also propose solutions compliant with financial regulations and standards to customers in the financial industry. Furthermore, it can propose solutions compliant with manufacturing regulations and standards to customers in the manufacturing industry. This allows customers to select solutions that conform to industry-specific regulations and standards. Some or all of the above processing in the analysis unit may be performed using generative AI, or not. For example, the analysis unit can perform analyses using a generative AI model that takes industry regulatory data as input and outputs regulatory-compliant solutions.

[0087] The service provider can adjust the content of advice given to customers according to their cultural background. For example, if a customer has a different cultural background, the service provider will provide advice that takes that culture into consideration. The service provider can also adjust the way advice is expressed according to the customer's language and communication style. Furthermore, the service provider can provide appropriate advice based on the customer's cultural values ​​and customs. This allows customers to receive advice in a way that is appropriate to their own cultural background. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can adjust the content of advice using an AI model that takes the customer's cultural background data as input and outputs optimal advice.

[0088] The reception desk can estimate the customer's emotions and suggest a method for inputting the task based on the estimated emotions. For example, if the customer is stressed, a simple multiple-choice input method can be suggested. If the customer is relaxed, a detailed text input method can be suggested. Furthermore, if the customer is focused, voice input can be suggested. This allows the customer to input the task in a way that best suits their emotional state. 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 processing in the reception desk may be performed using AI or not. For example, the reception desk can suggest a method for inputting the task using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0089] The analysis unit can estimate the customer's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the customer is stressed, the timing of providing the analysis results can be delayed. Conversely, if the customer is relaxed, the analysis results can be provided immediately. Furthermore, if the customer is focused, the analysis results can be provided in detail. This allows the customer to receive the analysis results at the optimal time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the timing of the analysis using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0090] The service provider can estimate the customer's emotions and adjust the tone of advice based on those emotions. For example, if the customer is stressed, advice can be given in a gentle tone. If the customer is relaxed, advice can be given in a friendly tone. Furthermore, if the customer is focused, advice can be given in a professional tone. This allows the customer to receive advice in a tone that is optimal for their emotional state. 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 processing described above in the service provider may be performed using AI or not. For example, the service provider can adjust the tone of advice using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0091] The analysis unit can estimate the customer's emotions and adjust the visual representation of the analysis based on the estimated emotions. For example, if the customer is relaxed, it can provide analysis results using detailed graphs and charts. If the customer is in a hurry, it can provide analysis results using concise icons and symbols. Furthermore, if the customer is excited, it can provide colorful and visually appealing analysis results. This allows the customer to receive analysis results in a visual representation that best suits their emotional state. 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 processing in the analysis unit may be performed using AI or not. For example, the analysis unit can adjust the visual representation of the analysis using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0092] The service provider can estimate the customer's emotions and prioritize advice based on those emotions. For example, if the customer is stressed, simple advice can be prioritized. If the customer is focused, important advice can be prioritized. Furthermore, if the customer is relaxed, long-term advice can be prioritized. This allows the customer to receive advice in the order that best suits their emotional state. 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 processing described above in the service provider may be performed using AI or not. For example, the service provider can determine the priority of advice using a generative AI model that takes customer facial expression data as input and outputs emotions.

[0093] The following briefly describes the processing flow for example form 2.

[0094] Step 1: The reception desk enters the customer's issue. This issue can include technical problems, business challenges, and more. The reception desk can receive the issue via online forms, phone calls, or in-person consultations. The reception desk can also estimate the customer's emotions and adjust the timing of issue submission based on those estimates. For example, if the customer is feeling stressed, they might be encouraged to submit the issue during a time when they can relax. Step 2: The analysis unit uses generative AI to analyze the issues entered by the reception unit and identify appropriate vendors. The analysis unit determines the degree of match between the issue and the solution based on past data and the vendor's track record. The generative AI analyzes the customer's issue using text generation AI (e.g., LLM) or multimodal generation AI. For example, it identifies the optimal vendor based on past success stories and customer feedback. Step 3: The service provider provides specific advice to the customer based on the vendor information identified by the analysis team. The service provider can provide advice through reports, proposals, or verbal explanations. The service provider can also estimate the customer's emotions and adjust the way the advice is presented based on those emotions. For example, if the customer is relaxed, they might provide more detailed advice.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can input the customer's issue using the reception device 38 of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the customer's issue using generated AI and identifies an appropriate vendor. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides specific advice to the customer based on the information of the identified vendor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0099] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can input the customer's issue using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the customer's issue using generated AI and identifies an appropriate vendor. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides specific advice to the customer based on the information of the identified vendor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can input the customer's issue using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the customer's issue using generated AI and identifies an appropriate vendor. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides specific advice to the customer based on the information of the identified vendor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can input the customer's issue using the microphone 238 of the robot 414. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the customer's issue using generated AI and identifies an appropriate vendor. The provision unit is implemented by the identification processing unit 290 of the data processing unit 12, which provides specific advice to the customer based on the information of the identified vendor. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] (Note 1) The reception area where customers enter their issues, An analysis unit analyzes the issues entered by the aforementioned reception unit and identifies an appropriate vendor, The system includes a provisioning unit that provides specific advice based on vendor information identified by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, To provide customers with specific advice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We provide vendors with information to help them streamline their customer reach. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We assess the degree of match between the challenges and the solutions based on past data and the vendor's track record. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates customer emotions and adjusts the timing of task input based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the customer's past issue submission history to select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When customers enter their tasks, filtering is performed based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Estimate customer emotions and prioritize tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering issues, the system prioritizes inputting issues that are highly relevant based on the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering a problem, analyze the customer's social media activity and enter the relevant problem. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate customer emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, adjust the level of detail based on the importance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates customer emotions and adjusts the length of the analysis based on the estimated customer emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on the submission deadline for the assignment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the issues. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We estimate the customer's emotions and adjust the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing advice, we adjust the level of detail based on the vendor's track record. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing advice, we refer to the customer's past feedback to provide the best possible advice. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, The system estimates customer emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, we provide the most suitable advice based on the customer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing advice, we analyze the client's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0167] 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. The reception area where customers enter their issues, An analysis unit analyzes the issues entered by the aforementioned reception unit and identifies an appropriate vendor, The system includes a provisioning unit that provides specific advice based on vendor information identified by the analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, To provide customers with specific advice. The system according to feature 1.

3. The aforementioned supply unit is, We provide vendors with information to help them streamline their customer reach. The system according to feature 1.

4. The aforementioned analysis unit, We assess the degree of match between the challenges and the solutions based on past data and the vendor's track record. The system according to feature 1.

5. The aforementioned reception unit is It estimates customer emotions and adjusts the timing of task input based on the estimated customer emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the customer's past issue submission history to select the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When customers enter their tasks, filtering is performed based on their current projects and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is Estimate customer emotions and prioritize tasks based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When entering issues, the system prioritizes inputting issues that are highly relevant based on the customer's geographical location. The system according to feature 1.

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