System

Abstract

An object of the system according to the embodiment is to appropriately grasp customer's problems and worries about AI utilization and propose a solution in which optimal AI products are combined.SOLUTION: A system according to an embodiment includes a proposal unit. The proposing unit solves customer's worries about problems and AI utilization using the interactive chat bot, and proposes a new solution by combining optimal products from among a plurality of AI products using the generated AI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to properly understand customers' issues and concerns regarding the use of AI and propose solutions that combined the most appropriate AI products.

[0005] The system of the embodiment aims to properly understand customers' challenges and concerns regarding the use of AI, and propose solutions that combine the most suitable AI products. [Means for solving the problem]

[0006] The system according to the embodiment includes a proposal unit that uses an interactive chatbot to understand customer issues and concerns about AI utilization, and then uses a generative AI to combine optimal products from multiple AI products to propose new solutions. [Effects of the Invention]

[0007] The system of the embodiment can properly understand customers' challenges and concerns regarding the use of AI, and propose solutions that combine the most suitable AI products. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The generative AI solution according to an embodiment of the present invention is a platform that connects customers who want to use AI to solve their problems with AI-based businesses. This platform uses a conversational chatbot to understand customers' issues and concerns about using AI, and proposes new solutions by combining the most suitable AI products from a wide range of options. This allows the generative AI solution to efficiently solve customers' issues and build sustainable business models.

[0029] A generative AI solution according to an embodiment includes a conversational chatbot, a generation AI, and a proposal unit. The conversational chatbot understands customer issues and concerns about AI utilization. For example, the conversational chatbot analyzes customer language using natural language processing technology to identify specific issues. The conversational chatbot can also receive input from customers and understand their issues and concerns. The generation AI proposes new solutions by combining optimal products from among many AI products. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an optimal solution for a customer's issues. The generation AI can also combine multiple AI products using a multimodal generation AI. The generation AI can also generate an optimal solution based on prompts containing the customer's issues and requests. The proposal unit proposes the solution generated by the generation AI to the customer. For example, the proposal unit proposes a new solution by combining AI products selected by the generation AI. The proposal unit can also propose an optimal solution for the customer's issues. The proposal unit can also make specific proposals to the customer based on the solutions generated by the generation AI. As a result, the generative AI solution according to the embodiment can provide an optimal solution for efficiently solving customer problems. For example, the generative AI solution can quickly solve customer problems and improve customer satisfaction. Furthermore, the generative AI solution can support the customer's business by providing an optimal solution according to the customer's needs. Furthermore, the generative AI solution can improve the efficiency of the customer's business by proposing an optimal AI product for solving the customer's problems.

[0030] A conversational chatbot can analyze a customer's past interaction history to identify their long-term challenges and trends. For example, a conversational chatbot can analyze a customer's past interaction history to extract frequently occurring keywords and phrases. For example, a conversational chatbot can identify a customer's long-term needs based on keywords such as "inventory management" and "efficiency" that are frequently mentioned in past interactions. A conversational chatbot can also identify a customer's long-term challenges and trends based on the customer's interaction history. A conversational chatbot can also analyze a customer's past interaction history to build a system for identifying a customer's long-term challenges and trends. This allows a customer's long-term challenges and trends to be identified and more effective solutions to be proposed. For example, a conversational chatbot can support the growth of a customer's business by identifying their long-term challenges. A conversational chatbot can also provide optimal solutions tailored to their needs by identifying their trends. A conversational chatbot can also improve the efficiency of a customer's business by identifying their long-term challenges and trends.

[0031] A conversational chatbot can learn terminology and expressions specific to a customer's industry, enabling more specialized conversations. For example, a conversational chatbot can learn terminology and expressions specific to a customer's industry and conduct specialized conversations. For example, a chatbot with knowledge of medical terminology and medical treatment processes can respond to customers in the medical industry. A conversational chatbot can also build a system that learns terminology and expressions specific to a customer's industry and conducts specialized conversations. A conversational chatbot can also develop algorithms that learn terminology and expressions specific to a customer's industry and enable more specialized conversations. This allows a conversational chatbot to learn terminology and expressions specific to a customer's industry and conduct more specialized conversations. For example, a conversational chatbot can learn terminology and expressions specific to a customer's industry to provide optimal solutions tailored to the customer's needs. A conversational chatbot can also improve the efficiency of a customer's business by learning terminology and expressions specific to a customer's industry. A conversational chatbot can also support the growth of a customer's business by learning terminology and expressions specific to a customer's industry.

[0032] A conversational chatbot can respond to voice input and understand customer issues using voice recognition technology. A conversational chatbot, for example, converts a customer's voice into text in real time. For example, if a customer says, "Please tell me how to improve the efficiency of inventory management," the content is converted into text and analyzed. A conversational chatbot can also respond to voice input, and a system can be built to convert a customer's voice into text. A conversational chatbot can also use voice recognition technology to understand customer issues. This allows it to respond to voice input and understand customer issues. For example, a conversational chatbot can improve customer convenience by responding to voice input. A conversational chatbot can also use voice recognition technology to quickly understand customer issues. A conversational chatbot can also respond to voice input and provide optimal solutions tailored to customer needs.

[0033] Conversational chatbots can support multiple languages ​​and accommodate international customers. Conversational chatbots, for example, can support multiple languages ​​and accommodate international customers. For example, chatbots that support major languages ​​such as English, French, and Chinese can be provided. Furthermore, a system can be built for the conversational chatbot to support multiple languages ​​and accommodate international customers. Furthermore, algorithms can be developed for the conversational chatbot to support multiple languages ​​and accommodate international customers. This allows for the conversational chatbot to support multiple languages ​​and accommodate international customers. For example, by supporting multiple languages, the conversational chatbot can provide optimal solutions that meet the needs of international customers. Furthermore, by supporting multiple languages, the conversational chatbot can improve the efficiency of international customers' businesses. Furthermore, by supporting multiple languages, the conversational chatbot can support the growth of international customers' businesses.

[0034] Generative AI can analyze data specific to a customer's industry and propose industry-specific solutions. For example, generative AI can analyze data specific to a customer's industry and propose industry-specific solutions. For example, for a customer in the manufacturing industry, generative AI can propose a solution specialized for optimizing the manufacturing process. Generative AI can also build a system for analyzing data specific to a customer's industry and proposing industry-specific solutions. Generative AI can also develop algorithms for analyzing data specific to a customer's industry and proposing industry-specific solutions. This allows generative AI to analyze data specific to a customer's industry and propose industry-specific solutions. For example, by analyzing data specific to a customer's industry, generative AI can provide optimal solutions tailored to the customer's needs. By analyzing data specific to a customer's industry, generative AI can improve the efficiency of the customer's business. By analyzing data specific to a customer's industry, generative AI can support the growth of the customer's business.

[0035] Generative AI can combine solutions from different industries to propose new business models. For example, generative AI can combine solutions from different industries to propose new business models. For example, applying medical industry technology to the manufacturing industry can propose a new business model. Generative AI can also build systems that combine solutions from different industries to propose new business models. Generative AI can also develop algorithms that combine solutions from different industries to propose new business models. This allows generative AI to combine solutions from different industries to propose new business models. For example, generative AI can support the growth of customers' businesses by combining solutions from different industries. Generative AI can also provide optimal business models that meet customer needs by combining solutions from different industries. Generative AI can also improve the efficiency of customers' businesses by combining solutions from different industries.

[0036] Generative AI can analyze market trends in real time and propose solutions based on the latest trends. For example, generative AI can analyze market trends in real time and propose solutions based on the latest trends. For example, it can propose the optimal solution based on the latest technological trends. Generative AI can also build a system for analyzing market trends in real time and proposing solutions based on the latest trends. Generative AI can also develop algorithms for analyzing market trends in real time and proposing solutions based on the latest trends. This allows it to analyze market trends in real time and propose solutions based on the latest trends. For example, generative AI can provide the optimal solution that meets customer needs based on the latest market trends. Generative AI can also improve the efficiency of customers' businesses based on the latest market trends. Generative AI can also support the growth of customers' businesses based on the latest market trends.

[0037] The platform can analyze a customer's usage history and reflect it in its next proposal. The platform, for example, analyzes a customer's usage history and reflects it in its next proposal. For example, the platform makes proposals tailored to the customer's preferences and needs based on past usage history. The platform can also build a system for analyzing a customer's usage history and reflecting it in its next proposal. The platform can also develop an algorithm for analyzing a customer's usage history and reflecting it in its next proposal. This allows the platform to analyze a customer's usage history and reflect it in its next proposal. For example, the platform can provide an optimal solution that meets the customer's needs based on the customer's usage history. The platform can also improve the efficiency of the customer's business based on the customer's usage history. The platform can also support the growth of the customer's business based on the customer's usage history.

[0038] The platform can quantitatively evaluate the effectiveness of AI products and prioritize the recommendation of the most effective products. For example, the platform can quantitatively evaluate the effectiveness of AI products and prioritize the recommendation of the most effective products. For example, the platform can quantify the effectiveness of each product and prioritize the recommendation of highly effective products. The platform can also build a system for quantitatively evaluating the effectiveness of AI products and prioritize the recommendation of the most effective products. The platform can also develop an algorithm for quantitatively evaluating the effectiveness of AI products and prioritize the recommendation of the most effective products. This allows the platform to quantitatively evaluate the effectiveness of AI products and prioritize the recommendation of the most effective products. For example, by quantifying the effectiveness of AI products, the platform can provide optimal solutions that meet customer needs. By quantifying the effectiveness of AI products, the platform can improve the efficiency of customers' businesses. By quantifying the effectiveness of AI products, the platform can support the growth of customers' businesses.

[0039] The platform can automatically generate marketing campaigns targeted at customers in different industries. For example, the platform automatically generates marketing campaigns targeted at customers in different industries. For example, the platform automatically generates campaigns specialized for each industry, such as manufacturing, healthcare, and retail. The platform can also build a system for automatically generating marketing campaigns targeted at customers in different industries. The platform can also develop an algorithm for automatically generating marketing campaigns targeted at customers in different industries. This automatically generates marketing campaigns targeted at customers in different industries. For example, by automatically generating campaigns targeted at customers in different industries, the platform provides optimal solutions tailored to customer needs. By automatically generating campaigns targeted at customers in different industries, the platform can improve the efficiency of customers' businesses. By automatically generating campaigns targeted at customers in different industries, the platform can support the growth of customers' businesses.

[0040] The platform can propose improvements to AI products based on customer feedback. For example, the platform proposes improvements to AI products based on customer feedback. For example, the platform analyzes customer feedback and identifies areas for improvement in the product. The platform can also build a system for proposing improvements to AI products based on customer feedback. The platform can also develop an algorithm for proposing improvements to AI products based on customer feedback. This allows the platform to propose improvements to AI products based on customer feedback. For example, the platform can provide optimal solutions that meet customer needs based on customer feedback. The platform can also improve the efficiency of the customer's business based on customer feedback. The platform can also support the growth of the customer's business based on customer feedback.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The generative AI solution further includes a prediction unit. The prediction unit can analyze the customer's business data and predict future issues and trends. For example, the prediction unit can predict sales for the next quarter based on past sales data. The prediction unit can also analyze the customer's market trends and predict future demand. The prediction unit can also analyze the customer's business processes and predict areas for improvement to improve efficiency. This allows the generative AI solution to identify the customer's future issues in advance and provide appropriate solutions. For example, the prediction unit can predict a future decline in sales and propose countermeasures in advance. The prediction unit can also predict a future increase in demand and propose optimization of inventory management. The prediction unit can also predict areas for improvement in business processes and make specific proposals for improving efficiency.

[0043] The generative AI solution further includes a learning unit. The learning unit can improve the accuracy of the generative AI's proposals based on customer feedback. For example, the learning unit analyzes feedback provided by customers and improves the generative AI's algorithm. The learning unit can also learn how to propose new solutions based on customer feedback. The learning unit can also customize the content of the generative AI's proposals based on customer feedback. This allows the generative AI solution to provide optimal solutions that reflect customer feedback. For example, the learning unit improves the content of the proposals based on customer feedback, thereby improving customer satisfaction. The learning unit can also learn how to propose new solutions based on customer feedback and improve the accuracy of the proposals. The learning unit can also customize the content of the proposals based on customer feedback, thereby providing optimal solutions that meet customer needs.

[0044] The generative AI solution further includes an integration unit. The integration unit can integrate multiple AI products to provide a single comprehensive solution. For example, the integration unit combines natural language processing AI and image recognition AI to solve customer problems. The integration unit can also integrate different AI products to provide more advanced solutions. The integration unit can also select and integrate the most appropriate AI products according to customer needs. This allows the generative AI solution to provide optimal solutions that integrate multiple AI products. For example, the integration unit integrates natural language processing AI and image recognition AI to solve customer problems comprehensively. The integration unit can also integrate different AI products to provide more advanced solutions. The integration unit can also select and integrate the most appropriate AI products according to customer needs.

[0045] The generative AI solution further includes a customization unit. The customization unit can customize the solution according to the specific requirements of the customer. For example, the customization unit can adjust the solution by taking into account requirements specific to the customer's industry. The customization unit can also optimize the solution to suit the customer's business processes. The customization unit can also fine-tune the solution based on customer feedback. This allows the generative AI solution to provide an optimal solution according to the specific requirements of the customer. For example, the customization unit can adjust the solution by taking into account requirements specific to the customer's industry. The customization unit can also optimize the solution to suit the customer's business processes. The customization unit can also fine-tune the solution based on customer feedback.

[0046] The generative AI solution further includes an evaluation unit. The evaluation unit can quantitatively evaluate the effectiveness of the proposed solution and reflect it in the next proposal. For example, the evaluation unit can quantify the results after the proposed solution is implemented and evaluate its effectiveness. The evaluation unit can also improve the proposal based on customer feedback. The evaluation unit can also analyze the effects of the proposed solution and reflect it in the next proposal. This allows the generative AI solution to improve the accuracy of the proposal and increase customer satisfaction. For example, the evaluation unit can quantify the results after the proposed solution is implemented and evaluate its effectiveness. The evaluation unit can also improve the proposal based on customer feedback. The evaluation unit can also analyze the effects of the proposed solution and reflect it in the next proposal.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The conversational chatbot understands the customer's issues and concerns regarding the use of AI. For example, the conversational chatbot uses natural language processing technology to analyze the customer's words and identify specific issues. The conversational chatbot can also receive input from the customer to understand their issues and concerns. Step 2: The generative AI combines the best products from among many AI products to propose new solutions. For example, the generative AI uses text generation AI (e.g., LLM) to generate the best solution for the customer's problem. The generative AI can also use multimodal generative AI to combine multiple AI products. The generative AI can also generate the best solution based on prompts that include the customer's problem and requests. Step 3: The proposal department proposes the solution generated by the generative AI to the customer. For example, the proposal department proposes a new solution by combining AI products selected by the generative AI. The proposal department can also propose the optimal solution to the customer's problem. The proposal department can also make specific proposals to the customer based on the solution generated by the generative AI.

[0049] (Example 2) The generative AI solution according to an embodiment of the present invention is a platform that connects customers who want to use AI to solve their problems with AI-based businesses. This platform uses a conversational chatbot to understand customers' issues and concerns about using AI, and proposes new solutions by combining the most suitable AI products from a wide range of options. This allows the generative AI solution to efficiently solve customers' issues and build sustainable business models.

[0050] A generative AI solution according to an embodiment includes a conversational chatbot, a generation AI, and a proposal unit. The conversational chatbot understands customer issues and concerns about AI utilization. For example, the conversational chatbot analyzes customer language using natural language processing technology to identify specific issues. The conversational chatbot can also receive input from customers and understand their issues and concerns. The generation AI proposes new solutions by combining optimal products from among many AI products. For example, the generation AI uses a text generation AI (e.g., LLM) to generate an optimal solution for a customer's issues. The generation AI can also combine multiple AI products using a multimodal generation AI. The generation AI can also generate an optimal solution based on prompts containing the customer's issues and requests. The proposal unit proposes the solution generated by the generation AI to the customer. For example, the proposal unit proposes a new solution by combining AI products selected by the generation AI. The proposal unit can also propose an optimal solution for the customer's issues. The proposal unit can also make specific proposals to the customer based on the solutions generated by the generation AI. As a result, the generative AI solution according to the embodiment can provide an optimal solution for efficiently solving customer problems. For example, the generative AI solution can quickly solve customer problems and improve customer satisfaction. Furthermore, the generative AI solution can support the customer's business by providing an optimal solution according to the customer's needs. Furthermore, the generative AI solution can improve the efficiency of the customer's business by proposing an optimal AI product for solving the customer's problems.

[0051] A conversational chatbot can analyze a customer's past interaction history to identify their long-term challenges and trends. For example, a conversational chatbot can analyze a customer's past interaction history to extract frequently occurring keywords and phrases. For example, a conversational chatbot can identify a customer's long-term needs based on keywords such as "inventory management" and "efficiency" that are frequently mentioned in past interactions. A conversational chatbot can also identify a customer's long-term challenges and trends based on the customer's interaction history. A conversational chatbot can also analyze a customer's past interaction history to build a system for identifying a customer's long-term challenges and trends. This allows a customer's long-term challenges and trends to be identified and more effective solutions to be proposed. For example, a conversational chatbot can support the growth of a customer's business by identifying their long-term challenges. A conversational chatbot can also provide optimal solutions tailored to their needs by identifying their trends. A conversational chatbot can also improve the efficiency of a customer's business by identifying their long-term challenges and trends.

[0052] A conversational chatbot can learn terminology and expressions specific to a customer's industry, enabling more specialized conversations. For example, a conversational chatbot can learn terminology and expressions specific to a customer's industry and conduct specialized conversations. For example, a chatbot with knowledge of medical terminology and medical treatment processes can respond to customers in the medical industry. A conversational chatbot can also build a system that learns terminology and expressions specific to a customer's industry and conducts specialized conversations. A conversational chatbot can also develop algorithms that learn terminology and expressions specific to a customer's industry and enable more specialized conversations. This allows a conversational chatbot to learn terminology and expressions specific to a customer's industry and conduct more specialized conversations. For example, a conversational chatbot can learn terminology and expressions specific to a customer's industry to provide optimal solutions tailored to the customer's needs. A conversational chatbot can also improve the efficiency of a customer's business by learning terminology and expressions specific to a customer's industry. A conversational chatbot can also support the growth of a customer's business by learning terminology and expressions specific to a customer's industry.

[0053] A conversational chatbot can use an emotion estimation function to grasp a customer's emotional state in real time and provide an appropriate response. A conversational chatbot, for example, analyzes a customer's input content and voice tone to estimate the customer's emotional state in real time. For example, if a customer is feeling stressed, the chatbot can respond in a way that helps them relax. A conversational chatbot can also analyze a customer's facial expressions to grasp the customer's emotional state in real time. A conversational chatbot can also use the emotion estimation function to build a system for grasping a customer's emotional state in real time and providing an appropriate response. This allows a conversational chatbot to grasp a customer's emotional state in real time and provide an appropriate response. For example, a conversational chatbot can improve customer satisfaction by grasping a customer's emotional state. A conversational chatbot can also provide an optimal solution that meets the customer's needs by grasping a customer's emotional state. A conversational chatbot can also improve the efficiency of a customer's business by grasping a customer's emotional state.

[0054] A conversational chatbot can respond to voice input and understand customer issues using voice recognition technology. A conversational chatbot, for example, converts a customer's voice into text in real time. For example, if a customer says, "Please tell me how to improve the efficiency of inventory management," the content is converted into text and analyzed. A conversational chatbot can also respond to voice input, and a system can be built to convert a customer's voice into text. A conversational chatbot can also use voice recognition technology to understand customer issues. This allows it to respond to voice input and understand customer issues. For example, a conversational chatbot can improve customer convenience by responding to voice input. A conversational chatbot can also use voice recognition technology to quickly understand customer issues. A conversational chatbot can also respond to voice input and provide optimal solutions tailored to customer needs.

[0055] Conversational chatbots can support multiple languages ​​and accommodate international customers. Conversational chatbots, for example, can support multiple languages ​​and accommodate international customers. For example, chatbots that support major languages ​​such as English, French, and Chinese can be provided. Furthermore, a system can be built for the conversational chatbot to support multiple languages ​​and accommodate international customers. Furthermore, algorithms can be developed for the conversational chatbot to support multiple languages ​​and accommodate international customers. This allows for the conversational chatbot to support multiple languages ​​and accommodate international customers. For example, by supporting multiple languages, the conversational chatbot can provide optimal solutions that meet the needs of international customers. Furthermore, by supporting multiple languages, the conversational chatbot can improve the efficiency of international customers' businesses. Furthermore, by supporting multiple languages, the conversational chatbot can support the growth of international customers' businesses.

[0056] A conversational chatbot can automatically adjust a conversation style according to a customer's emotions using an emotion estimation function. For example, a conversational chatbot automatically adjusts a conversation style according to a customer's emotions using the emotion estimation function. For example, if a customer is feeling stressed, the conversational chatbot adopts a conversation style that relaxes the customer. The conversational chatbot can also build a system for automatically adjusting a conversation style according to a customer's emotions. The conversational chatbot can also use the emotion estimation function to develop an algorithm for automatically adjusting a conversation style according to a customer's emotions. This automatically adjusts a conversation style according to a customer's emotions. For example, the conversational chatbot improves customer satisfaction by adopting a conversation style according to a customer's emotions. The conversational chatbot can provide an optimal solution according to the customer's needs by adopting a conversation style according to a customer's emotions. The conversational chatbot can improve the efficiency of a customer's business by adopting a conversation style according to a customer's emotions.

[0057] Generative AI can analyze data specific to a customer's industry and propose industry-specific solutions. For example, generative AI can analyze data specific to a customer's industry and propose industry-specific solutions. For example, for a customer in the manufacturing industry, generative AI can propose a solution specialized for optimizing the manufacturing process. Generative AI can also build a system for analyzing data specific to a customer's industry and proposing industry-specific solutions. Generative AI can also develop algorithms for analyzing data specific to a customer's industry and proposing industry-specific solutions. This allows generative AI to analyze data specific to a customer's industry and propose industry-specific solutions. For example, by analyzing data specific to a customer's industry, generative AI can provide optimal solutions tailored to the customer's needs. By analyzing data specific to a customer's industry, generative AI can improve the efficiency of the customer's business. By analyzing data specific to a customer's industry, generative AI can support the growth of the customer's business.

[0058] The generative AI can use the emotion estimation function to prioritize solutions to which customers have the most positive reactions. The generative AI can, for example, use the emotion estimation function to prioritize solutions to which customers have the most positive reactions. For example, the generative AI can analyze customers' emotional reactions to past proposals and prioritize solutions that have received the most positive reactions. The generative AI can also use the emotion estimation function to build a system for prioritized proposals to which customers have the most positive reactions. The generative AI can also use the emotion estimation function to develop an algorithm for prioritized proposals to which customers have the most positive reactions. This prioritizes proposals to which customers have the most positive reactions. For example, the generative AI can improve customer satisfaction based on customers' emotional reactions. The generative AI can also provide optimal solutions that meet customer needs based on customers' emotional reactions. The generative AI can also improve the efficiency of customers' businesses based on customers' emotional reactions.

[0059] Generative AI can combine solutions from different industries to propose new business models. For example, generative AI can combine solutions from different industries to propose new business models. For example, applying medical industry technology to the manufacturing industry can propose a new business model. Generative AI can also build systems that combine solutions from different industries to propose new business models. Generative AI can also develop algorithms that combine solutions from different industries to propose new business models. This allows generative AI to combine solutions from different industries to propose new business models. For example, generative AI can support the growth of customers' businesses by combining solutions from different industries. Generative AI can also provide optimal business models that meet customer needs by combining solutions from different industries. Generative AI can also improve the efficiency of customers' businesses by combining solutions from different industries.

[0060] Generative AI can analyze market trends in real time and propose solutions based on the latest trends. For example, generative AI can analyze market trends in real time and propose solutions based on the latest trends. For example, it can propose the optimal solution based on the latest technological trends. Generative AI can also build a system for analyzing market trends in real time and proposing solutions based on the latest trends. Generative AI can also develop algorithms for analyzing market trends in real time and proposing solutions based on the latest trends. This allows it to analyze market trends in real time and propose solutions based on the latest trends. For example, generative AI can provide the optimal solution that meets customer needs based on the latest market trends. Generative AI can also improve the efficiency of customers' businesses based on the latest market trends. Generative AI can also support the growth of customers' businesses based on the latest market trends.

[0061] The generative AI can use the emotion estimation function to customize solutions based on customer emotions. For example, the generative AI uses the emotion estimation function to customize solutions based on customer emotions. For example, if a customer expresses positive emotions, the generative AI can propose a solution that matches those emotions. The generative AI can also use the emotion estimation function to build a system for customizing solutions based on customer emotions. The generative AI can also use the emotion estimation function to develop an algorithm for customizing solutions based on customer emotions. This customizes solutions based on customer emotions. For example, by providing solutions based on customer emotions, the generative AI can improve customer satisfaction. By providing solutions based on customer emotions, the generative AI can provide optimal solutions that meet customer needs. By providing solutions based on customer emotions, the generative AI can improve the efficiency of customers' businesses.

[0062] The platform can analyze a customer's usage history and reflect it in its next proposal. The platform, for example, analyzes a customer's usage history and reflects it in its next proposal. For example, the platform makes proposals tailored to the customer's preferences and needs based on past usage history. The platform can also build a system for analyzing a customer's usage history and reflecting it in its next proposal. The platform can also develop an algorithm for analyzing a customer's usage history and reflecting it in its next proposal. This allows the platform to analyze a customer's usage history and reflect it in its next proposal. For example, the platform can provide an optimal solution that meets the customer's needs based on the customer's usage history. The platform can also improve the efficiency of the customer's business based on the customer's usage history. The platform can also support the growth of the customer's business based on the customer's usage history.

[0063] The platform can quantitatively evaluate the effectiveness of AI products and prioritize the recommendation of the most effective products. For example, the platform can quantitatively evaluate the effectiveness of AI products and prioritize the recommendation of the most effective products. For example, the platform can quantify the effectiveness of each product and prioritize the recommendation of highly effective products. The platform can also build a system for quantitatively evaluating the effectiveness of AI products and prioritize the recommendation of the most effective products. The platform can also develop an algorithm for quantitatively evaluating the effectiveness of AI products and prioritize the recommendation of the most effective products. This allows the platform to quantitatively evaluate the effectiveness of AI products and prioritize the recommendation of the most effective products. For example, by quantifying the effectiveness of AI products, the platform can provide optimal solutions that meet customer needs. By quantifying the effectiveness of AI products, the platform can improve the efficiency of customers' businesses. By quantifying the effectiveness of AI products, the platform can support the growth of customers' businesses.

[0064] The platform can automatically generate marketing campaigns targeted at customers in different industries. For example, the platform automatically generates marketing campaigns targeted at customers in different industries. For example, the platform automatically generates campaigns specialized for each industry, such as manufacturing, healthcare, and retail. The platform can also build a system for automatically generating marketing campaigns targeted at customers in different industries. The platform can also develop an algorithm for automatically generating marketing campaigns targeted at customers in different industries. This automatically generates marketing campaigns targeted at customers in different industries. For example, by automatically generating campaigns targeted at customers in different industries, the platform provides optimal solutions tailored to customer needs. By automatically generating campaigns targeted at customers in different industries, the platform can improve the efficiency of customers' businesses. By automatically generating campaigns targeted at customers in different industries, the platform can support the growth of customers' businesses.

[0065] The platform can propose improvements to AI products based on customer feedback. For example, the platform proposes improvements to AI products based on customer feedback. For example, the platform analyzes customer feedback and identifies areas for improvement in the product. The platform can also build a system for proposing improvements to AI products based on customer feedback. The platform can also develop an algorithm for proposing improvements to AI products based on customer feedback. This allows the platform to propose improvements to AI products based on customer feedback. For example, the platform can provide optimal solutions that meet customer needs based on customer feedback. The platform can also improve the efficiency of the customer's business based on customer feedback. The platform can also support the growth of the customer's business based on customer feedback.

[0066] The platform can use the emotion estimation function to provide customer support based on the emotions of a customer. For example, the platform uses the emotion estimation function to provide customer support based on the emotions of a customer. For example, if a customer is feeling stressed, the platform provides support that helps the customer relax. The platform can also use the emotion estimation function to build a system for providing customer support based on the emotions of a customer. The platform can also use the emotion estimation function to develop an algorithm for providing customer support based on the emotions of a customer. This provides customer support based on the emotions of a customer. For example, by providing support based on the emotions of a customer, the platform improves customer satisfaction. By providing support based on the emotions of a customer, the platform can provide optimal solutions that meet the needs of the customer. By providing support based on the emotions of a customer, the platform can improve the efficiency of the customer's business.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The generative AI solution further includes a prediction unit. The prediction unit can analyze the customer's business data and predict future issues and trends. For example, the prediction unit can predict sales for the next quarter based on past sales data. The prediction unit can also analyze the customer's market trends and predict future demand. The prediction unit can also analyze the customer's business processes and predict areas for improvement to improve efficiency. This allows the generative AI solution to identify the customer's future issues in advance and provide appropriate solutions. For example, the prediction unit can predict a future decline in sales and propose countermeasures in advance. The prediction unit can also predict a future increase in demand and propose optimization of inventory management. The prediction unit can also predict areas for improvement in business processes and make specific proposals for improving efficiency.

[0069] The generative AI solution further includes a learning unit. The learning unit can improve the accuracy of the generative AI's proposals based on customer feedback. For example, the learning unit analyzes feedback provided by customers and improves the generative AI's algorithm. The learning unit can also learn how to propose new solutions based on customer feedback. The learning unit can also customize the content of the generative AI's proposals based on customer feedback. This allows the generative AI solution to provide optimal solutions that reflect customer feedback. For example, the learning unit improves the content of the proposals based on customer feedback, thereby improving customer satisfaction. The learning unit can also learn how to propose new solutions based on customer feedback and improve the accuracy of the proposals. The learning unit can also customize the content of the proposals based on customer feedback, thereby providing optimal solutions that meet customer needs.

[0070] The generative AI solution further includes an integration unit. The integration unit can integrate multiple AI products to provide a single comprehensive solution. For example, the integration unit combines natural language processing AI and image recognition AI to solve customer problems. The integration unit can also integrate different AI products to provide more advanced solutions. The integration unit can also select and integrate the most appropriate AI products according to customer needs. This allows the generative AI solution to provide optimal solutions that integrate multiple AI products. For example, the integration unit integrates natural language processing AI and image recognition AI to solve customer problems comprehensively. The integration unit can also integrate different AI products to provide more advanced solutions. The integration unit can also select and integrate the most appropriate AI products according to customer needs.

[0071] The generative AI solution further includes a customization unit. The customization unit can customize the solution according to the specific requirements of the customer. For example, the customization unit can adjust the solution by taking into account requirements specific to the customer's industry. The customization unit can also optimize the solution to suit the customer's business processes. The customization unit can also fine-tune the solution based on customer feedback. This allows the generative AI solution to provide an optimal solution according to the specific requirements of the customer. For example, the customization unit can adjust the solution by taking into account requirements specific to the customer's industry. The customization unit can also optimize the solution to suit the customer's business processes. The customization unit can also fine-tune the solution based on customer feedback.

[0072] The generative AI solution further includes an evaluation unit. The evaluation unit can quantitatively evaluate the effectiveness of the proposed solution and reflect it in the next proposal. For example, the evaluation unit can quantify the results after the proposed solution is implemented and evaluate its effectiveness. The evaluation unit can also improve the proposal based on customer feedback. The evaluation unit can also analyze the effects of the proposed solution and reflect it in the next proposal. This allows the generative AI solution to improve the accuracy of the proposal and increase customer satisfaction. For example, the evaluation unit can quantify the results after the proposed solution is implemented and evaluate its effectiveness. The evaluation unit can also improve the proposal based on customer feedback. The evaluation unit can also analyze the effects of the proposed solution and reflect it in the next proposal.

[0073] The generative AI solution can further use an emotion estimation function to propose a marketing strategy based on the customer's emotions. For example, if a customer expresses positive emotions, the emotion estimation function can be used to propose a marketing strategy that matches those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to propose a marketing strategy that alleviates those emotions. Furthermore, the emotion estimation function can be used to build a system for proposing marketing strategies based on the customer's emotions. In this way, the generative AI solution can provide an optimal marketing strategy based on the customer's emotions. For example, if a customer expresses positive emotions, the emotion estimation function can be used to propose a marketing strategy that matches those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to propose a marketing strategy that alleviates those emotions. Furthermore, the emotion estimation function can be used to build a system for proposing marketing strategies based on the customer's emotions.

[0074] The generative AI solution can further use an emotion estimation function to provide customer support based on the customer's emotions. For example, if a customer is feeling stressed, the emotion estimation function can be used to provide support that helps the customer relax. Also, if a customer is satisfied, the emotion estimation function can be used to provide support that helps the customer maintain that emotion. Furthermore, the emotion estimation function can be used to build a system for providing customer support based on the customer's emotions. This allows the generative AI solution to provide optimal customer support based on the customer's emotions. For example, if a customer is feeling stressed, the emotion estimation function can be used to provide support that helps the customer relax. Also, if a customer is satisfied, the emotion estimation function can be used to provide support that helps the customer maintain that emotion. Furthermore, the emotion estimation function can be used to build a system for providing customer support based on the customer's emotions.

[0075] The generative AI solution can further use an emotion estimation function to customize products based on customer emotions. For example, if a customer expresses positive emotions, the emotion estimation function can be used to suggest products that match those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to suggest products that will alleviate those emotions. Furthermore, the emotion estimation function can be used to build a system for customizing products based on customer emotions. This allows the generative AI solution to provide optimal products based on customer emotions. For example, if a customer expresses positive emotions, the emotion estimation function can be used to suggest products that match those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to suggest products that will alleviate those emotions. Furthermore, the emotion estimation function can be used to build a system for customizing products based on customer emotions.

[0076] The generative AI solution can further use an emotion estimation function to collect feedback based on customer emotions and reflect it in product improvements. For example, if a customer expresses positive emotions, the emotion estimation function can be used to collect feedback based on those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to collect feedback based on those emotions. Furthermore, a system can be built using the emotion estimation function to collect feedback based on customer emotions and reflect it in product improvements. In this way, the generative AI solution can collect optimal feedback based on customer emotions and reflect it in product improvements. For example, if a customer expresses positive emotions, the emotion estimation function can be used to collect feedback based on those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to collect feedback based on those emotions. Furthermore, a system can be built using the emotion estimation function to collect feedback based on customer emotions and reflect it in product improvements.

[0077] The generative AI solution can further use an emotion estimation function to automatically generate advertisements based on customer emotions. For example, if a customer expresses positive emotions, the emotion estimation function can be used to automatically generate an advertisement that matches those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to automatically generate an advertisement that alleviates those emotions. Furthermore, the emotion estimation function can be used to build a system for automatically generating advertisements based on customer emotions. This allows the generative AI solution to provide optimal advertisements based on customer emotions. For example, if a customer expresses positive emotions, the emotion estimation function can be used to automatically generate an advertisement that matches those emotions. Also, if a customer expresses negative emotions, the emotion estimation function can be used to automatically generate an advertisement that alleviates those emotions. Furthermore, the emotion estimation function can be used to build a system for automatically generating advertisements based on customer emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The conversational chatbot understands the customer's issues and concerns regarding the use of AI. For example, the conversational chatbot uses natural language processing technology to analyze the customer's words and identify specific issues. The conversational chatbot can also receive input from the customer to understand their issues and concerns. Step 2: The generative AI combines the best products from among many AI products to propose new solutions. For example, the generative AI uses text generation AI (e.g., LLM) to generate the best solution for the customer's problem. The generative AI can also use multimodal generative AI to combine multiple AI products. The generative AI can also generate the best solution based on prompts that include the customer's problem and requests. Step 3: The proposal department proposes the solution generated by the generative AI to the customer. For example, the proposal department proposes a new solution by combining AI products selected by the generative AI. The proposal department can also propose the optimal solution to the customer's problem. The proposal department can also make specific proposals to the customer based on the solution generated by the generative AI.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] 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.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Using a conversational chatbot, we understand customer issues and concerns regarding the use of AI, The proposal department uses generative AI to combine the best products from multiple AI products and propose new solutions. A system characterized by:

2. The conversational chatbot comprises: Analyze the customer's past interaction history to understand the customer's long-term challenges and trends 2. The system of claim 1.

3. The conversational chatbot comprises: Supports voice input and uses voice recognition technology to understand the customer's issues.

2. The system of claim 1.

4. The generated AI is Learning from past success stories and proposing optimal solutions to similar problems 2. The system of claim 1.

5. The platform is Analyzing the customer's usage history and reflecting it in the next proposal 2. The system of claim 1.

6. The conversational chatbot comprises: Using emotion estimation function, grasp the emotional state of the customer in real time and respond appropriately.

2. The system of claim 1.

7. The generated AI is Using a sentiment estimation function, the solution to which the customer has the most positive response is preferentially proposed.

2. The system of claim 1.

8. The platform is Using emotion estimation function, monitor customer satisfaction in real time and reflect it in fee setting.

2. The system of claim 1.

Images