System

Abstract

An object of the system according to the embodiment is to generate and propose a new solution by utilizing multilateral viewpoints obtained from different cultures and occupations.SOLUTION: A system according to an embodiment includes a problem input unit, a viewpoint collection unit, a solution generation unit, and a proposal unit. The task input unit inputs a task or a problem presented by a user. The viewpoint collection unit collects multilateral viewpoints from cultures, environments, industries, and specialized fields around the world on the basis of the issues and problems input by the issue input unit. The solution generation unit generates a new solution or idea based on the multilateral viewpoints collected by the viewpoint collection unit. The proposal unit presents the solution or idea generated by the solution generation unit to the user.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] Conventional technology lacks a means to solve problems by centrally utilizing the multifaceted perspectives gained from different cultures and occupations.

[0005] The system according to the embodiment aims to generate and propose new solutions by utilizing the multifaceted perspectives gained from different cultures and occupations. [Means for solving the problem]

[0006] The system according to the embodiment includes a problem input unit, a perspective collection unit, a solution generation unit, and a proposal unit. The problem input unit inputs problems or issues presented by users. The perspective collection unit collects multiple perspectives from cultures, environments, industries, and specialized fields around the world based on the problems or issues input by the problem input unit. The solution generation unit generates new solutions and ideas based on the multiple perspectives collected by the perspective collection unit. The proposal unit presents the solutions and ideas generated by the solution generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate and propose new solutions by utilizing the multifaceted perspectives gained from different cultures and occupations. [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 multifaceted problem-solving AI platform according to an embodiment of the present invention is a system in which AI analyzes and aggregates multifaceted perspectives obtained from cultures, environments, industries, and specialized fields around the world for issues and problems presented by users, and generates and proposes new solutions and ideas. This enables the multifaceted problem-solving AI platform to efficiently and effectively provide solutions to any issues and problems presented by users.

[0029] A multifaceted problem-solving AI platform according to an embodiment includes a problem input unit, a perspective collection unit, a solution generation unit, and a proposal unit. The problem input unit inputs a problem or issue presented by a user. For example, a user may input a problem such as "I want to think of a new marketing strategy." The problem input unit also analyzes the problem input by the user and understands its content. For example, a generation AI analyzes the user's input content and collects related information. The perspective collection unit collects multifaceted perspectives from cultures, environments, industries, and fields of expertise around the world based on the problem or issue input by the problem input unit. For example, it collects marketing strategies from different countries and regions, success stories from different industries, knowledge from different fields of expertise, etc. The solution generation unit generates new solutions and ideas based on the multifaceted perspectives collected by the perspective collection unit. For example, it combines information obtained from different cultures and environments to propose a new marketing strategy. The proposal unit presents the solutions and ideas generated by the solution generation unit to the user. For example, the generation AI makes a proposal in the form of, "As a new marketing strategy, I propose that you implement a campaign utilizing social media." As a result, the multifaceted problem-solving AI platform according to the embodiment can provide solutions from multiple perspectives to problems with diverse backgrounds.

[0030] The problem input unit can refer to the user's past problem input history and prioritize analysis of solutions to similar problems. For example, the generation AI stores the user's past problem input history in a database, and the problem input unit references that history when a similar problem is input. For example, if a user who previously input a "new marketing strategy" inputs a similar problem again, the unit will prioritize analysis of past solutions. This makes it possible to efficiently provide solutions by utilizing past history.

[0031] The problem input unit can automatically generate related questions based on the user's input and delve deeper into the details of the problem. For example, the problem input unit uses a generation AI to analyze the user's input and automatically generate related questions. For example, in response to the input "I want to think of a new marketing strategy," it generates a question such as "What is the target market?" This allows for deeper digging into the details of the problem, making it possible to provide more accurate solutions.

[0032] The task input unit can also accept tasks as voice or image data, which the generation AI can analyze and convert into text. For example, the task input unit accepts task input as voice data, and the generation AI converts it into text using voice recognition technology. For example, if a user inputs by voice, "I want to think of a new marketing strategy," the content is converted into text. The task input unit can also accept tasks as image data, and the generation AI converts it into text using image analysis technology. For example, if a user inputs handwritten notes as an image, the content is converted into text. This improves user convenience by converting voice and image data into text.

[0033] The problem input unit can automatically translate problem inputs in different languages ​​and provide solutions from a global perspective. For example, the problem input unit automatically translates problem inputs in different languages, and the generation AI analyzes the content. For example, if a user inputs "I would like to come up with a new marketing strategy" in French, the content is automatically translated and analyzed. In this way, by automatically translating problem inputs in different languages, solutions from a global perspective can be provided.

[0034] The perspective collection unit can collect data from different cultures and environments in real time and perform analysis based on the latest information. For example, the generation AI can collect data from different cultures and environments in real time and perform analysis based on the latest information. For example, it can collect and analyze marketing strategies from different countries. This allows analysis to be based on the latest information, making it possible to provide more appropriate solutions.

[0035] The perspective gathering unit automatically collects expert opinions and papers, making it possible to provide in-depth insight into issues. For example, generative AI automatically collects expert opinions and papers to provide in-depth insight into issues. For example, it collects and analyzes the opinions of marketing experts and the latest research papers. This allows for the collection of expert opinions and papers to provide even deeper insight.

[0036] The perspective collection unit automatically collects success stories from different industries and can provide multifaceted solutions to problems. For example, the generation AI automatically collects success stories from different industries and provides multifaceted solutions to problems. For example, it collects and analyzes success stories from the IT industry and the manufacturing industry. This allows it to collect success stories from different industries and provide multifaceted solutions.

[0037] The viewpoint collection unit collects feedback from users in different regions and can provide solutions that reflect the viewpoints of each region. For example, the generation AI collects feedback from users in different regions and provides solutions that reflect the viewpoints of each region. For example, feedback from users in Asia and Europe is collected and analyzed. In this way, by collecting feedback from different regions, it is possible to provide solutions that reflect the viewpoints of each region.

[0038] The solution generation unit can learn the success rates of past solutions and prioritize generating the most effective solutions. For example, the generation AI stores the success rates of past solutions in a database and prioritizes generating the most effective solutions. For example, it makes new proposals based on marketing strategies that have been successful in the past. This allows it to learn past success rates and provide effective solutions.

[0039] The solution generation unit can simulate different solutions and select the optimal solution. For example, the generation AI simulates different solutions and selects the optimal solution. For example, it simulates multiple marketing strategies and proposes the most effective strategy. This allows the optimal solution to be provided by simulating different solutions.

[0040] The solution generation unit can combine knowledge from different fields to create new solutions. For example, the generation AI combines knowledge from different fields to create new solutions. For example, it combines knowledge from the medical and IT fields to propose new medical technologies. This makes it possible to provide new solutions by combining knowledge from different fields.

[0041] The solution generation unit can improve and continuously optimize solutions based on user feedback. For example, the generation AI improves and continuously optimizes solutions based on user feedback. For example, it improves marketing strategies by reflecting user opinions. This makes it possible to provide continuously optimized solutions by improving solutions based on user feedback.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The problem input unit can automatically search for related past success stories based on the user's input and present them to the user. For example, if the user inputs "I want to think of a new marketing strategy," the unit searches a database for past successful cases for similar problems and presents them to the user. This allows the user to find more effective solutions by referring to past success stories. The problem input unit can also learn from problems and solutions previously input by the user and automatically suggest solutions for similar problems. For example, if the user previously inputs "a method for promoting a new product," a new proposal can be made based on that solution. Furthermore, the problem input unit can analyze the user's input and provide related trends and the latest information. For example, when the user inputs "a new marketing strategy," the unit can present the latest marketing trends, allowing the user to consider solutions based on the latest information.

[0044] When collecting data from different cultures and environments, the perspective collection unit can provide customized information based on the user's interests. For example, if a user is interested in a particular region or culture, information related to that region or culture is preferentially collected and provided to the user. This allows the user to obtain information based on their own interests. The perspective collection unit can also predict and provide information that the user is likely to be interested in based on the user's past search history and behavioral history. For example, if a user previously searched for "marketing strategies in Asia," the latest information related to Asia can be provided. Furthermore, the perspective collection unit can collect opinions from experts and influencers in different fields and provide them to the user. For example, by collecting opinions from marketing experts and influencers and providing them to the user, solutions can be considered from a more multifaceted perspective.

[0045] The solution generation unit can simulate different scenarios based on user input and select the optimal solution. For example, if the user inputs a "new marketing strategy," the unit can simulate multiple marketing strategies and propose the most effective one. This allows the user to compare different scenarios and select the optimal solution. The solution generation unit can also improve and continuously optimize solutions based on user feedback. For example, if the user provides feedback on a proposed solution, the solution can be improved based on that feedback and reflected in the next proposal. Furthermore, the solution generation unit can combine knowledge from different fields to create new solutions. For example, it can combine knowledge from the medical and IT fields to propose new medical technologies. This makes it possible to provide new solutions by combining knowledge from different fields.

[0046] The problem input unit can provide related trends and the latest information based on the user's input. For example, when a user inputs "a new marketing strategy," the latest marketing trends can be presented, allowing the user to consider solutions based on the latest information. This allows the user to always have the latest information and find more effective solutions. The problem input unit can also predict and provide information that the user is likely to be interested in based on the user's past search history and behavioral history. For example, if a user previously searched for "marketing strategy in Asia," the latest information related to Asia can be provided. Furthermore, the problem input unit can analyze the user's input and automatically generate related questions. For example, in response to the input "I want to consider a new marketing strategy," questions such as "What is the target market?" can be generated to dig deeper into the details of the problem.

[0047] The suggestion unit can improve and continuously optimize solutions based on user feedback. For example, if a user provides feedback on a proposed solution, the solution can be improved based on that feedback and reflected in the next proposal. This makes it possible to provide continuously optimized solutions by improving solutions based on user feedback. The suggestion unit can also analyze user feedback and identify common issues or problems. For example, if similar feedback is received from multiple users, the suggestion unit can identify common issues based on that feedback and propose solutions. Furthermore, the suggestion unit can create new solutions based on user feedback. For example, by reflecting user opinions and proposing a new marketing strategy, it is possible to provide solutions that meet the user's needs.

[0048] The perspective collection unit can collect feedback from users in different regions and provide solutions that reflect the regional perspectives. For example, by collecting and analyzing feedback from users in Asia and Europe, it is possible to provide solutions that reflect the regional perspectives. In this way, by collecting feedback from different regions, it is possible to provide solutions that reflect the regional perspectives. The perspective collection unit can also collect and analyze information based on the cultures and environments of different regions. For example, by collecting and analyzing marketing strategies based on the cultures and environments of Asia, it is possible to provide solutions that reflect the regional perspectives. Furthermore, the perspective collection unit can collect opinions of experts and influencers in different regions and provide them to users. For example, by collecting opinions of marketing experts in Asia and Europe and providing them to users, it is possible to provide solutions that reflect the regional perspectives.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The task input unit inputs the task or problem presented by the user. For example, a user can input a task such as "I want to think of a new marketing strategy." The task input unit also analyzes the task entered by the user and understands its content. For example, the generation AI analyzes the user's input and collects related information. Step 2: The perspective collection unit collects diverse perspectives from cultures, environments, industries, and fields of expertise around the world based on the issues and problems entered by the problem input unit. For example, it collects marketing strategies from different countries and regions, success stories from different industries, and knowledge from different fields of expertise. Step 3: The solution generation unit generates new solutions and ideas based on the multiple perspectives collected by the perspective collection unit. For example, it combines information from different cultures and environments to propose a new marketing strategy. Step 4: The proposal unit presents the solutions and ideas generated by the solution generation unit to the user. For example, the generation AI may make a proposal such as, "As a new marketing strategy, we propose that you implement a campaign using social media."

[0051] (Example 2) The multifaceted problem-solving AI platform according to an embodiment of the present invention is a system in which AI analyzes and aggregates multifaceted perspectives obtained from cultures, environments, industries, and specialized fields around the world for issues and problems presented by users, and generates and proposes new solutions and ideas. This enables the multifaceted problem-solving AI platform to efficiently and effectively provide solutions to any issues and problems presented by users.

[0052] A multifaceted problem-solving AI platform according to an embodiment includes a problem input unit, a perspective collection unit, a solution generation unit, and a proposal unit. The problem input unit inputs a problem or issue presented by a user. For example, a user may input a problem such as "I want to think of a new marketing strategy." The problem input unit also analyzes the problem input by the user and understands its content. For example, a generation AI analyzes the user's input content and collects related information. The perspective collection unit collects multifaceted perspectives from cultures, environments, industries, and fields of expertise around the world based on the problem or issue input by the problem input unit. For example, it collects marketing strategies from different countries and regions, success stories from different industries, knowledge from different fields of expertise, etc. The solution generation unit generates new solutions and ideas based on the multifaceted perspectives collected by the perspective collection unit. For example, it combines information obtained from different cultures and environments to propose a new marketing strategy. The proposal unit presents the solutions and ideas generated by the solution generation unit to the user. For example, the generation AI makes a proposal in the form of, "As a new marketing strategy, I propose that you implement a campaign utilizing social media." As a result, the multifaceted problem-solving AI platform according to the embodiment can provide solutions from multiple perspectives to problems with diverse backgrounds.

[0053] The problem input unit can refer to the user's past problem input history and prioritize analysis of solutions to similar problems. For example, the generation AI stores the user's past problem input history in a database, and the problem input unit references that history when a similar problem is input. For example, if a user who previously input a "new marketing strategy" inputs a similar problem again, the unit will prioritize analysis of past solutions. This makes it possible to efficiently provide solutions by utilizing past history.

[0054] The problem input unit can automatically generate related questions based on the user's input and delve deeper into the details of the problem. For example, the problem input unit uses a generation AI to analyze the user's input and automatically generate related questions. For example, in response to the input "I want to think of a new marketing strategy," it generates a question such as "What is the target market?" This allows for deeper digging into the details of the problem, making it possible to provide more accurate solutions.

[0055] The task input unit can use the emotion estimation function to analyze the emotion of the user at the time of input and prioritize tasks based on the emotion. For example, the task input unit uses the emotion estimation function to analyze the emotion of the user at the time of input and prioritizes tasks with a strong positive emotion. For example, when a user inputs "new marketing strategy," if the emotion score is high, that task is prioritized in the analysis. This makes it possible to provide more effective solutions by prioritizing tasks based on the user's emotion.

[0056] The task input unit can also accept tasks as voice or image data, which the generation AI can analyze and convert into text. For example, the task input unit accepts task input as voice data, and the generation AI converts it into text using voice recognition technology. For example, if a user inputs by voice, "I want to think of a new marketing strategy," the content is converted into text. The task input unit can also accept tasks as image data, and the generation AI converts it into text using image analysis technology. For example, if a user inputs handwritten notes as an image, the content is converted into text. This improves user convenience by converting voice and image data into text.

[0057] The problem input unit can automatically translate problem inputs in different languages ​​and provide solutions from a global perspective. For example, the problem input unit automatically translates problem inputs in different languages, and the generation AI analyzes the content. For example, if a user inputs "I would like to come up with a new marketing strategy" in French, the content is automatically translated and analyzed. In this way, by automatically translating problem inputs in different languages, solutions from a global perspective can be provided.

[0058] The problem input unit can use the emotion estimation function to analyze the emotion of the user when entering input in real time and provide feedback to elicit positive emotions. The problem input unit can, for example, use the emotion estimation function to analyze the emotion of the user when entering input in real time and provide feedback to elicit positive emotions. For example, when the user enters "new marketing strategy," a positive message is displayed. This makes it possible to analyze the user's emotion in real time and elicit positive emotions, thereby providing a better solution.

[0059] The perspective collection unit can collect data from different cultures and environments in real time and perform analysis based on the latest information. For example, the generation AI can collect data from different cultures and environments in real time and perform analysis based on the latest information. For example, it can collect and analyze marketing strategies from different countries. This allows analysis to be based on the latest information, making it possible to provide more appropriate solutions.

[0060] The perspective gathering unit automatically collects expert opinions and papers, making it possible to provide in-depth insight into issues. For example, generative AI automatically collects expert opinions and papers to provide in-depth insight into issues. For example, it collects and analyzes the opinions of marketing experts and the latest research papers. This allows for the collection of expert opinions and papers to provide even deeper insight.

[0061] The viewpoint collection unit can use the emotion estimation function to evaluate the emotional value of the collected information and prioritize analysis of positive information. The viewpoint collection unit, for example, uses the emotion estimation function to evaluate the emotional value of the collected information and prioritize analysis of positive information. For example, it prioritizes analysis of marketing strategies with high positive emotion scores. In this way, by evaluating the emotional value, it is possible to prioritize analysis of positive information.

[0062] The perspective collection unit automatically collects success stories from different industries and can provide multifaceted solutions to problems. For example, the generation AI automatically collects success stories from different industries and provides multifaceted solutions to problems. For example, it collects and analyzes success stories from the IT industry and the manufacturing industry. This allows it to collect success stories from different industries and provide multifaceted solutions.

[0063] The viewpoint collection unit collects feedback from users in different regions and can provide solutions that reflect the viewpoints of each region. For example, the generation AI collects feedback from users in different regions and provides solutions that reflect the viewpoints of each region. For example, feedback from users in Asia and Europe is collected and analyzed. In this way, by collecting feedback from different regions, it is possible to provide solutions that reflect the viewpoints of each region.

[0064] The viewpoint collection unit can use the emotion estimation function to analyze the user's emotional response to the collected information and provide preferentially information that is likely to resonate emotionally. The viewpoint collection unit, for example, uses the emotion estimation function to analyze the user's emotional response to the collected information and provide preferentially information that is likely to resonate emotionally. For example, information with a high number of positive emotional responses is provided preferentially. This improves user satisfaction by providing preferentially information that is likely to resonate emotionally.

[0065] The solution generation unit can learn the success rates of past solutions and prioritize generating the most effective solutions. For example, the generation AI stores the success rates of past solutions in a database and prioritizes generating the most effective solutions. For example, it makes new proposals based on marketing strategies that have been successful in the past. This allows it to learn past success rates and provide effective solutions.

[0066] The solution generation unit can simulate different solutions and select the optimal solution. For example, the generation AI simulates different solutions and selects the optimal solution. For example, it simulates multiple marketing strategies and proposes the most effective strategy. This allows the optimal solution to be provided by simulating different solutions.

[0067] The solution generation unit can use the emotion estimation function to generate a solution that will evoke the most positive emotion in the user. For example, the solution generation unit uses the emotion estimation function to generate a solution that will evoke the most positive emotion in the user. For example, the solution generation unit preferentially proposes a solution with a high emotion score for the user. This improves user satisfaction by providing a solution that will evoke the most positive emotion in the user.

[0068] The solution generation unit can combine knowledge from different fields to create new solutions. For example, the generation AI combines knowledge from different fields to create new solutions. For example, it combines knowledge from the medical and IT fields to propose new medical technologies. This makes it possible to provide new solutions by combining knowledge from different fields.

[0069] The solution generation unit can improve and continuously optimize solutions based on user feedback. For example, the generation AI improves and continuously optimizes solutions based on user feedback. For example, it improves marketing strategies by reflecting user opinions. This makes it possible to provide continuously optimized solutions by improving solutions based on user feedback.

[0070] The solution generation unit can use the emotion estimation function to monitor the user's emotional reaction to the generated solution in real time and provide an optimal solution. The solution generation unit can, for example, use the emotion estimation function to monitor the user's emotional reaction to the generated solution in real time and provide an optimal solution. For example, solutions with a high user emotion score are preferentially proposed. In this way, by monitoring the user's emotional reaction in real time, an optimal solution can be provided.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The problem input unit can automatically search for related past success stories based on the user's input and present them to the user. For example, if the user inputs "I want to think of a new marketing strategy," the unit searches a database for past successful cases for similar problems and presents them to the user. This allows the user to find more effective solutions by referring to past success stories. The problem input unit can also learn from problems and solutions previously input by the user and automatically suggest solutions for similar problems. For example, if the user previously inputs "a method for promoting a new product," a new proposal can be made based on that solution. Furthermore, the problem input unit can analyze the user's input and provide related trends and the latest information. For example, when the user inputs "a new marketing strategy," the unit can present the latest marketing trends, allowing the user to consider solutions based on the latest information.

[0073] When collecting data from different cultures and environments, the perspective collection unit can provide customized information based on the user's interests. For example, if a user is interested in a particular region or culture, information related to that region or culture is preferentially collected and provided to the user. This allows the user to obtain information based on their own interests. The perspective collection unit can also predict and provide information that the user is likely to be interested in based on the user's past search history and behavioral history. For example, if a user previously searched for "marketing strategies in Asia," the latest information related to Asia can be provided. Furthermore, the perspective collection unit can collect opinions from experts and influencers in different fields and provide them to the user. For example, by collecting opinions from marketing experts and influencers and providing them to the user, solutions can be considered from a more multifaceted perspective.

[0074] The solution generation unit can simulate different scenarios based on user input and select the optimal solution. For example, if the user inputs a "new marketing strategy," the unit can simulate multiple marketing strategies and propose the most effective one. This allows the user to compare different scenarios and select the optimal solution. The solution generation unit can also improve and continuously optimize solutions based on user feedback. For example, if the user provides feedback on a proposed solution, the solution can be improved based on that feedback and reflected in the next proposal. Furthermore, the solution generation unit can combine knowledge from different fields to create new solutions. For example, it can combine knowledge from the medical and IT fields to propose new medical technologies. This makes it possible to provide new solutions by combining knowledge from different fields.

[0075] The suggestion unit can estimate the user's emotions and provide feedback based on the emotions. For example, if the user expresses positive emotions toward a proposed solution, the solution can be emphasized and presented. This allows the user to prioritize solutions that the user feels positive about. The suggestion unit can also customize the way in which the solution is presented based on the user's emotions. For example, if the user is feeling stressed, a presentation method that helps the user relax can be adopted. Furthermore, the suggestion unit can monitor the user's emotions in real time and provide support according to the emotions. For example, if the user is confused, additional explanations or support can be provided to make it easier for the user to understand the solution.

[0076] The problem input unit can provide related trends and the latest information based on the user's input. For example, when a user inputs "a new marketing strategy," the latest marketing trends can be presented, allowing the user to consider solutions based on the latest information. This allows the user to always have the latest information and find more effective solutions. The problem input unit can also predict and provide information that the user is likely to be interested in based on the user's past search history and behavioral history. For example, if a user previously searched for "marketing strategy in Asia," the latest information related to Asia can be provided. Furthermore, the problem input unit can analyze the user's input and automatically generate related questions. For example, in response to the input "I want to consider a new marketing strategy," questions such as "What is the target market?" can be generated to dig deeper into the details of the problem.

[0077] The viewpoint collection unit can use the emotion estimation function to evaluate the emotional value of the collected information and prioritize analysis of positive information. For example, by prioritizing analysis of marketing strategies with a high positive emotion score, it is possible to provide information that is useful to the user. In this way, by evaluating the emotional value, it is possible to prioritize analysis of positive information. The viewpoint collection unit can also analyze the user's emotional response to the collected information and prioritize provision of information that is likely to resonate with the user emotionally. For example, by prioritizing provision of information with a high number of positive emotional responses, it is possible to improve user satisfaction. Furthermore, the viewpoint collection unit can evaluate the emotional value of the collected information and filter out negative information. For example, by excluding information with a high negative emotion score, it is possible to provide information that is useful to the user.

[0078] The solution generation unit can estimate the user's emotions and generate a solution based on the emotions. For example, if the user expresses positive emotions, a solution that satisfies the user can be generated based on the emotions. This makes it possible to provide a solution that satisfies the user by providing a solution based on the user's emotions, thereby improving user satisfaction. The solution generation unit can also monitor the user's emotions in real time and provide a solution that corresponds to the emotions. For example, if the user is feeling stressed, it can propose a solution that will help the user relax. Furthermore, the solution generation unit can analyze the user's emotions and generate a solution that is likely to resonate with the user emotionally. For example, it is possible to improve user satisfaction by preferentially proposing solutions that the user can easily resonate with.

[0079] The suggestion unit can improve and continuously optimize solutions based on user feedback. For example, if a user provides feedback on a proposed solution, the solution can be improved based on that feedback and reflected in the next proposal. This makes it possible to provide continuously optimized solutions by improving solutions based on user feedback. The suggestion unit can also analyze user feedback and identify common issues or problems. For example, if similar feedback is received from multiple users, the suggestion unit can identify common issues based on that feedback and propose solutions. Furthermore, the suggestion unit can create new solutions based on user feedback. For example, by reflecting user opinions and proposing a new marketing strategy, it is possible to provide solutions that meet the user's needs.

[0080] The perspective collection unit can collect feedback from users in different regions and provide solutions that reflect the regional perspectives. For example, by collecting and analyzing feedback from users in Asia and Europe, it is possible to provide solutions that reflect the regional perspectives. In this way, by collecting feedback from different regions, it is possible to provide solutions that reflect the regional perspectives. The perspective collection unit can also collect and analyze information based on the cultures and environments of different regions. For example, by collecting and analyzing marketing strategies based on the cultures and environments of Asia, it is possible to provide solutions that reflect the regional perspectives. Furthermore, the perspective collection unit can collect opinions of experts and influencers in different regions and provide them to users. For example, by collecting opinions of marketing experts in Asia and Europe and providing them to users, it is possible to provide solutions that reflect the regional perspectives.

[0081] The solution generation unit can use the emotion estimation function to monitor the user's emotional response to the generated solution in real time and provide an optimal solution. For example, by preferentially proposing a solution with a high user emotion score, a solution that satisfies the user can be provided. In this way, by monitoring the user's emotional response in real time, an optimal solution can be provided. The solution generation unit can also analyze the user's emotional response and generate a solution that is likely to resonate with emotionally. For example, by preferentially proposing a solution that the user can easily resonate with, user satisfaction can be improved. Furthermore, the solution generation unit can analyze the user's emotion and generate a solution based on the emotion. For example, if the user shows positive emotion, a solution that satisfies the user can be provided by generating a solution based on that emotion.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The task input unit inputs the task or problem presented by the user. For example, a user can input a task such as "I want to think of a new marketing strategy." The task input unit also analyzes the task entered by the user and understands its content. For example, the generation AI analyzes the user's input and collects related information. Step 2: The perspective collection unit collects diverse perspectives from cultures, environments, industries, and fields of expertise around the world based on the issues and problems entered by the problem input unit. For example, it collects marketing strategies from different countries and regions, success stories from different industries, and knowledge from different fields of expertise. Step 3: The solution generation unit generates new solutions and ideas based on the multiple perspectives collected by the perspective collection unit. For example, it combines information from different cultures and environments to propose a new marketing strategy. Step 4: The proposal unit presents the solutions and ideas generated by the solution generation unit to the user. For example, the generation AI may make a proposal such as, "As a new marketing strategy, we propose that you implement a campaign using social media."

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. a task input section for inputting tasks and problems presented by a user; a viewpoint collection unit that collects multifaceted viewpoints from cultures, environments, industries, and specialized fields around the world based on the tasks and problems input by the task input unit; a solution generation unit that generates new solutions and ideas based on the multiple perspectives collected by the perspective collection unit; a proposal unit that presents the solutions and ideas generated by the solution generation unit to a user. A system characterized by:

2. The task input unit Tasks are accepted as audio or image data, and the generative AI analyzes and converts them into text.

2. The system of claim 1.

3. The viewpoint collection unit Collect data from different cultures and environments in real time and conduct analysis based on the latest information 2. The system of claim 1.

4. The solution generator Learns the success rate of past solutions and prioritizes the generation of the most effective solutions 2. The system of claim 1.

5. The task input unit Analyze user input sentiment and prioritize issues based on sentiment 2. The system of claim 1.

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