System

Abstract

An object of a system according to an embodiment is for a user to predict a result of his / her own determination and obtain an appropriate recommendation.SOLUTION: A system according to an embodiment includes a situation input unit, a determination input unit, a result prediction unit, a recommendation unit, and an additional information confirmation unit. The situation input unit inputs a situation and a problem of the user. The determination input unit inputs a determination of the user based on the situation and the problem input by the situation input unit. The result prediction unit predicts a result of the determination input by the determination input unit. The recommendation unit recommends another method when the result prediction unit predicts an undesirable result. The additional information confirmation unit confirms additional information when the situation input by the situation input unit is insufficient.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 technologies have had the problem that it is difficult for users to predict the results of their own decisions and obtain appropriate recommendations.

[0005] The system according to the embodiment aims to enable users to predict the results of their own decisions and obtain appropriate recommendations. [Means for solving the problem]

[0006] The system according to the embodiment includes a situation input unit, a judgment input unit, a result prediction unit, a recommendation unit, and an additional information confirmation unit. The situation input unit inputs the user's situation and problems. The judgment input unit inputs the user's judgment based on the situation and problems input by the situation input unit. The result prediction unit predicts the result of the judgment input by the judgment input unit. The recommendation unit recommends an alternative method when the result prediction unit predicts an undesirable result. The additional information confirmation unit confirms additional information when the situation input by the situation input unit is insufficient. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to predict the outcome of their own decisions and obtain appropriate recommendations. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) A decision support system according to an embodiment of the present invention is a system in which a generative AI predicts the outcome of a decision a user is about to make and provides appropriate advice. This allows the decision support system to know in advance what outcome the user's decision will bring, enabling the user to make a more appropriate decision.

[0029] A decision support system according to an embodiment includes a situation input unit, a decision input unit, a result prediction unit, a recommendation unit, and an additional information confirmation unit. The situation input unit inputs a user's situation and problems. For example, the user inputs, "I'm considering fundraising methods to start a new business." The decision input unit inputs the user's decision based on the situation and problems input by the situation input unit. For example, the user inputs, "I will get a loan from a bank." The result prediction unit predicts the outcome of the decision input by the decision input unit. For example, the generation AI predicts, "If I get a loan from a bank, my repayment plan may become difficult." The recommendation unit recommends an alternative method if the outcome prediction unit predicts an undesirable outcome. For example, the generation AI recommends, "Use crowdfunding." The additional information confirmation unit confirms additional information if the situation input by the situation input unit is insufficient. For example, the generation AI asks, "Please tell me the specific amount of funding you are aiming for." This enables the decision support system according to an embodiment to provide appropriate advice for the user's decision and make a better decision.

[0030] The situation input section allows the generation AI to refer to similar past cases and present reference examples for situations and problems entered by the user. For example, if a user enters, "I am considering how to raise funds to start a new business," the situation input section searches a database for past successful cases in similar situations and presents specific examples. For example, it could present a case where a company was successful using crowdfunding. This allows users to make better decisions by referring to past successful cases.

[0031] The situation input unit analyzes the user's input in real time and can automatically suggest related additional information based on the input. For example, if a user inputs, "I am considering ways to raise funds to start a new business," the generation AI will analyze the situation in real time and automatically suggest related fundraising methods and success stories. For example, it will provide information on angel investors and venture capitalists. This allows users to quickly obtain the information they need.

[0032] The situation input unit can also accommodate voice and image input of information entered by the user, providing a wider variety of input methods. For example, the situation input unit allows the user to input by voice, such as "I am considering ways to raise funds to start a new business," and the generation AI analyzes the voice data and converts it into text. For example, voice input can be performed using a smartphone microphone. This allows the user to input information in a wider variety of ways.

[0033] The situation input unit can add a function of receiving feedback from experts in different industries and fields in real time. For example, when a user inputs "I am considering how to raise funds to start a new business," the situation input unit adds a function of receiving feedback from experts in different industries in real time. For example, advice from experts in the financial industry is provided. This allows the user to receive feedback from experts in different industries and fields in real time.

[0034] The decision input unit allows the generation AI to automatically analyze the background and rationale of a decision entered by the user and provide supplementary information. For example, if a user enters "take out a loan from a bank," the decision input unit automatically analyzes the background and rationale of the decision and provides supplementary information. For example, it may present the advantages and disadvantages of a bank loan. This makes it easier for the user to understand the background and rationale of the decision.

[0035] The decision input unit allows the generation AI to present past examples of success and failure based on the input content of the decision, thereby supporting the user's decision. For example, if the user inputs "obtain a loan from a bank," the decision input unit will present past examples of success and failure to support the user's decision. For example, it will introduce examples of companies that have succeeded and failed after receiving bank loans. This allows the user to make a decision by referring to past examples.

[0036] The judgment input unit can change the input of the judgment to a format in which the user selects from multiple options, making it easier for the user to input the judgment. For example, instead of the user inputting "obtain a loan from a bank," the judgment input unit changes the format to a format in which the user selects from multiple options, making it easier for the user to input the judgment. For example, the options provided are "bank loan," "crowdfunding," and "angel investor." This allows the user to input the judgment easily.

[0037] The decision input section can be configured so that the generation AI automatically generates different scenarios and the user can select the most appropriate decision from among them. For example, instead of the user inputting "obtaining a loan from a bank," the decision input section can automatically generate different scenarios and the user can select the most appropriate decision from among them. For example, the decision input section can present scenarios of "bank loan," "crowdfunding," and "angel investors." This allows the user to select the most appropriate decision.

[0038] The outcome prediction unit can refer to not only past data but also real-time market trends and news when the generation AI predicts the outcome of a decision. For example, if a user inputs "take out a loan from a bank," the generation AI will predict the outcome by referring to not only past data but also real-time market trends and news. For example, it will take into account current interest rate trends and the impact of monetary policy. This allows the user to predict the outcome of their decision based on the latest information.

[0039] When predicting the outcome of a decision, the outcome prediction unit allows the generation AI to generate multiple scenarios and perform a risk assessment for each scenario. For example, if a user inputs "obtain a loan from a bank," the outcome prediction unit generates multiple scenarios and performs a risk assessment for each scenario. For example, it considers scenarios of rising interest rates and economic recession. This allows the user to evaluate risk based on multiple scenarios and make the optimal decision.

[0040] When predicting the outcome of a decision, the outcome prediction unit can refer to data from different industries and regions and make predictions from a global perspective. For example, if a user inputs "obtain a loan from a bank," the outcome prediction unit will refer to data from different industries and regions and make predictions from a global perspective. For example, it will take into account trends in international financial markets and success stories from other countries. This allows users to predict the outcome of their decision from a global perspective.

[0041] The result prediction unit allows the generation AI to visualize the predicted results, allowing the user to intuitively understand them. For example, if a user inputs "obtain a loan from a bank," the result prediction unit visualizes the predicted results, allowing the user to intuitively understand them. For example, the result prediction unit displays repayment plans and risk assessments in graphs and charts. This allows the user to intuitively understand the predicted results.

[0042] When the generation AI makes a recommendation, the recommendation unit can refer to past success stories and failure stories and provide specific advice. For example, if a user inputs "obtain a loan from a bank" and an undesirable outcome is predicted, the generation AI will refer to past success stories and failure stories and provide specific advice. For example, it may introduce successful examples of crowdfunding. This allows the user to receive specific advice.

[0043] When making a recommendation, the generation AI can refer to the user's past decision history and suggest a method that suits the user's preferences. For example, if a user inputs "obtain a loan from a bank" and an undesirable result is predicted, the generation AI will refer to the user's past decision history and suggest a method that suits the user's preferences. For example, it may suggest crowdfunding. This allows the user to be suggested a method that suits their preferences.

[0044] The recommendation unit can add a function to receive feedback from experts in different industries or fields in real time when making a recommendation. For example, if a user inputs "obtain a loan from a bank" and an undesirable result is predicted, the recommendation unit adds a function to receive feedback from experts in different industries in real time. For example, advice from experts in the financial industry is provided. This allows the user to receive feedback from experts in different industries or fields in real time.

[0045] When the generation AI makes a recommendation, the recommendation unit monitors the user's current situation and environment in real time and can make recommendations based on that. For example, if a user inputs "obtain a loan from a bank" and an undesirable outcome is predicted, the generation AI will monitor the user's current situation and environment in real time and make recommendations based on that. For example, it will provide advice that takes into account the current economic situation and market trends. This allows the user to receive recommendations based on their current situation and environment.

[0046] When the generation AI checks the additional information, the additional information confirmation unit can refer to the user's past input history and automatically complete the necessary information. For example, when the user inputs "I am considering how to raise funds to start a new business," the generation AI refers to the user's past input history and automatically completes the necessary information. For example, the additional information confirmation unit refers to previously input business plans and fundraising goals. This allows the user to automatically complete the necessary information based on their past input history.

[0047] When confirming additional information, the additional information confirmation unit allows the generation AI to monitor the user's current situation and environment in real time and ask questions based on that. For example, when a user inputs, "I'm considering ways to raise funds to start a new business," the additional information confirmation unit allows the generation AI to monitor the user's current situation and environment in real time and ask questions based on that. For example, the additional information confirmation unit asks questions that take into account the current economic situation and market trends. This allows the user to receive questions based on their current situation and environment.

[0048] The additional information verification unit can add a function of receiving feedback from experts in different industries or fields in real time when verifying the additional information. For example, the additional information verification unit adds a function of receiving feedback from experts in different industries in real time when verifying the additional information, for example, if the user inputs "I am considering a method of raising funds to start a new business." For example, advice from experts in the financial industry is provided. This allows the user to receive feedback from experts in different industries or fields in real time.

[0049] The additional information confirmation unit can visualize the user's input content when the generation AI confirms the additional information, allowing for intuitive understanding. For example, if a user inputs "I am considering a method of raising funds to start a new business," and the generation AI confirms the additional information, the additional information confirmation unit can visualize the user's input content to allow for intuitive understanding. For example, the additional information confirmation unit can display the fundraising plan in graphs and charts. This allows the user to intuitively understand the additional information.

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

[0051] In a decision support system, a generative AI can predict the long-term impact of a decision entered by a user and evaluate future risks and benefits. For example, if a user enters "obtaining a loan from a bank," the generative AI predicts the impact of that decision five or ten years from now and evaluates the long-term risks and benefits. This allows users to make decisions from a long-term perspective, as well as a short-term perspective. Similarly, if a user enters "considering ways to raise funds to start a new business," the generative AI predicts what market trends will affect that business in the future and evaluates the likelihood of long-term success. Furthermore, if a user enters "introduce new technology," the generative AI predicts what technological innovations and market fluctuations will affect that technology in the future and evaluates the long-term risks and benefits. This allows users to make decisions from a long-term perspective.

[0052] In a decision support system, a generative AI can evaluate the social impact of a decision entered by a user and present the social risks and benefits. For example, if a user enters "start a new business," the generative AI will evaluate the impact that business will have on the local community and the environment and present the social risks and benefits. This allows users to make decisions from a societal perspective. Similarly, if a user enters "develop a new product," the generative AI will evaluate the impact that product will have on consumers and the market and present the societal risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI will evaluate the impact that policy will have on society as a whole and present the societal risks and benefits. This allows users to make decisions from a societal perspective.

[0053] In a decision support system, a generative AI can evaluate the ethical impact of a decision entered by a user and present the ethical risks and benefits. For example, if a user enters "start a new business," the generative AI will evaluate what ethical issues the business may cause and present the ethical risks and benefits. This allows the user to make decisions from an ethical perspective. Similarly, if a user enters "introduce new technology," the generative AI will evaluate what ethical issues the technology may cause and present the ethical risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI will evaluate what ethical issues the policy may cause and present the ethical risks and benefits. This allows the user to make decisions from an ethical perspective.

[0054] In a decision support system, a generative AI can evaluate the cultural impact of a decision entered by a user and present the cultural risks and benefits. For example, if a user enters "start a new business," the generative AI evaluates the impact that business will have on local culture and traditions and presents the cultural risks and benefits. This allows users to make decisions from a cultural perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the impact that product will have on consumer culture and values ​​and presents the cultural risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the cultural impact of that policy and presents the cultural risks and benefits. This allows users to make decisions from a cultural perspective.

[0055] In a decision support system, the generative AI can evaluate the environmental impact of a decision entered by a user and present the environmental risks and benefits. For example, if a user enters "start a new business," the generative AI will evaluate the environmental impact of that business and present the environmental risks and benefits. This allows the user to make decisions from an environmental perspective. Similarly, if a user enters "develop a new product," the generative AI will evaluate the environmental impact of that product and present the environmental risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI will evaluate the environmental impact of that policy and present the environmental risks and benefits. This allows the user to make decisions from an environmental perspective.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The situation input section inputs the user's situation and problems. For example, the user inputs "I am considering how to raise funds to start a new business." Step 2: The decision input unit inputs the user's decision based on the situation and problems input by the situation input unit. For example, the user inputs "Get a loan from a bank." Step 3: The outcome prediction unit predicts the outcome of the decision input by the decision input unit. For example, the generation AI predicts that "if you take out a loan from a bank, your repayment plan may become stricter." Step 4: The recommendation unit recommends alternative methods if the outcome prediction unit predicts an undesirable outcome. For example, the generation AI might recommend "using crowdfunding." Step 5: The additional information confirmation unit checks for additional information if the situation input by the situation input unit is insufficient. For example, the generation AI may ask, "Please tell me the specific amount of fundraising you are aiming for."

[0058] (Example 2) A decision support system according to an embodiment of the present invention is a system in which a generative AI predicts the outcome of a decision a user is about to make and provides appropriate advice. This allows the decision support system to know in advance what outcome the user's decision will bring, enabling the user to make a more appropriate decision.

[0059] A decision support system according to an embodiment includes a situation input unit, a decision input unit, a result prediction unit, a recommendation unit, and an additional information confirmation unit. The situation input unit inputs a user's situation and problems. For example, the user inputs, "I'm considering fundraising methods to start a new business." The decision input unit inputs the user's decision based on the situation and problems input by the situation input unit. For example, the user inputs, "I will get a loan from a bank." The result prediction unit predicts the outcome of the decision input by the decision input unit. For example, the generation AI predicts, "If I get a loan from a bank, my repayment plan may become difficult." The recommendation unit recommends an alternative method if the outcome prediction unit predicts an undesirable outcome. For example, the generation AI recommends, "Use crowdfunding." The additional information confirmation unit confirms additional information if the situation input by the situation input unit is insufficient. For example, the generation AI asks, "Please tell me the specific amount of funding you are aiming for." This enables the decision support system according to an embodiment to provide appropriate advice for the user's decision and make a better decision.

[0060] The situation input section allows the generation AI to refer to similar past cases and present reference examples for situations and problems entered by the user. For example, if a user enters, "I am considering how to raise funds to start a new business," the situation input section searches a database for past successful cases in similar situations and presents specific examples. For example, it could present a case where a company was successful using crowdfunding. This allows users to make better decisions by referring to past successful cases.

[0061] The situation input unit analyzes the user's input in real time and can automatically suggest related additional information based on the input. For example, if a user inputs, "I am considering ways to raise funds to start a new business," the generation AI will analyze the situation in real time and automatically suggest related fundraising methods and success stories. For example, it will provide information on angel investors and venture capitalists. This allows users to quickly obtain the information they need.

[0062] The situation input unit can use the emotion estimation function to analyze the user's emotions at the time of input and provide advice to reduce stress and anxiety. For example, when a user inputs "I'm considering ways to raise funds to start a new business," the situation input unit can use the emotion estimation function to analyze the user's stress level and provide advice to help them relax. For example, the unit can suggest taking a deep breath or a short break. This can reduce the user's stress and anxiety and enable them to make better decisions.

[0063] The situation input unit can also accommodate voice and image input of information entered by the user, providing a wider variety of input methods. For example, the situation input unit allows the user to input by voice, such as "I am considering ways to raise funds to start a new business," and the generation AI analyzes the voice data and converts it into text. For example, voice input can be performed using a smartphone microphone. This allows the user to input information in a wider variety of ways.

[0064] The situation input unit can add a function of receiving feedback from experts in different industries and fields in real time. For example, when a user inputs "I am considering how to raise funds to start a new business," the situation input unit adds a function of receiving feedback from experts in different industries in real time. For example, advice from experts in the financial industry is provided. This allows the user to receive feedback from experts in different industries and fields in real time.

[0065] The situation input unit can use the emotion estimation function to monitor the user's emotions in real time when inputting information and provide an interface for eliciting positive emotions. For example, when the user inputs "I'm considering ways to raise funds to start a new business," the situation input unit can use the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, an encouraging message can be displayed. This allows the user to input information while feeling positive emotions.

[0066] The decision input unit allows the generation AI to automatically analyze the background and rationale of a decision entered by the user and provide supplementary information. For example, if a user enters "take out a loan from a bank," the decision input unit automatically analyzes the background and rationale of the decision and provides supplementary information. For example, it may present the advantages and disadvantages of a bank loan. This makes it easier for the user to understand the background and rationale of the decision.

[0067] The decision input unit allows the generation AI to present past examples of success and failure based on the input content of the decision, thereby supporting the user's decision. For example, if the user inputs "obtain a loan from a bank," the decision input unit will present past examples of success and failure to support the user's decision. For example, it will introduce examples of companies that have succeeded and failed after receiving bank loans. This allows the user to make a decision by referring to past examples.

[0068] The judgment input unit can use the emotion estimation function to analyze the emotion of the user when inputting a judgment and provide advice to eliminate emotional bias. For example, when the user inputs "obtaining a loan from a bank," the judgment input unit can use the emotion estimation function to analyze the user's emotion and provide advice to eliminate emotional bias. For example, information to encourage a calm decision is provided. This allows the user to eliminate emotional bias and make a calm decision.

[0069] The judgment input unit can change the input of the judgment to a format in which the user selects from multiple options, making it easier for the user to input the judgment. For example, instead of the user inputting "obtain a loan from a bank," the judgment input unit changes the format to a format in which the user selects from multiple options, making it easier for the user to input the judgment. For example, the options provided are "bank loan," "crowdfunding," and "angel investor." This allows the user to input the judgment easily.

[0070] The decision input section can be configured so that the generation AI automatically generates different scenarios and the user can select the most appropriate decision from among them. For example, instead of the user inputting "obtaining a loan from a bank," the decision input section can automatically generate different scenarios and the user can select the most appropriate decision from among them. For example, the decision input section can present scenarios of "bank loan," "crowdfunding," and "angel investors." This allows the user to select the most appropriate decision.

[0071] The judgment input unit can use the emotion estimation function to monitor the user's emotions in real time when inputting a judgment and provide an interface for eliciting positive emotions. For example, when the user inputs "obtain a loan from a bank," the judgment input unit uses the emotion estimation function to monitor the user's emotions in real time and provides an interface for eliciting positive emotions. For example, an encouraging message can be displayed. This allows the user to input a judgment while feeling positive emotions.

[0072] The outcome prediction unit can refer to not only past data but also real-time market trends and news when the generation AI predicts the outcome of a decision. For example, if a user inputs "take out a loan from a bank," the generation AI will predict the outcome by referring to not only past data but also real-time market trends and news. For example, it will take into account current interest rate trends and the impact of monetary policy. This allows the user to predict the outcome of their decision based on the latest information.

[0073] When predicting the outcome of a decision, the outcome prediction unit allows the generation AI to generate multiple scenarios and perform a risk assessment for each scenario. For example, if a user inputs "obtain a loan from a bank," the outcome prediction unit generates multiple scenarios and performs a risk assessment for each scenario. For example, it considers scenarios of rising interest rates and economic recession. This allows the user to evaluate risk based on multiple scenarios and make the optimal decision.

[0074] The result prediction unit can use the emotion estimation function to analyze the emotions of the user when receiving the prediction result and provide advice to reduce negative emotions. For example, when a user inputs "obtain a loan from a bank" and receives the prediction result, the result prediction unit uses the emotion estimation function to analyze the user's emotions and provide advice to reduce negative emotions. For example, the result prediction unit suggests a risk management method. This allows the user to reduce negative emotions and calmly receive the prediction result.

[0075] When predicting the outcome of a decision, the outcome prediction unit can refer to data from different industries and regions and make predictions from a global perspective. For example, if a user inputs "obtain a loan from a bank," the outcome prediction unit will refer to data from different industries and regions and make predictions from a global perspective. For example, it will take into account trends in international financial markets and success stories from other countries. This allows users to predict the outcome of their decision from a global perspective.

[0076] The result prediction unit allows the generation AI to visualize the predicted results, allowing the user to intuitively understand them. For example, if a user inputs "obtain a loan from a bank," the result prediction unit visualizes the predicted results, allowing the user to intuitively understand them. For example, the result prediction unit displays repayment plans and risk assessments in graphs and charts. This allows the user to intuitively understand the predicted results.

[0077] The result prediction unit can use the emotion estimation function to monitor the user's emotions in real time when receiving the prediction result and provide an interface for eliciting positive emotions. For example, when a user inputs "obtain a loan from a bank" and receives the prediction result, the result prediction unit can use the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, we will introduce a successful case. This allows the user to receive the prediction result with positive emotions.

[0078] When the generation AI makes a recommendation, the recommendation unit can refer to past success stories and failure stories and provide specific advice. For example, if a user inputs "obtain a loan from a bank" and an undesirable outcome is predicted, the generation AI will refer to past success stories and failure stories and provide specific advice. For example, it may introduce successful examples of crowdfunding. This allows the user to receive specific advice.

[0079] When making a recommendation, the generation AI can refer to the user's past decision history and suggest a method that suits the user's preferences. For example, if a user inputs "obtain a loan from a bank" and an undesirable result is predicted, the generation AI will refer to the user's past decision history and suggest a method that suits the user's preferences. For example, it may suggest crowdfunding. This allows the user to be suggested a method that suits their preferences.

[0080] The recommendation unit can use the emotion estimation function to analyze the emotions of the user when receiving the recommendation and provide advice to elicit positive emotions. For example, if the user inputs "obtain a loan from a bank" and an undesirable result is predicted, the recommendation unit can use the emotion estimation function to analyze the user's emotions and provide advice to elicit positive emotions. For example, we will introduce a successful case of crowdfunding. This allows the user to receive the recommendation while feeling positive emotions.

[0081] The recommendation unit can add a function to receive feedback from experts in different industries or fields in real time when making a recommendation. For example, if a user inputs "obtain a loan from a bank" and an undesirable result is predicted, the recommendation unit adds a function to receive feedback from experts in different industries in real time. For example, advice from experts in the financial industry is provided. This allows the user to receive feedback from experts in different industries or fields in real time.

[0082] When the generation AI makes a recommendation, the recommendation unit monitors the user's current situation and environment in real time and can make recommendations based on that. For example, if a user inputs "obtain a loan from a bank" and an undesirable outcome is predicted, the generation AI will monitor the user's current situation and environment in real time and make recommendations based on that. For example, it will provide advice that takes into account the current economic situation and market trends. This allows the user to receive recommendations based on their current situation and environment.

[0083] The recommendation unit can use the emotion estimation function to monitor the user's emotions in real time when receiving recommendations and provide an interface for eliciting positive emotions. For example, if a user inputs "obtain a loan from a bank" and an undesirable result is predicted, the recommendation unit can use the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, we will introduce a success story. This allows the user to receive recommendations with positive emotions.

[0084] When the generation AI checks the additional information, the additional information confirmation unit can refer to the user's past input history and automatically complete the necessary information. For example, when the user inputs "I am considering how to raise funds to start a new business," the generation AI refers to the user's past input history and automatically completes the necessary information. For example, the additional information confirmation unit refers to previously input business plans and fundraising goals. This allows the user to automatically complete the necessary information based on their past input history.

[0085] When confirming additional information, the additional information confirmation unit allows the generation AI to monitor the user's current situation and environment in real time and ask questions based on that. For example, when a user inputs, "I'm considering ways to raise funds to start a new business," the additional information confirmation unit allows the generation AI to monitor the user's current situation and environment in real time and ask questions based on that. For example, the additional information confirmation unit asks questions that take into account the current economic situation and market trends. This allows the user to receive questions based on their current situation and environment.

[0086] The additional information confirmation unit can use the emotion estimation function to analyze the emotion of the user when entering additional information and provide advice to reduce stress and anxiety. For example, when the user enters "I'm considering ways to raise funds to start a new business" and enters the additional information, the additional information confirmation unit uses the emotion estimation function to analyze the user's emotion and provide advice to reduce stress and anxiety. For example, advice to relax is provided. This allows the user to enter additional information while reducing stress and anxiety.

[0087] The additional information verification unit can add a function of receiving feedback from experts in different industries or fields in real time when verifying the additional information. For example, the additional information verification unit adds a function of receiving feedback from experts in different industries in real time when verifying the additional information, for example, if the user inputs "I am considering a method of raising funds to start a new business." For example, advice from experts in the financial industry is provided. This allows the user to receive feedback from experts in different industries or fields in real time.

[0088] The additional information confirmation unit can visualize the user's input content when the generation AI confirms the additional information, allowing for intuitive understanding. For example, if a user inputs "I am considering a method of raising funds to start a new business," and the generation AI confirms the additional information, the additional information confirmation unit can visualize the user's input content to allow for intuitive understanding. For example, the additional information confirmation unit can display the fundraising plan in graphs and charts. This allows the user to intuitively understand the additional information.

[0089] The additional information confirmation unit can use the emotion estimation function to monitor the user's emotions in real time when the user inputs additional information and provide an interface for eliciting positive emotions. For example, when the user inputs "I'm considering ways to raise funds to start a new business" and inputs the additional information, the additional information confirmation unit can use the emotion estimation function to monitor the user's emotions in real time and provide an interface for eliciting positive emotions. For example, an encouraging message can be displayed. This allows the user to input the additional information with positive emotions.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] In a decision support system, a generative AI can predict the long-term impact of a decision entered by a user and evaluate future risks and benefits. For example, if a user enters "obtaining a loan from a bank," the generative AI predicts the impact of that decision five or ten years from now and evaluates the long-term risks and benefits. This allows users to make decisions from a long-term perspective, as well as a short-term perspective. Similarly, if a user enters "considering ways to raise funds to start a new business," the generative AI predicts what market trends will affect that business in the future and evaluates the likelihood of long-term success. Furthermore, if a user enters "introduce new technology," the generative AI predicts what technological innovations and market fluctuations will affect that technology in the future and evaluates the long-term risks and benefits. This allows users to make decisions from a long-term perspective.

[0092] In a decision support system, a generative AI can evaluate the social impact of a decision entered by a user and present the social risks and benefits. For example, if a user enters "start a new business," the generative AI will evaluate the impact that business will have on the local community and the environment and present the social risks and benefits. This allows users to make decisions from a societal perspective. Similarly, if a user enters "develop a new product," the generative AI will evaluate the impact that product will have on consumers and the market and present the societal risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI will evaluate the impact that policy will have on society as a whole and present the societal risks and benefits. This allows users to make decisions from a societal perspective.

[0093] In a decision support system, a generative AI can evaluate the ethical impact of a decision entered by a user and present the ethical risks and benefits. For example, if a user enters "start a new business," the generative AI will evaluate what ethical issues the business may cause and present the ethical risks and benefits. This allows the user to make decisions from an ethical perspective. Similarly, if a user enters "introduce new technology," the generative AI will evaluate what ethical issues the technology may cause and present the ethical risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI will evaluate what ethical issues the policy may cause and present the ethical risks and benefits. This allows the user to make decisions from an ethical perspective.

[0094] In a decision support system, a generative AI can evaluate the cultural impact of a decision entered by a user and present the cultural risks and benefits. For example, if a user enters "start a new business," the generative AI evaluates the impact that business will have on local culture and traditions and presents the cultural risks and benefits. This allows users to make decisions from a cultural perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the impact that product will have on consumer culture and values ​​and presents the cultural risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the cultural impact of that policy and presents the cultural risks and benefits. This allows users to make decisions from a cultural perspective.

[0095] In a decision support system, the generative AI can evaluate the environmental impact of a decision entered by a user and present the environmental risks and benefits. For example, if a user enters "start a new business," the generative AI will evaluate the environmental impact of that business and present the environmental risks and benefits. This allows the user to make decisions from an environmental perspective. Similarly, if a user enters "develop a new product," the generative AI will evaluate the environmental impact of that product and present the environmental risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI will evaluate the environmental impact of that policy and present the environmental risks and benefits. This allows the user to make decisions from an environmental perspective.

[0096] In a decision support system, a generative AI uses emotion estimation to evaluate the impact of a decision entered by a user and provide advice based on the user's emotions. For example, if a user enters "start a new business," the generative AI evaluates the emotional impact of the decision on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the emotional impact of the product on the user and presents the emotional risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the emotional impact of the policy on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective.

[0097] In a decision support system, a generative AI uses emotion estimation to evaluate the impact of a decision entered by a user and provide recommendations based on the user's emotions. For example, if a user enters "start a new business," the generative AI evaluates the emotional impact of the decision on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the emotional impact of the product on the user and presents the emotional risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the emotional impact of the policy on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective.

[0098] In a decision support system, a generative AI uses emotion estimation to evaluate the impact of a decision entered by a user and provide feedback based on the user's emotions. For example, if a user enters "start a new business," the generative AI evaluates the emotional impact of the decision on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the emotional impact of the product on the user and presents the emotional risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the emotional impact of the policy on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective.

[0099] In a decision support system, a generative AI uses emotion estimation to evaluate the impact of a decision entered by a user and provide support based on the user's emotions. For example, if a user enters "start a new business," the generative AI evaluates the emotional impact of the decision on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the emotional impact of the product on the user and presents the emotional risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the emotional impact of the policy on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective.

[0100] In a decision support system, a generative AI uses emotion estimation to evaluate the impact of a decision entered by a user and provide an interface based on the user's emotions. For example, if a user enters "start a new business," the generative AI evaluates the emotional impact of the decision on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective. Similarly, if a user enters "develop a new product," the generative AI evaluates the emotional impact of the product on the user and presents the emotional risks and benefits. Furthermore, if a user enters "introduce a new policy," the generative AI evaluates the emotional impact of the policy on the user and presents the emotional risks and benefits. This allows the user to make decisions from an emotional perspective.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The situation input section inputs the user's situation and problems. For example, the user inputs "I am considering how to raise funds to start a new business." Step 2: The decision input unit inputs the user's decision based on the situation and problems input by the situation input unit. For example, the user inputs "Get a loan from a bank." Step 3: The outcome prediction unit predicts the outcome of the decision input by the decision input unit. For example, the generation AI predicts that "if you take out a loan from a bank, your repayment plan may become stricter." Step 4: The recommendation unit recommends alternative methods if the outcome prediction unit predicts an undesirable outcome. For example, the generation AI might recommend "using crowdfunding." Step 5: The additional information confirmation unit checks for additional information if the situation input by the situation input unit is insufficient. For example, the generation AI may ask, "Please tell me the specific amount of fundraising you are aiming for."

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 situation input section for inputting a user's situation and problems; a judgment input unit for inputting a user's judgment based on the situation and the problem input by the situation input unit; a result prediction unit that predicts the result of the judgment input by the judgment input unit; a recommendation unit that recommends an alternative method when an undesirable result is predicted by the result prediction unit; an additional information confirmation unit that confirms additional information when the situation input by the situation input unit is insufficient; A system characterized by:

2. The situation input unit Analyzes user input in real time and automatically suggests additional relevant information based on said input 2. The system of claim 1.

3. The judgment input unit When a user makes a decision, the generation AI automatically analyzes the background and rationale of that decision and provides supplementary information.

2. The system of claim 1.

4. The result prediction unit When predicting the outcome of a decision, the generative AI references market trends and news, as well as historical data.

2. The system of claim 1.

5. The recommendation unit When making the recommendation, the generative AI refers to past successes and failures to provide specific advice.

2. The system of claim 1.

6. The additional information confirmation unit When the generation AI checks for additional information, it references the user's past input history and automatically completes the necessary information.

2. The system of claim 1.

7. The situation input unit Using emotion estimation, the app analyzes the user's emotions as they type and provides advice to reduce stress and anxiety.

2. The system of claim 1.

8. The judgment input unit Using emotion estimation, we analyze the emotions users express when entering their judgments and provide advice to eliminate emotional bias.

2. The system of claim 1.

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