system

Abstract

The system according to the embodiment aims to propose quick and accurate solutions to users' questions and problems. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and an action proposal unit. The reception unit accepts a user's question or problem. The analysis unit analyzes the information accepted by the reception unit. The proposal unit proposes a solution based on the information analyzed by the analysis unit. The action proposal unit proposes specific action options based on the solution proposed by the proposal unit.

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 of making it difficult to provide quick and accurate solutions to users' questions and problems.

[0005] The system according to the embodiment aims to propose quick and accurate solutions to users' questions and problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and an action proposal unit. The reception unit accepts a user's question or problem. The analysis unit analyzes the information accepted by the reception unit. The proposal unit proposes a solution based on the information analyzed by the analysis unit. The action proposal unit proposes specific action options based on the solution proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and accurate solutions to users' questions and problems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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) An interactive suggestion system according to an embodiment of the present invention utilizes a generative AI to quickly provide essential solutions to questions and problems users face in their daily lives. In this interactive suggestion system, users input their questions or problems, and the generative AI analyzes the questions or problems and proposes essential solutions. Furthermore, if the user wants to implement the solutions, the generative AI suggests specific options for action. This system allows users to take the next step without wasting time worrying, allowing them to actively enjoy their lives. Gaining new insights also enriches the user's life. For example, a user inputs a specific question, such as "I want to know how to improve my work efficiency." This information is input into the generative AI. The generative AI then analyzes the input information and proposes essential solutions. The generative AI utilizes past data and a knowledge base to derive optimal solutions. For example, it provides specific advice such as "prioritize tasks" or "try the Pomodoro technique." Furthermore, if the user wants to implement the solutions, the generative AI suggests specific options for action. For example, it suggests specific actions such as "install a task management app" or "focus on working for 25 minutes." This allows the user to take immediate action. This tool allows users to take the next step without wasting time worrying, allowing them to actively enjoy their lives. Gaining new insights also enriches users' lives. For example, discovering a new hobby or learning efficient work methods can improve the quality of life. This allows the interactive suggestion system to quickly provide essential solutions to users' questions and problems, suggesting specific options for action, and allowing users to actively enjoy their lives.

[0029] An interactive suggestion system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and an action suggestion unit. The reception unit receives a user's questions and problems. Examples of the user's questions and problems include, but are not limited to, technical problems and questions about daily life. The reception unit, for example, receives text data input by the user. The reception unit can also receive voice input. For example, the user can input the question or problem by voice using a microphone. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes the user's questions and problems using data analysis technology. The analysis unit can also analyze the user's questions and problems using text analysis technology. For example, the analysis unit analyzes the text data using natural language processing technology to understand the user's intention. The suggestion unit proposes a solution based on the information analyzed by the analysis unit. The suggestion unit generates a solution using, for example, a generation AI. The generation AI utilizes past data and a knowledge base to derive an optimal solution. For example, the suggestion unit uses the generation AI to provide specific advice such as "prioritize tasks" or "try the Pomodoro technique." The action suggestion unit suggests specific action options based on the solutions proposed by the suggestion unit. The action suggestion unit suggests specific actions using, for example, the generation AI. The generation AI suggests specific actions so that the user can take action immediately. For example, the action suggestion unit suggests specific actions such as "install a task management app" or "concentrate on work for 25 minutes." As a result, the interactive suggestion system according to the embodiment quickly provides essential solutions to the user's questions and problems and suggests specific action options, allowing the user to actively enjoy their life. Some or all of the above-described processing in the reception unit, analysis unit, proposal unit, and action suggestion unit may be performed using, or without, the generation AI. For example, the reception unit accepts the user's questions and problems as text data, the analysis unit analyzes the text data, the proposal unit proposes solutions based on the analysis results, and the action suggestion unit proposes specific actions based on the solutions.

[0030] The reception unit can analyze the user's history of past questions and problems and select the optimal reception method. For example, the reception unit can automatically display questions and problems that the user frequently entered in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions and problems that will be used during a specific time period based on the user's history of past questions and problems. This makes it possible to provide the optimal reception method for the user by utilizing the past history, thereby enabling efficient reception of questions and problems. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input historical data of the user's past questions and problems into the generation AI and have the generation AI select the optimal reception method.

[0031] When receiving a question or problem, the reception unit can filter the question or problem based on the user's current situation and areas of interest. For example, the reception unit may preferentially receive questions or problems related to a project the user is currently working on. The reception unit can also filter and receive related questions or problems based on the user's areas of interest. Furthermore, the reception unit can also receive appropriate questions or problems depending on the user's current situation (e.g., at work, on vacation). This makes it possible to receive appropriate questions or problems depending on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's current situation data into the generation AI and have the generation AI perform filtering.

[0032] When receiving questions or problems, the reception unit can prioritize receiving questions or problems that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions or problems related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions or problems related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions or problems related to the user's home. This makes it possible to receive appropriate questions and problems based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant questions or problems.

[0033] When receiving a question or problem, the reception unit can analyze the user's social media activity and receive related questions or problems. For example, the reception unit can prioritize receiving questions or problems related to topics that the user frequently mentions on social media. The reception unit can also analyze the content of the user's social media posts and suggest related questions or problems. Furthermore, the reception unit can also receive related questions or problems based on the topics of accounts the user follows on social media. This makes it possible to receive appropriate questions or problems based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI select related questions or problems.

[0034] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question or problem. For example, the analysis unit performs a detailed analysis on questions or problems of high importance. The analysis unit can also perform a simplified analysis on questions or problems of low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance. This enables appropriate analysis according to the importance of the question or problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input importance data of the question or problem into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0035] During analysis, the analysis unit can apply different analysis algorithms depending on the category of question or problem. For example, the analysis unit can apply a specialized analysis algorithm to technical questions or problems. The analysis unit can also apply a general analysis algorithm to questions or problems related to daily life. Furthermore, the analysis unit can apply an analysis algorithm that utilizes medical data to health-related questions or problems. This enables appropriate analysis depending on the category of question or problem. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input category data of the question or problem into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0036] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the question or problem. For example, the analysis unit prioritizes analysis of recently submitted questions or problems. The analysis unit can also analyze older submitted questions or problems with normal priority. Furthermore, the analysis unit can adjust the priority of analysis depending on the time of submission. This enables appropriate analysis depending on the time of submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the question or problem into the generation AI and have the generation AI determine the priority of analysis.

[0037] During analysis, the analysis unit can adjust the order of analysis based on the relevance of questions and problems. For example, the analysis unit prioritizes analysis of highly relevant questions and problems. The analysis unit can also analyze less relevant questions and problems in the normal order. Furthermore, the analysis unit can adjust the order of analysis based on the relevance. This enables appropriate analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of questions and problems into the generation AI and have the generation AI adjust the order of analysis.

[0038] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a simplified proposal for a solution with low importance. Furthermore, the proposal unit can adjust the priority of the proposal according to the importance. This enables appropriate proposals to be made according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI, for example. For example, the proposal unit can input importance data of the solution into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0039] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution. For example, the proposal unit can apply a specialized proposal algorithm to technical solutions. The proposal unit can also apply a general proposal algorithm to solutions related to daily life. Furthermore, the proposal unit can also apply a proposal algorithm that utilizes medical data to solutions related to health. This enables appropriate proposals to be made depending on the category of the solution. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, the generation AI. For example, the proposal unit can input solution category data into the generation AI and cause the generation AI to select an appropriate proposal algorithm.

[0040] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the solution. For example, the proposal unit can prioritize the most recently submitted solution. The proposal unit can also propose older submitted solutions with normal priority. Furthermore, the proposal unit can adjust the priority of the proposal depending on the time of submission. This enables appropriate proposals to be made according to the time of submission. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the proposal unit can input solution submission time data into the generation AI and have the generation AI determine the priority of the proposals.

[0041] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the solutions. For example, the proposal unit prioritizes proposing highly relevant solutions. The proposal unit can also propose less relevant solutions in a normal order. Furthermore, the proposal unit can adjust the order of proposals based on the relevance. This enables appropriate proposals based on the relevance. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the proposal unit can input relevance data of the solutions into the generation AI and cause the generation AI to adjust the order of proposals.

[0042] When suggesting an action, the action suggestion unit can analyze the user's past action history and select the optimal action suggestion method. The action suggestion unit, for example, makes optimal action suggestions based on actions performed by the user in the past. The action suggestion unit can also select effective action suggestions from the user's past action history. Furthermore, the action suggestion unit can analyze the user's past action patterns and make the most appropriate action suggestions. This makes it possible to make optimal action suggestions for the user by utilizing the past action history. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's past action history data into the generation AI and cause the generation AI to select the optimal action suggestion method.

[0043] When suggesting an action, the action suggestion unit can customize the means of suggesting an action based on the user's current living situation. For example, if the user is at work, the action suggestion unit can suggest an action that can be performed in a short time. Furthermore, if the user is on vacation, the action suggestion unit can suggest an action that allows the user to relax. Furthermore, if the user is at home, the action suggestion unit can suggest an action that can be performed at home. This makes it possible to suggest appropriate actions according to the user's living situation. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the means of suggesting an action.

[0044] When suggesting an action, the action suggestion unit can select the optimal action suggestion method by taking into account the user's geographical location information. For example, if the user is in a specific area, the action suggestion unit can suggest actions that can be performed in that area. Furthermore, if the user is traveling, the action suggestion unit can also suggest actions that can be performed at the travel destination. Furthermore, if the user is at home, the action suggestion unit can also suggest actions that can be performed at home. This enables appropriate action suggestions based on the user's geographical location information. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal action suggestion method.

[0045] When suggesting an action, the action suggestion unit can analyze the user's social media activity and suggest a means for suggesting an action. For example, the action suggestion unit can suggest an action related to a topic that the user frequently mentions on social media. The action suggestion unit can also analyze the content of the user's social media posts and suggest related actions. Furthermore, the action suggestion unit can also suggest related actions based on the topics of accounts the user follows on social media. This makes it possible to suggest appropriate actions based on the user's social media activity. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, or without, a generation AI. For example, the action suggestion unit can input the user's social media activity data into the generation AI and have the generation AI select a means for suggesting an action.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the user's past behavior history and select the optimal reception method. For example, it can automatically display questions and problems that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions and problems that will be used during a specific time period based on the user's past history of questions and problems. This makes it possible to provide the optimal reception method for the user by utilizing the past history, thereby enabling efficient reception of questions and problems. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input historical data of the user's past questions and problems into the generation AI and have the generation AI select the optimal reception method.

[0048] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the solution. For example, a detailed proposal is made for a solution with high importance. The proposal unit can also make a simplified proposal for a solution with low importance. Furthermore, the proposal unit can adjust the priority of the proposal according to the importance. This enables appropriate proposals to be made according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the proposal unit can input importance data of the solution to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0049] When receiving questions or problems, the reception unit can prioritize receiving questions or problems that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions or problems related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions or problems related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions or problems related to the home. This makes it possible to receive appropriate questions and problems based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant questions or problems.

[0050] When suggesting an action, the action suggestion unit can analyze the user's social media activity and suggest a means for suggesting an action. For example, the action suggestion unit can suggest an action related to a topic that the user frequently mentions on social media. The action suggestion unit can also analyze the content of the user's social media posts and suggest related actions. Furthermore, the action suggestion unit can suggest related actions based on the topics of accounts the user follows on social media. This enables appropriate action suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, or without, a generation AI. For example, the action suggestion unit can input the user's social media activity data into the generation AI and have the generation AI select a means for suggesting an action.

[0051] During analysis, the analysis unit can apply different analysis algorithms depending on the category of question or problem. For example, a specialized analysis algorithm can be applied to technical questions or problems. The analysis unit can also apply a general analysis algorithm to questions or problems related to daily life. Furthermore, the analysis unit can apply an analysis algorithm that utilizes medical data to questions or problems related to health. This enables appropriate analysis depending on the category of question or problem. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input category data of the question or problem into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0052] When suggesting an action, the action suggestion unit can customize the means of suggesting an action based on the user's current living situation. For example, if the user is at work, the action suggestion unit can suggest an action that can be performed in a short time. Furthermore, if the user is on vacation, the action suggestion unit can suggest an action that allows the user to relax. Furthermore, if the user is at home, the action suggestion unit can suggest an action that can be performed at home. This makes it possible to suggest appropriate actions according to the user's living situation. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's current living situation data into the generation AI and have the generation AI customize the means of suggesting an action.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception unit receives questions and problems from users. The questions and problems from users include technical problems and problems related to daily life. The reception unit can receive text data and voice input from users. For example, users can use a microphone to input their questions and problems by voice. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses data analysis technology and text analysis technology to analyze the user's questions and problems. For example, it uses natural language processing technology to analyze text data and understand the user's intentions. Step 3: The proposal unit proposes solutions based on the information analyzed by the analysis unit. The proposal unit uses generative AI to generate solutions and derives the optimal solution by utilizing past data and knowledge bases. For example, it provides specific advice such as "prioritize tasks" or "try the Pomodoro technique." Step 4: The action suggestion unit proposes specific options for action based on the solutions proposed by the proposal unit. The action suggestion unit uses generative AI to suggest specific actions that the user can take immediately. For example, it suggests specific actions such as "install a task management app" or "work concentratively for 25 minutes."

[0055] (Example 2) An interactive suggestion system according to an embodiment of the present invention utilizes a generative AI to quickly provide essential solutions to questions and problems users face in their daily lives. In this interactive suggestion system, users input their questions or problems, and the generative AI analyzes the questions or problems and proposes essential solutions. Furthermore, if the user wants to implement the solutions, the generative AI suggests specific options for action. This system allows users to take the next step without wasting time worrying, allowing them to actively enjoy their lives. Gaining new insights also enriches the user's life. For example, a user inputs a specific question, such as "I want to know how to improve my work efficiency." This information is input into the generative AI. The generative AI then analyzes the input information and proposes essential solutions. The generative AI utilizes past data and a knowledge base to derive optimal solutions. For example, it provides specific advice such as "prioritize tasks" or "try the Pomodoro technique." Furthermore, if the user wants to implement the solutions, the generative AI suggests specific options for action. For example, it suggests specific actions such as "install a task management app" or "focus on working for 25 minutes." This allows the user to take immediate action. This tool allows users to take the next step without wasting time worrying, allowing them to actively enjoy their lives. Gaining new insights also enriches users' lives. For example, discovering a new hobby or learning efficient work methods can improve the quality of life. This allows the interactive suggestion system to quickly provide essential solutions to users' questions and problems, suggesting specific options for action, and allowing users to actively enjoy their lives.

[0056] An interactive suggestion system according to an embodiment includes a reception unit, an analysis unit, a suggestion unit, and an action suggestion unit. The reception unit receives a user's questions and problems. Examples of the user's questions and problems include, but are not limited to, technical problems and questions about daily life. The reception unit, for example, receives text data input by the user. The reception unit can also receive voice input. For example, the user can input the question or problem by voice using a microphone. The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, analyzes the user's questions and problems using data analysis technology. The analysis unit can also analyze the user's questions and problems using text analysis technology. For example, the analysis unit analyzes the text data using natural language processing technology to understand the user's intention. The suggestion unit proposes a solution based on the information analyzed by the analysis unit. The suggestion unit generates a solution using, for example, a generation AI. The generation AI utilizes past data and a knowledge base to derive an optimal solution. For example, the suggestion unit uses the generation AI to provide specific advice such as "prioritize tasks" or "try the Pomodoro technique." The action suggestion unit suggests specific action options based on the solutions proposed by the suggestion unit. The action suggestion unit suggests specific actions using, for example, the generation AI. The generation AI suggests specific actions so that the user can take action immediately. For example, the action suggestion unit suggests specific actions such as "install a task management app" or "concentrate on work for 25 minutes." As a result, the interactive suggestion system according to the embodiment quickly provides essential solutions to the user's questions and problems and suggests specific action options, allowing the user to actively enjoy their life. Some or all of the above-described processing in the reception unit, analysis unit, proposal unit, and action suggestion unit may be performed using, or without, the generation AI. For example, the reception unit accepts the user's questions and problems as text data, the analysis unit analyzes the text data, the proposal unit proposes solutions based on the analysis results, and the action suggestion unit proposes specific actions based on the solutions.

[0057] The reception unit can estimate the user's emotions and adjust the method for accepting questions and problems based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of questions and problems. This provides an appropriate reception method according to the user's emotions, reducing user stress and enabling efficient reception of questions and problems. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0058] The reception unit can analyze the user's history of past questions and problems and select the optimal reception method. For example, the reception unit can automatically display questions and problems that the user frequently entered in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions and problems that will be used during a specific time period based on the user's history of past questions and problems. This makes it possible to provide the optimal reception method for the user by utilizing the past history, thereby enabling efficient reception of questions and problems. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the reception unit can input historical data of the user's past questions and problems into the generation AI and have the generation AI select the optimal reception method.

[0059] When receiving a question or problem, the reception unit can filter the question or problem based on the user's current situation and areas of interest. For example, the reception unit may preferentially receive questions or problems related to a project the user is currently working on. The reception unit can also filter and receive related questions or problems based on the user's areas of interest. Furthermore, the reception unit can also receive appropriate questions or problems depending on the user's current situation (e.g., at work, on vacation). This makes it possible to receive appropriate questions or problems depending on the user's situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's current situation data into the generation AI and have the generation AI perform filtering.

[0060] The reception unit can estimate the user's emotions and determine the priority of questions and problems to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize urgent questions and problems. Furthermore, when the user is relaxed, the reception unit can also prioritize questions and problems that require a quick solution when the user is in a hurry. This allows for prompt and appropriate responses by receiving questions and problems in a priority order based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0061] When receiving questions or problems, the reception unit can prioritize receiving questions or problems that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions or problems related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions or problems related to the user's travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions or problems related to the user's home. This makes it possible to receive appropriate questions and problems based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant questions or problems.

[0062] When receiving a question or problem, the reception unit can analyze the user's social media activity and receive related questions or problems. For example, the reception unit can prioritize receiving questions or problems related to topics that the user frequently mentions on social media. The reception unit can also analyze the content of the user's social media posts and suggest related questions or problems. Furthermore, the reception unit can also receive related questions or problems based on the topics of accounts the user follows on social media. This makes it possible to receive appropriate questions or problems based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI select related questions or problems.

[0063] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can select a simple and quick analysis method. Furthermore, if the user is relaxed, the analysis unit can also select an analysis method that provides quick results if the user is in a hurry. This enables fast and accurate analysis by providing an appropriate analysis method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question or problem. For example, the analysis unit performs a detailed analysis on questions or problems of high importance. The analysis unit can also perform a simplified analysis on questions or problems of low importance. Furthermore, the analysis unit can adjust the priority of the analysis according to the importance. This enables appropriate analysis according to the importance of the question or problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input importance data of the question or problem into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the category of question or problem. For example, the analysis unit can apply a specialized analysis algorithm to technical questions or problems. The analysis unit can also apply a general analysis algorithm to questions or problems related to daily life. Furthermore, the analysis unit can apply an analysis algorithm that utilizes medical data to health-related questions or problems. This enables appropriate analysis depending on the category of question or problem. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input category data of the question or problem into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0066] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing questions and problems with high urgency. Furthermore, if the user is relaxed, the analysis unit can also analyze questions and problems with normal priority. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing questions and problems that require a quick solution. This enables prompt and appropriate responses by analyzing questions and problems with priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0067] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the question or problem. For example, the analysis unit prioritizes analysis of recently submitted questions or problems. The analysis unit can also analyze older submitted questions or problems with normal priority. Furthermore, the analysis unit can adjust the priority of analysis depending on the time of submission. This enables appropriate analysis depending on the time of submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time of submission of the question or problem into the generation AI and have the generation AI determine the priority of analysis.

[0068] During analysis, the analysis unit can adjust the order of analysis based on the relevance of questions and problems. For example, the analysis unit prioritizes analysis of highly relevant questions and problems. The analysis unit can also analyze less relevant questions and problems in the normal order. Furthermore, the analysis unit can adjust the order of analysis based on the relevance. This enables appropriate analysis based on the relevance. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input relevance data of questions and problems into the generation AI and have the generation AI adjust the order of analysis.

[0069] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can select a simple and easy-to-understand way of expression. Furthermore, if the user is relaxed, the suggestion unit can select a way of expression that includes detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can select a way of expression that can be quickly understood. This enables prompt and accurate suggestions by providing an appropriate way of expression for the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0070] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. For example, the proposal unit makes a detailed proposal for a solution with high importance. The proposal unit can also make a simplified proposal for a solution with low importance. Furthermore, the proposal unit can adjust the priority of the proposal according to the importance. This enables appropriate proposals to be made according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, or without, the generation AI, for example. For example, the proposal unit can input importance data of the solution into the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0071] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution. For example, the proposal unit can apply a specialized proposal algorithm to technical solutions. The proposal unit can also apply a general proposal algorithm to solutions related to daily life. Furthermore, the proposal unit can also apply a proposal algorithm that utilizes medical data to solutions related to health. This enables appropriate proposals to be made depending on the category of the solution. Some or all of the above-mentioned processing in the proposal unit can be performed using, or without, the generation AI. For example, the proposal unit can input solution category data into the generation AI and cause the generation AI to select an appropriate proposal algorithm.

[0072] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can also provide short suggestions that can be quickly understood. This enables prompt and accurate suggestions by providing appropriate suggestion lengths according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the solution. For example, the proposal unit can prioritize the most recently submitted solution. The proposal unit can also propose older submitted solutions with normal priority. Furthermore, the proposal unit can adjust the priority of the proposal depending on the time of submission. This enables appropriate proposals to be made according to the time of submission. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the proposal unit can input solution submission time data into the generation AI and have the generation AI determine the priority of the proposals.

[0074] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the solutions. For example, the proposal unit prioritizes proposing highly relevant solutions. The proposal unit can also propose less relevant solutions in a normal order. Furthermore, the proposal unit can adjust the order of proposals based on the relevance. This enables appropriate proposals based on the relevance. Some or all of the above-described processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the proposal unit can input relevance data of the solutions into the generation AI and cause the generation AI to adjust the order of proposals.

[0075] The action suggestion unit can estimate the user's emotions and adjust the method of action suggestion based on the estimated user emotions. For example, if the user is feeling stressed, the action suggestion unit can suggest simple and easy-to-execute actions. Furthermore, if the user is relaxed, the action suggestion unit can also suggest detailed actions. Furthermore, if the user is in a hurry, the action suggestion unit can also suggest actions that can be quickly executed. This enables prompt and accurate action suggestion by providing an appropriate action suggestion method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the action suggestion unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0076] When suggesting an action, the action suggestion unit can analyze the user's past action history and select the optimal action suggestion method. The action suggestion unit, for example, makes optimal action suggestions based on actions performed by the user in the past. The action suggestion unit can also select effective action suggestions from the user's past action history. Furthermore, the action suggestion unit can analyze the user's past action patterns and make the most appropriate action suggestions. This makes it possible to make optimal action suggestions for the user by utilizing the past action history. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's past action history data into the generation AI and cause the generation AI to select the optimal action suggestion method.

[0077] When suggesting an action, the action suggestion unit can customize the means of suggesting an action based on the user's current living situation. For example, if the user is at work, the action suggestion unit can suggest an action that can be performed in a short time. Furthermore, if the user is on vacation, the action suggestion unit can suggest an action that allows the user to relax. Furthermore, if the user is at home, the action suggestion unit can suggest an action that can be performed at home. This makes it possible to suggest appropriate actions according to the user's living situation. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's current living situation data into the generation AI and cause the generation AI to customize the means of suggesting an action.

[0078] The action suggestion unit can estimate the user's emotions and prioritize action suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the action suggestion unit can prioritize action suggestions with a high degree of urgency. Furthermore, if the user is relaxed, the action suggestion unit can also prioritize action suggestions that require immediate execution if the user is in a hurry. This allows for prompt and appropriate responses by prioritizing action suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, the generation AI. For example, the action suggestion unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] When suggesting an action, the action suggestion unit can select the optimal action suggestion method by taking into account the user's geographical location information. For example, if the user is in a specific area, the action suggestion unit can suggest actions that can be performed in that area. Furthermore, if the user is traveling, the action suggestion unit can also suggest actions that can be performed at the travel destination. Furthermore, if the user is at home, the action suggestion unit can also suggest actions that can be performed at home. This enables appropriate action suggestions based on the user's geographical location information. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the optimal action suggestion method.

[0080] When suggesting an action, the action suggestion unit can analyze the user's social media activity and suggest a means for suggesting an action. For example, the action suggestion unit can suggest an action related to a topic that the user frequently mentions on social media. The action suggestion unit can also analyze the content of the user's social media posts and suggest related actions. Furthermore, the action suggestion unit can also suggest related actions based on the topics of accounts the user follows on social media. This makes it possible to suggest appropriate actions based on the user's social media activity. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, or without, a generation AI. For example, the action suggestion unit can input the user's social media activity data into the generation AI and have the generation AI select a means for suggesting an action. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and action suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and accepts the user's questions and problems as text data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the text data to understand the user's intention. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates solutions using a generation AI. The action suggestion unit is realized by the control unit 46A of the smart device 14 and suggests specific action options. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and action suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts the user's questions and problems as text data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the text data to understand the user's intention. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates solutions using a generation AI. The action suggestion unit is realized by the control unit 46A of the smart glasses 214 and suggests specific action options. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, suggestion unit, and action suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and accepts the user's questions and problems as text data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the text data to understand the user's intention. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates solutions using a generation AI. The action suggestion unit is realized by the control unit 46A of the headset-type terminal 314 and suggests specific action options. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, proposal unit, and action proposal unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts the user's questions and problems as text data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the text data to understand the user's intention. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and generates solutions using a generative AI. The action proposal unit is realized by the control unit 46A of the robot 414 and proposes specific action options.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The reception unit can analyze the user's past behavior history and select the optimal reception method. For example, it can automatically display questions and problems that the user has frequently input in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest questions and problems that will be used during a specific time period based on the user's past history of questions and problems. This makes it possible to provide the optimal reception method for the user by utilizing the past history, thereby enabling efficient reception of questions and problems. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the reception unit can input historical data of the user's past questions and problems into the generation AI and have the generation AI select the optimal reception method.

[0083] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can select a simple and quick analysis method. If the user is relaxed, the analysis unit can also select a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can select an analysis method that provides quick results. This enables fast and accurate analysis by providing an appropriate analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0084] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the solution. For example, a detailed proposal is made for a solution with high importance. The proposal unit can also make a simplified proposal for a solution with low importance. Furthermore, the proposal unit can adjust the priority of the proposal according to the importance. This enables appropriate proposals to be made according to the importance of the solution. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the proposal unit can input importance data of the solution to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0085] The action suggestion unit can estimate the user's emotions and adjust the method of action suggestion based on the estimated user's emotions. For example, if the user is feeling stressed, the action suggestion unit can suggest simple and easy-to-execute actions. Furthermore, if the user is relaxed, the action suggestion unit can suggest detailed actions. Furthermore, if the user is in a hurry, the action suggestion unit can suggest actions that can be quickly executed. This enables prompt and accurate action suggestion by providing an appropriate action suggestion method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the action suggestion unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the action suggestion unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0086] When receiving questions or problems, the reception unit can prioritize receiving questions or problems that are highly relevant based on the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving questions or problems related to that area. Furthermore, if the user is traveling, the reception unit can prioritize receiving questions or problems related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving questions or problems related to the home. This makes it possible to receive appropriate questions and problems based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information data into the generation AI and have the generation AI select highly relevant questions or problems.

[0087] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can select a simple and easy-to-understand way of expression. Furthermore, if the user is relaxed, the suggestion unit can select a way of expression that includes detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can select a way of expression that can be quickly understood. This enables prompt and accurate suggestions by providing an appropriate way of expression for the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI, or without the generation AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0088] When suggesting an action, the action suggestion unit can analyze the user's social media activity and suggest a means for suggesting an action. For example, the action suggestion unit can suggest an action related to a topic that the user frequently mentions on social media. The action suggestion unit can also analyze the content of the user's social media posts and suggest related actions. Furthermore, the action suggestion unit can suggest related actions based on the topics of accounts the user follows on social media. This enables appropriate action suggestions based on the user's social media activity. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, or without, a generation AI. For example, the action suggestion unit can input the user's social media activity data into the generation AI and have the generation AI select a means for suggesting an action.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of question or problem. For example, a specialized analysis algorithm can be applied to technical questions or problems. The analysis unit can also apply a general analysis algorithm to questions or problems related to daily life. Furthermore, the analysis unit can apply an analysis algorithm that utilizes medical data to questions or problems related to health. This enables appropriate analysis depending on the category of question or problem. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input category data of the question or problem into the generation AI and have the generation AI select an appropriate analysis algorithm.

[0090] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly understood. This enables prompt and accurate suggestions by providing appropriate suggestion lengths according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, the generation AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0091] When suggesting an action, the action suggestion unit can customize the means of suggesting an action based on the user's current living situation. For example, if the user is at work, the action suggestion unit can suggest an action that can be performed in a short time. Furthermore, if the user is on vacation, the action suggestion unit can suggest an action that allows the user to relax. Furthermore, if the user is at home, the action suggestion unit can suggest an action that can be performed at home. This makes it possible to suggest appropriate actions according to the user's living situation. Some or all of the above-mentioned processing in the action suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the action suggestion unit can input the user's current living situation data into the generation AI and have the generation AI customize the means of suggesting an action.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives questions and problems from users. The questions and problems from users include technical problems and problems related to daily life. The reception unit can receive text data and voice input from users. For example, users can use a microphone to input their questions and problems by voice. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit uses data analysis technology and text analysis technology to analyze the user's questions and problems. For example, it uses natural language processing technology to analyze text data and understand the user's intentions. Step 3: The proposal unit proposes solutions based on the information analyzed by the analysis unit. The proposal unit uses generative AI to generate solutions and derives the optimal solution by utilizing past data and knowledge bases. For example, it provides specific advice such as "prioritize tasks" or "try the Pomodoro technique." Step 4: The action suggestion unit proposes specific options for action based on the solutions proposed by the proposal unit. The action suggestion unit uses generative AI to suggest specific actions that the user can take immediately. For example, it suggests specific actions such as "install a task management app" or "work concentratively for 25 minutes."

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

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

[0101] The 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.

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

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

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

[0105] Fig. 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.

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

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0110] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 reception unit that receives questions or problems from users; an analysis unit that analyzes the information received by the reception unit; a proposal unit that proposes a solution based on the information analyzed by the analysis unit; an action suggestion unit that suggests specific options for action based on the solution proposed by the suggestion unit; A system characterized by:

2. The reception unit Estimate user emotions and adjust how questions and problems are handled based on the estimated user emotions 2. The system of claim 1.

3. The reception unit Analyze the user's past questions and problems and select the appropriate reception method 2. The system of claim 1.

4. The reception unit Filter questions and issues based on your current situation or area of ​​interest 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the questions and problems to be accepted based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Prioritize relevant questions and issues based on your geographic location when submitting questions or issues 2. The system of claim 1.

7. The reception unit When receiving a question or problem, analyze the user's social media activity and receive related questions or problems.

2. The system of claim 1.

8. The analysis unit Estimate the user's emotions and adjust the analysis method based on the estimated user emotions.

2. The system of claim 1.

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