system

Abstract

The system according to the embodiment aims to analyze the user's emotions and concerns and provide support through dialogue. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a dialogue unit. The reception unit receives user input. The analysis unit analyzes the input received by the reception unit. The provision unit provides the analysis results obtained by the analysis unit. The dialogue unit engages in dialogue based on the analysis results provided by the provision 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 not being able to adequately analyze the user's emotions and concerns and provide sufficient support through dialogue.

[0005] The system according to the embodiment aims to analyze the user's emotions and concerns and provide support through dialogue. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a dialogue unit. The reception unit receives a user input. The analysis unit analyzes the input received by the reception unit. The provision unit provides the analysis result obtained by the analysis unit. The dialogue unit conducts a dialogue based on the analysis result provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the user's emotions and concerns and provide support through dialogue. [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) In an embodiment of the present invention, a self-management system allows a user to record things that made them happy, things that made them unhappy, worries, etc., either by voice or manual input. A generation AI analyzes these inputs and provides specific suggestions for when these feelings occur and, if they are worries, how to deal with them. The self-management system allows a user to record things that made them happy, things that made them unhappy, worries, etc., either by voice or manual input. The generation AI analyzes these inputs and provides specific suggestions for when these feelings occur and, if they are worries, how to deal with them. The self-management system also allows for dialogue using the multimodal function of the generation AI if a conversation partner is needed. For example, the self-management system allows a user to record things that made them happy, things that made them unhappy, worries, etc., either by voice or manual input. For example, a user might input content such as "I had a great time with my friends today" or "I'm feeling down because my boss scolded me at work." The self-management system then uses the generation AI to analyze the user's input content and identify when these feelings occur. The input to the generation AI is the user's input content itself, and the generation AI performs analysis based on that content. For example, the generative AI might identify patterns such as "I enjoy spending time with friends" or "I hate work stress." The self-management system then uses the generative AI's analysis results to suggest specific ways to address the problem. For example, it might provide advice such as, "To reduce work stress, it's important to take time to relax." The self-management system then uses the generative AI's multimodal capabilities to engage in dialogue. By interacting with the generative AI, users can organize their feelings and calmly analyze themselves. This allows the self-management system to help users organize their feelings and prevent illness from mental distress. The self-management system also allows users to calmly analyze themselves and organize their feelings. For example, by using the app daily, users can regularly reflect on their feelings and reduce stress. Furthermore, they can share their feelings and reset themselves through dialogue with the generative AI. This allows users to live healthier lives.

[0029] The self-management system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a dialogue unit. The reception unit receives user input. The user input includes, but is not limited to, voice input, text input, and image input. For example, the reception unit receives voice input using a microphone. The reception unit can also receive text input using a keyboard. The reception unit can also receive image input using a camera. For example, the reception unit records voice input using a microphone and converts it into text data using voice recognition technology. The reception unit receives text input as text data entered using a keyboard. The reception unit receives image input as image data captured by a camera. The analysis unit analyzes the input received by the reception unit using a generation AI. The analysis can be performed using, for example, natural language processing, sentiment analysis, data mining, or other methods, but is not limited to these examples. For example, the generation AI analyzes the input content using a text generation AI (e.g., LLM). The analysis unit can also analyze input content including voice and images using a multimodal generation AI. The analysis unit can also use a generation AI to analyze the emotions of the input content. For example, a text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The generation AI uses emotion analysis technology to analyze the emotions of the input content. The provision unit provides the analysis results obtained by the analysis unit. The provision can be performed, for example, by displaying text, outputting audio, or notifying users, but is not limited to these examples. For example, the provision unit can display the analysis results as text. The provision unit can also output the analysis results as audio. The provision unit can also transmit the analysis results as a notification. For example, the provision unit can display the analysis results as text on a screen. The provision unit can output the analysis results as audio from a speaker. The provision unit can also transmit the analysis results as a notification to a smartphone. The dialogue unit uses the generation AI to engage in dialogue based on the analysis results provided by the provision unit.The dialogue may be conducted using, for example, a chatbot, a voice assistant, an interactive UI, or other methods, but is not limited to these examples. For example, the generation AI may conduct the dialogue using a text generation AI (e.g., LLM). The dialogue unit may also conduct a dialogue including audio and images using a multimodal generation AI. The dialogue unit may also conduct a dialogue based on the user's emotions using the generation AI. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI conducts a dialogue based on the user's emotions using emotion analysis technology. This allows the self-management system according to the embodiment to efficiently analyze the user's input and provide appropriate countermeasures and dialogue. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit may conduct a dialogue using an AI model that receives the dialogue content generated by the generation AI as input and outputs a dialogue.

[0030] The reception unit can record the user's happy or unhappy experiences and worries by voice or manual input. Examples of happy or unhappy experiences and worries include, but are not limited to, events in daily life, worries at work, and interpersonal problems. The reception unit can, for example, allow the user to input voice using a microphone. The reception unit can also allow the user to input text using a keyboard. The reception unit can also allow the user to input images using a camera. For example, the reception unit can allow the user to input content such as "I had a great time with my friends today" or "I'm depressed because my boss scolded me at work." This allows the user to freely express their feelings. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can record voice input using a microphone and convert it into text data using voice recognition technology.

[0031] The analysis unit can analyze the user's input content and identify when the user feels a certain emotion. Examples of when the user feels a certain emotion include, but are not limited to, a specific event, a time period, or a location. The analysis unit can analyze the user's input content using, for example, a generation AI. For example, the generation AI can analyze the input content using a text generation AI (e.g., LLM). The analysis unit can also analyze input content including audio and images using a multimodal generation AI. The analysis unit can also analyze the emotions in the input content using the generation AI. For example, the generation AI can find patterns such as "I enjoy spending time with friends" and "I hate stress at work." This can identify the user's emotional pattern. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's input content to the generation AI and have the generation AI identify the emotional pattern.

[0032] The providing unit can suggest specific solutions to the worries based on the analysis results. Solutions to the worries include, but are not limited to, stress relief methods, problem-solving procedures, and expert advice. The providing unit can suggest specific solutions to the user based on the analysis results. For example, the providing unit can provide advice such as, "To reduce work stress, it is important to take time to relax." The providing unit can also provide expert advice to the user based on the analysis results. For example, the providing unit can suggest, "To receive expert advice, it is effective to receive counseling." This allows the user to be provided with specific solutions. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the analysis results to a generating AI and have the generating AI execute the solution suggestions.

[0033] The dialogue unit can dialogue with the user to help them sort out or reset their feelings. Examples of ways to sort out or reset their feelings include, but are not limited to, relaxation techniques, mental health care, and counseling. The dialogue unit can dialogue with the user using, for example, a generation AI. For example, the generation AI can dialogue using a text generation AI (e.g., LLM). The dialogue unit can also use a multimodal generation AI to conduct dialogue including audio and images. The dialogue unit can also use the generation AI to conduct dialogue based on the user's emotions. For example, the generation AI can suggest relaxation techniques based on the user's emotions. The dialogue unit can also use the generation AI to suggest mental health care methods based on the user's emotions. The dialogue unit can also use the generation AI to suggest counseling based on the user's emotions. This allows the user to sort out and reset their feelings. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can conduct dialogue using an AI model that receives dialogue content generated by the generation AI as input and outputs a dialogue.

[0034] The reception unit can analyze the user's past input history and select the optimal reception method. Methods for analyzing the past input history include, but are not limited to, data mining, statistical analysis, and machine learning. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period based on the user's past input history. The reception unit can also analyze the user's past input content and suggest the most efficient reception method. This makes it possible to provide the optimal reception method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input past input history data into a generation AI and have the generation AI select the optimal reception method.

[0035] When receiving input, the reception unit can filter the input based on the user's current living situation and areas of interest. Examples of the current living situation and areas of interest include, but are not limited to, survey results and social media activity history. For example, when the user is at work, the reception unit can prioritize receiving work-related input content. Furthermore, when the user is on vacation, the reception unit can prioritize receiving input content related to relaxation and hobbies. Furthermore, when the user is participating in a specific event, the reception unit can prioritize receiving input content related to the event. This allows input according to the user's living situation and areas of interest to be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0036] When receiving input, the reception unit can select the optimal reception means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit can receive the input using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially receive keyboard input. Furthermore, if the user selects image input, the reception unit can also receive input using image analysis technology. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal reception means.

[0037] When receiving input, the reception unit can prioritize receiving highly relevant input based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific location, the reception unit can prioritize receiving input related to the location. Furthermore, when the user is traveling, the reception unit can prioritize receiving input related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving input related to the home. This allows highly relevant input based on the user's geographical location information to be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize receiving highly relevant input.

[0038] The reception unit may analyze the user's social media activity when receiving input and receive related input. Social media activity includes, but is not limited to, for example, post content, like history, and follower information. The reception unit may, for example, preferentially receive related input content based on content shared by the user on social media. The reception unit may also analyze the user's social media posts and preferentially receive related input content. The reception unit may also preferentially receive related input content based on the activities of the user's friends on social media. This allows reception of related input based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media activity data to the generation AI and cause the generation AI to receive related input.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method from the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the most efficient reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the reception method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. The importance of the input content includes, but is not limited to, user ratings, urgency, and relevance. For example, the analysis unit performs a detailed analysis of input content with high importance. The analysis unit can also perform a concise analysis of input content with low importance. The analysis unit can also perform an analysis with an appropriate level of detail of input content with medium importance. This makes it possible to provide an optimal analysis result according to the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. Examples of categories of input content include, but are not limited to, emotion categories, topic categories, and time zone categories. For example, the analysis unit applies an emotion analysis algorithm to input content related to emotions. The analysis unit can also apply a stress analysis algorithm to input content related to stress. The analysis unit can also apply a worry analysis algorithm to input content related to worries. This makes it possible to provide optimal analysis results according to the category of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input content to the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, past databases and user feedback. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also find specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to provide optimal analysis results based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input content. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit, for example, prioritizes analysis of recently submitted input content. The analysis unit can also postpone input content that was submitted recently. The analysis unit can also appropriately analyze input content that was submitted recently. This makes it possible to provide optimal analysis results according to the submission time of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data to the generation AI and have the generation AI determine the analysis priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input content. The relevance of the input content includes, but is not limited to, topic relevance, temporal relevance, etc. For example, the analysis unit prioritizes analysis of highly relevant input content. The analysis unit can also postpone analysis of less relevant input content. The analysis unit can also appropriately analyze input content with medium relevance. This makes it possible to provide optimal analysis results according to the relevance of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input content to the generation AI and have the generation AI adjust the analysis order.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis based on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past input content. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also provide analysis results that use appropriate technical terminology depending on the user's level of expertise. This allows for optimal analysis results to be provided according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0046] The providing unit can adjust the level of detail of the analysis results provided based on the importance of the analysis results when providing the analysis results. The importance of the analysis results includes, but is not limited to, user ratings, urgency, and relevance. For example, the providing unit can provide detailed information for analysis results with high importance. The providing unit can also provide concise information for analysis results with low importance. The providing unit can also provide information with an appropriate level of detail for analysis results with medium importance. This makes it possible to provide optimal information according to the importance of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input importance data of the analysis results to the generating AI and cause the generating AI to adjust the level of detail of the analysis results provided.

[0047] The providing unit can apply different providing algorithms depending on the category of the analysis result when providing the analysis result. The categories of the analysis result include, but are not limited to, emotion categories, topic categories, and time zone categories, for example. The providing unit can apply an emotion analysis algorithm to analysis results related to emotions. The providing unit can also apply a stress analysis algorithm to analysis results related to stress. The providing unit can also apply a worry analysis algorithm to analysis results related to worries. This makes it possible to provide optimal information according to the category of the analysis result. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input category data of the analysis result to the generation AI and cause the generation AI to apply different providing algorithms.

[0048] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results. Past provision results include, but are not limited to, past databases and user feedback. The providing unit, for example, corrects the current information provided by the user based on the user's past provision results. The providing unit can also find specific patterns from the user's past provision results and reflect them in the current information provided by the user. The providing unit can also analyze the user's past provision results and optimize the provision algorithm. This allows optimal information to be provided based on the user's past provision results. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided by the user.

[0049] At the time of providing the analysis results, the providing unit can determine the priority of provision based on the submission time of the analysis results. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The providing unit, for example, prioritizes providing the most recently submitted analysis results. The providing unit can also postpone analysis results that were submitted a long time ago. The providing unit can also appropriately provide analysis results that were submitted a medium time ago. This makes it possible to provide optimal information according to the submission time of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input submission time data into the generation AI and have the generation AI determine the provision priority.

[0050] The providing unit can adjust the order of provision based on the relevance of the analysis results when providing them. The relevance of the analysis results includes, but is not limited to, for example, topic relevance and temporal relevance. For example, the providing unit can prioritize providing highly relevant analysis results. The providing unit can also postpone analysis results with low relevance. The providing unit can also moderately provide analysis results with medium relevance. This makes it possible to provide optimal information according to the relevance of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0051] The providing unit can adjust the use of technical terminology in the provided information depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past input content. For example, if the user has technical expertise, the providing unit can provide information using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information in simple language. Furthermore, the providing unit can provide information using appropriate technical terminology depending on the user's level of expertise. This allows optimal information to be provided depending on the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0052] During a dialogue, the dialogue unit can select an optimal dialogue method based on the user's past dialogue history. Examples of past dialogue history include, but are not limited to, past chat logs and voice recordings. The dialogue unit selects the optimal dialogue method based on, for example, the user's preferred dialogue style in the past. The dialogue unit can also prioritize a specific topic from the user's past dialogue history. The dialogue unit can also analyze the user's past dialogue history and suggest the most effective dialogue method. This makes it possible to provide an optimal dialogue method based on the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past dialogue history data into a generation AI and have the generation AI select an optimal dialogue method.

[0053] During a dialogue, the dialogue unit can customize dialogue content based on the user's current living situation and areas of interest. Examples of the current living situation and areas of interest include, but are not limited to, survey results and social media activity history. For example, if the user is at work, the dialogue unit can prioritize providing work-related dialogue content. Furthermore, if the user is on vacation, the dialogue unit can prioritize providing relaxation and hobbies-related dialogue content. Furthermore, if the user is participating in a specific event, the dialogue unit can prioritize providing event-related dialogue content. This makes it possible to provide optimal dialogue content according to the user's living situation and areas of interest. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to customize the dialogue content.

[0054] The dialogue unit can improve the dialogue method by reflecting user feedback during dialogue. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. For example, the dialogue unit can suggest an optimal dialogue method based on feedback previously provided by the user. The dialogue unit can also preferentially select a specific dialogue method based on the user's past feedback. The dialogue unit can also analyze the user's past feedback and optimize the dialogue algorithm. This makes it possible to provide an optimal dialogue method based on the user's feedback. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input feedback data into a generation AI and cause the generation AI to improve the dialogue method.

[0055] During a dialogue, the dialogue unit can select an optimal dialogue method based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is in a specific location, the dialogue unit can prioritize providing dialogue content related to that location. Furthermore, when the user is traveling, the dialogue unit can prioritize providing dialogue content related to the user's travel destination. Furthermore, when the user is at home, the dialogue unit can prioritize providing dialogue content related to the user's home. This allows optimal dialogue content to be provided based on the user's geographical location information. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the user's geographical location information data into a generation AI and cause the generation AI to select an optimal dialogue method.

[0056] During a dialogue, the dialogue unit can analyze the user's social media activity and suggest dialogue content. Social media activity includes, but is not limited to, for example, posted content, like history, and follower information. For example, the dialogue unit can prioritize providing related dialogue content based on content shared by the user on social media. The dialogue unit can also analyze the user's social media posts and prioritize providing related dialogue content. The dialogue unit can also refer to the activities of the user's friends on social media and prioritize providing related dialogue content. This makes it possible to provide optimal dialogue content based on the user's social media activity. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest dialogue content.

[0057] The dialogue unit can customize the dialogue method by reflecting the user's past feedback during dialogue. Examples of the feedback include, but are not limited to, user ratings, comments, and survey results. The dialogue unit can, for example, suggest an optimal dialogue method based on the user's past feedback. The dialogue unit can also preferentially select a specific dialogue method based on the user's past feedback. The dialogue unit can also analyze the user's past feedback and optimize the dialogue algorithm. This makes it possible to provide an optimal dialogue method based on the user's past feedback. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input feedback data into a generation AI and have the generation AI customize the dialogue method.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can estimate the user's health condition based on the user's input content and adjust the input reception method based on the estimated health condition. For example, if the user is tired, simple voice input can be preferentially received. If the user is healthy, detailed text input can be received. If the user is ill, input can be temporarily stopped and the user can be encouraged to rest. This makes it possible to provide an optimal input reception method according to the user's health condition.

[0060] The analysis unit can estimate the user's hobbies and interests based on the user's input, and customize the analysis results based on the estimated hobbies and interests. For example, if the user is interested in sports, analysis results related to sports can be provided preferentially. Also, if the user is interested in music, analysis results related to music can be provided. Also, if the user is interested in travel, analysis results related to travel can be provided. This makes it possible to provide optimal analysis results according to the user's hobbies and interests.

[0061] The providing unit can estimate the user's learning style based on the user's input, and adjust the information providing method based on the estimated learning style. For example, if the user has a visual learning style, the information can be provided using visually easy-to-understand graphs and diagrams. If the user has an auditory learning style, the information can be provided by voice. If the user has an experiential learning style, the information can be provided in an interactive manner. This makes it possible to provide the optimal information providing method according to the user's learning style.

[0062] The dialogue unit can estimate the user's communication style based on the user's input content and adjust the dialogue method based on the estimated communication style. For example, if the user has a direct communication style, the dialogue can be concise and clear. If the user has an indirect communication style, the dialogue can be polite and detailed. If the user has an emotional communication style, the dialogue can be emotionally sensitive. This makes it possible to provide the optimal dialogue method according to the user's communication style.

[0063] The reception unit can estimate the user's lifestyle rhythm based on the user's input content and adjust the timing of receiving input based on the estimated lifestyle rhythm. For example, if the user has a nocturnal lifestyle rhythm, input can be preferentially received at night. Also, if the user has a morning lifestyle rhythm, input can be preferentially received in the morning. Also, if the user has an irregular lifestyle rhythm, input can be received at flexible timing. This makes it possible to provide the optimal input reception timing according to the user's lifestyle rhythm.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives user input. User input includes voice input, text input, image input, etc. For example, voice input is received using a microphone, text input is received using a keyboard, and image input is received using a camera. Voice input is converted into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the input received by the reception unit. The analysis is performed using methods such as natural language processing, sentiment analysis, and data mining. For example, the input content can be analyzed using text generation AI (LLM), and input content including audio and images can be analyzed using multimodal generation AI. Furthermore, the generation AI can also be used to analyze the sentiment of the input content. Step 3: The providing unit provides the analysis results obtained by the analysis unit. The provision is performed by methods such as text display, audio output, and notification. For example, the analysis results may be displayed on the screen as text, output as audio from a speaker, and sent to a smartphone as a notification. Step 4: The dialogue unit uses the generation AI to conduct a dialogue based on the analysis results provided by the provision unit. The dialogue can be conducted using methods such as a chatbot, voice assistant, or interactive UI. For example, a text generation AI (LLM) can be used to conduct a dialogue, and a multimodal generation AI can be used to conduct a dialogue that includes voice and images. Furthermore, the generation AI can be used to conduct a dialogue that corresponds to the user's emotions.

[0066] (Example 2) In an embodiment of the present invention, a self-management system allows a user to record things that made them happy, things that made them unhappy, worries, etc., either by voice or manual input. A generation AI analyzes these inputs and provides specific suggestions for when these feelings occur and, if they are worries, how to deal with them. The self-management system allows a user to record things that made them happy, things that made them unhappy, worries, etc., either by voice or manual input. The generation AI analyzes these inputs and provides specific suggestions for when these feelings occur and, if they are worries, how to deal with them. The self-management system also allows for dialogue using the multimodal function of the generation AI if a conversation partner is needed. For example, the self-management system allows a user to record things that made them happy, things that made them unhappy, worries, etc., either by voice or manual input. For example, a user might input content such as "I had a great time with my friends today" or "I'm feeling down because my boss scolded me at work." The self-management system then uses the generation AI to analyze the user's input content and identify when these feelings occur. The input to the generation AI is the user's input content itself, and the generation AI performs analysis based on that content. For example, the generative AI might identify patterns such as "I enjoy spending time with friends" or "I hate work stress." The self-management system then uses the generative AI's analysis results to suggest specific ways to address the problem. For example, it might provide advice such as, "To reduce work stress, it's important to take time to relax." The self-management system then uses the generative AI's multimodal capabilities to engage in dialogue. By interacting with the generative AI, users can organize their feelings and calmly analyze themselves. This allows the self-management system to help users organize their feelings and prevent illness from mental distress. The self-management system also allows users to calmly analyze themselves and organize their feelings. For example, by using the app daily, users can regularly reflect on their feelings and reduce stress. Furthermore, they can share their feelings and reset themselves through dialogue with the generative AI. This allows users to live healthier lives.

[0067] The self-management system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a dialogue unit. The reception unit receives user input. The user input includes, but is not limited to, voice input, text input, and image input. For example, the reception unit receives voice input using a microphone. The reception unit can also receive text input using a keyboard. The reception unit can also receive image input using a camera. For example, the reception unit records voice input using a microphone and converts it into text data using voice recognition technology. The reception unit receives text input as text data entered using a keyboard. The reception unit receives image input as image data captured by a camera. The analysis unit analyzes the input received by the reception unit using a generation AI. The analysis can be performed using, for example, natural language processing, sentiment analysis, data mining, or other methods, but is not limited to these examples. For example, the generation AI analyzes the input content using a text generation AI (e.g., LLM). The analysis unit can also analyze input content including voice and images using a multimodal generation AI. The analysis unit can also use a generation AI to analyze the emotions of the input content. For example, a text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle multiple modalities, such as images and audio, in addition to text. The generation AI uses emotion analysis technology to analyze the emotions of the input content. The provision unit provides the analysis results obtained by the analysis unit. The provision can be performed, for example, by displaying text, outputting audio, or notifying users, but is not limited to these examples. For example, the provision unit can display the analysis results as text. The provision unit can also output the analysis results as audio. The provision unit can also transmit the analysis results as a notification. For example, the provision unit can display the analysis results as text on a screen. The provision unit can output the analysis results as audio from a speaker. The provision unit can also transmit the analysis results as a notification to a smartphone. The dialogue unit uses the generation AI to engage in dialogue based on the analysis results provided by the provision unit.The dialogue may be conducted using, for example, a chatbot, a voice assistant, an interactive UI, or other methods, but is not limited to these examples. For example, the generation AI may conduct the dialogue using a text generation AI (e.g., LLM). The dialogue unit may also conduct a dialogue including audio and images using a multimodal generation AI. The dialogue unit may also conduct a dialogue based on the user's emotions using the generation AI. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI conducts a dialogue based on the user's emotions using emotion analysis technology. This allows the self-management system according to the embodiment to efficiently analyze the user's input and provide appropriate countermeasures and dialogue. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without AI. For example, the dialogue unit may conduct a dialogue using an AI model that receives the dialogue content generated by the generation AI as input and outputs a dialogue.

[0068] The reception unit can record the user's happy or unhappy experiences and worries by voice or manual input. Examples of happy or unhappy experiences and worries include, but are not limited to, events in daily life, worries at work, and interpersonal problems. The reception unit can, for example, allow the user to input voice using a microphone. The reception unit can also allow the user to input text using a keyboard. The reception unit can also allow the user to input images using a camera. For example, the reception unit can allow the user to input content such as "I had a great time with my friends today" or "I'm depressed because my boss scolded me at work." This allows the user to freely express their feelings. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can record voice input using a microphone and convert it into text data using voice recognition technology.

[0069] The analysis unit can analyze the user's input content and identify when the user feels a certain emotion. Examples of when the user feels a certain emotion include, but are not limited to, a specific event, a time period, or a location. The analysis unit can analyze the user's input content using, for example, a generation AI. For example, the generation AI can analyze the input content using a text generation AI (e.g., LLM). The analysis unit can also analyze input content including audio and images using a multimodal generation AI. The analysis unit can also analyze the emotions in the input content using the generation AI. For example, the generation AI can find patterns such as "I enjoy spending time with friends" and "I hate stress at work." This can identify the user's emotional pattern. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's input content to the generation AI and have the generation AI identify the emotional pattern.

[0070] The providing unit can suggest specific solutions to the worries based on the analysis results. Solutions to the worries include, but are not limited to, stress relief methods, problem-solving procedures, and expert advice. The providing unit can suggest specific solutions to the user based on the analysis results. For example, the providing unit can provide advice such as, "To reduce work stress, it is important to take time to relax." The providing unit can also provide expert advice to the user based on the analysis results. For example, the providing unit can suggest, "To receive expert advice, it is effective to receive counseling." This allows the user to be provided with specific solutions. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the analysis results to a generating AI and have the generating AI execute the solution suggestions.

[0071] The dialogue unit can dialogue with the user to help them sort out or reset their feelings. Examples of ways to sort out or reset their feelings include, but are not limited to, relaxation techniques, mental health care, and counseling. The dialogue unit can dialogue with the user using, for example, a generation AI. For example, the generation AI can dialogue using a text generation AI (e.g., LLM). The dialogue unit can also use a multimodal generation AI to conduct dialogue including audio and images. The dialogue unit can also use the generation AI to conduct dialogue based on the user's emotions. For example, the generation AI can suggest relaxation techniques based on the user's emotions. The dialogue unit can also use the generation AI to suggest mental health care methods based on the user's emotions. The dialogue unit can also use the generation AI to suggest counseling based on the user's emotions. This allows the user to sort out and reset their feelings. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can conduct dialogue using an AI model that receives dialogue content generated by the generation AI as input and outputs a dialogue.

[0072] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can delay the timing of input reception to provide the user with time to relax. Furthermore, if the user is relaxed, the reception unit can immediately accept input, enabling smooth operation. Furthermore, if the user is in a hurry, the reception unit can accelerate the timing of input reception to quickly record information. This allows input to be received at the optimal timing depending on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of input reception.

[0073] The reception unit can analyze the user's past input history and select the optimal reception method. Methods for analyzing the past input history include, but are not limited to, data mining, statistical analysis, and machine learning. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also select the optimal reception method for a specific time period based on the user's past input history. The reception unit can also analyze the user's past input content and suggest the most efficient reception method. This makes it possible to provide the optimal reception method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input past input history data into a generation AI and have the generation AI select the optimal reception method.

[0074] When receiving input, the reception unit can filter the input based on the user's current living situation and areas of interest. Examples of the current living situation and areas of interest include, but are not limited to, survey results and social media activity history. For example, when the user is at work, the reception unit can prioritize receiving work-related input content. Furthermore, when the user is on vacation, the reception unit can prioritize receiving input content related to relaxation and hobbies. Furthermore, when the user is participating in a specific event, the reception unit can prioritize receiving input content related to the event. This allows input according to the user's living situation and areas of interest to be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the user's living situation and areas of interest to the generation AI and have the generation AI perform filtering.

[0075] When receiving input, the reception unit can select the optimal reception means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit can receive the input using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also preferentially receive keyboard input. Furthermore, if the user selects image input, the reception unit can also receive input using image analysis technology. This makes it possible to provide the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal reception means.

[0076] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the reception unit can prioritize receiving inputs related to stress. Furthermore, if the user is relaxed, the reception unit can prioritize receiving inputs related to relaxation. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving inputs related to urgent matters. This allows the priority of inputs to be determined according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI determine the priority of the inputs.

[0077] When receiving input, the reception unit can prioritize receiving highly relevant input based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is in a specific location, the reception unit can prioritize receiving input related to the location. Furthermore, when the user is traveling, the reception unit can prioritize receiving input related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving input related to the home. This allows highly relevant input based on the user's geographical location information to be prioritized. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to prioritize receiving highly relevant input.

[0078] The reception unit may analyze the user's social media activity when receiving input and receive related input. Social media activity includes, but is not limited to, for example, post content, like history, and follower information. The reception unit may, for example, preferentially receive related input content based on content shared by the user on social media. The reception unit may also analyze the user's social media posts and preferentially receive related input content. The reception unit may also preferentially receive related input content based on the activities of the user's friends on social media. This allows reception of related input based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's social media activity data to the generation AI and cause the generation AI to receive related input.

[0079] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input. Past feedback includes, but is not limited to, user ratings, comments, and survey results. The reception unit can, for example, suggest an optimal reception method based on the user's past feedback. The reception unit can also preferentially select a specific reception method from the user's past feedback. The reception unit can also analyze the user's past feedback and suggest the most efficient reception method. This makes it possible to provide an optimal reception method based on the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input past feedback data into a generation AI and have the generation AI customize the reception method.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the analysis unit can provide a simple, visually easy-to-understand analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for providing an optimal analysis result 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the input content. The importance of the input content includes, but is not limited to, user ratings, urgency, and relevance. For example, the analysis unit performs a detailed analysis of input content with high importance. The analysis unit can also perform a concise analysis of input content with low importance. The analysis unit can also perform an analysis with an appropriate level of detail of input content with medium importance. This makes it possible to provide an optimal analysis result according to the importance of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the input content to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the input content. Examples of categories of input content include, but are not limited to, emotion categories, topic categories, and time zone categories. For example, the analysis unit applies an emotion analysis algorithm to input content related to emotions. The analysis unit can also apply a stress analysis algorithm to input content related to stress. The analysis unit can also apply a worry analysis algorithm to input content related to worries. This makes it possible to provide optimal analysis results according to the category of the input content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the input content to the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, past databases and user feedback. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also find specific patterns from the user's past analysis results and reflect them in the current analysis. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. This makes it possible to provide optimal analysis results based on the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result. This allows for providing an optimal analysis result 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the input content. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit, for example, prioritizes analysis of recently submitted input content. The analysis unit can also postpone input content that was submitted recently. The analysis unit can also appropriately analyze input content that was submitted recently. This makes it possible to provide optimal analysis results according to the submission time of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input submission time data to the generation AI and have the generation AI determine the analysis priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the input content. The relevance of the input content includes, but is not limited to, topic relevance, temporal relevance, etc. For example, the analysis unit prioritizes analysis of highly relevant input content. The analysis unit can also postpone analysis of less relevant input content. The analysis unit can also appropriately analyze input content with medium relevance. This makes it possible to provide optimal analysis results according to the relevance of the input content. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the input content to the generation AI and have the generation AI adjust the analysis order.

[0087] During analysis, the analysis unit can adjust the use of technical terminology in the analysis based on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past input content. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terminology. Alternatively, if the user does not have technical expertise, the analysis unit can provide analysis results in simple language. The analysis unit can also provide analysis results that use appropriate technical terminology depending on the user's level of expertise. This allows for optimal analysis results to be provided according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0088] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, when the user is feeling stressed, the providing unit can provide simple, visually easy-to-understand information. When the user is relaxed, the providing unit can also provide detailed information. When the user is in a hurry, the providing unit can also provide concise information that focuses on the main points. This makes it possible to provide optimal information 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the information.

[0089] The providing unit can adjust the level of detail of the analysis results provided based on the importance of the analysis results when providing the analysis results. The importance of the analysis results includes, but is not limited to, user ratings, urgency, and relevance. For example, the providing unit can provide detailed information for analysis results with high importance. The providing unit can also provide concise information for analysis results with low importance. The providing unit can also provide information with an appropriate level of detail for analysis results with medium importance. This makes it possible to provide optimal information according to the importance of the analysis results. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input importance data of the analysis results to the generating AI and cause the generating AI to adjust the level of detail of the analysis results provided.

[0090] The providing unit can apply different providing algorithms depending on the category of the analysis result when providing the analysis result. The categories of the analysis result include, but are not limited to, emotion categories, topic categories, and time zone categories, for example. The providing unit can apply an emotion analysis algorithm to analysis results related to emotions. The providing unit can also apply a stress analysis algorithm to analysis results related to stress. The providing unit can also apply a worry analysis algorithm to analysis results related to worries. This makes it possible to provide optimal information according to the category of the analysis result. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input category data of the analysis result to the generation AI and cause the generation AI to apply different providing algorithms.

[0091] The providing unit can improve the accuracy of the information provided by referring to the user's past provision results. Past provision results include, but are not limited to, past databases and user feedback. The providing unit, for example, corrects the current information provided by the user based on the user's past provision results. The providing unit can also find specific patterns from the user's past provision results and reflect them in the current information provided by the user. The providing unit can also analyze the user's past provision results and optimize the provision algorithm. This allows optimal information to be provided based on the user's past provision results. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input past provision result data into the generation AI and cause the generation AI to improve the accuracy of the information provided by the user.

[0092] The providing unit can estimate the user's emotion and adjust the length of the information to be provided based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the providing unit can provide short, concise information. If the user is relaxed, the providing unit can also provide detailed information. If the user is in a hurry, the providing unit can also provide concise information. This allows optimal information to be provided according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the information.

[0093] At the time of providing the analysis results, the providing unit can determine the priority of provision based on the submission time of the analysis results. The submission time includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The providing unit, for example, prioritizes providing the most recently submitted analysis results. The providing unit can also postpone analysis results that were submitted a long time ago. The providing unit can also appropriately provide analysis results that were submitted a medium time ago. This makes it possible to provide optimal information according to the submission time of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can input submission time data into the generation AI and have the generation AI determine the provision priority.

[0094] The providing unit can adjust the order of provision based on the relevance of the analysis results when providing them. The relevance of the analysis results includes, but is not limited to, for example, topic relevance and temporal relevance. For example, the providing unit can prioritize providing highly relevant analysis results. The providing unit can also postpone analysis results with low relevance. The providing unit can also moderately provide analysis results with medium relevance. This makes it possible to provide optimal information according to the relevance of the analysis results. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the analysis results to the generation AI and cause the generation AI to adjust the order of provision.

[0095] The providing unit can adjust the use of technical terminology in the provided information depending on the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past input content. For example, if the user has technical expertise, the providing unit can provide information using a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can also provide information in simple language. Furthermore, the providing unit can provide information using appropriate technical terminology depending on the user's level of expertise. This allows optimal information to be provided depending on the user's level of expertise. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0096] The dialogue unit can estimate the user's emotions and adjust the dialogue expression method based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the dialogue unit can speak in a calm tone. If the user is relaxed, the dialogue unit can speak in a bright tone. If the user is in a hurry, the dialogue unit can speak quickly and concisely. This makes it possible to provide an optimal dialogue 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or without AI. For example, the dialogue unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the dialogue expression method.

[0097] During a dialogue, the dialogue unit can select an optimal dialogue method based on the user's past dialogue history. Examples of past dialogue history include, but are not limited to, past chat logs and voice recordings. The dialogue unit selects the optimal dialogue method based on, for example, the user's preferred dialogue style in the past. The dialogue unit can also prioritize a specific topic from the user's past dialogue history. The dialogue unit can also analyze the user's past dialogue history and suggest the most effective dialogue method. This makes it possible to provide an optimal dialogue method based on the user's past dialogue history. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input past dialogue history data into a generation AI and have the generation AI select an optimal dialogue method.

[0098] During a dialogue, the dialogue unit can customize dialogue content based on the user's current living situation and areas of interest. Examples of the current living situation and areas of interest include, but are not limited to, survey results and social media activity history. For example, if the user is at work, the dialogue unit can prioritize providing work-related dialogue content. Furthermore, if the user is on vacation, the dialogue unit can prioritize providing relaxation and hobbies-related dialogue content. Furthermore, if the user is participating in a specific event, the dialogue unit can prioritize providing event-related dialogue content. This makes it possible to provide optimal dialogue content according to the user's living situation and areas of interest. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input data on the user's living situation and areas of interest into the generation AI and cause the generation AI to customize the dialogue content.

[0099] The dialogue unit can improve the dialogue method by reflecting user feedback during dialogue. Examples of feedback include, but are not limited to, user ratings, comments, and survey results. For example, the dialogue unit can suggest an optimal dialogue method based on feedback previously provided by the user. The dialogue unit can also preferentially select a specific dialogue method based on the user's past feedback. The dialogue unit can also analyze the user's past feedback and optimize the dialogue algorithm. This makes it possible to provide an optimal dialogue method based on the user's feedback. Some or all of the above-described processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input feedback data into a generation AI and cause the generation AI to improve the dialogue method.

[0100] The dialogue unit can estimate the user's emotions and determine the priority of dialogues based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the dialogue unit can prioritize providing dialogue content related to stress. Furthermore, if the user is relaxed, the dialogue unit can prioritize providing dialogue content related to relaxation. Furthermore, if the user is in a hurry, the dialogue unit can prioritize providing dialogue content related to urgent matters. This makes it possible to provide optimal dialogue content 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, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI. For example, the dialogue unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of dialogues.

[0101] During a dialogue, the dialogue unit can select an optimal dialogue method based on the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is in a specific location, the dialogue unit can prioritize providing dialogue content related to that location. Furthermore, when the user is traveling, the dialogue unit can prioritize providing dialogue content related to the user's travel destination. Furthermore, when the user is at home, the dialogue unit can prioritize providing dialogue content related to the user's home. This allows optimal dialogue content to be provided based on the user's geographical location information. Some or all of the above-described processing in the dialogue unit may be performed using, or without, an AI. For example, the dialogue unit can input the user's geographical location information data into a generation AI and cause the generation AI to select an optimal dialogue method.

[0102] During a dialogue, the dialogue unit can analyze the user's social media activity and suggest dialogue content. Social media activity includes, but is not limited to, for example, posted content, like history, and follower information. For example, the dialogue unit can prioritize providing related dialogue content based on content shared by the user on social media. The dialogue unit can also analyze the user's social media posts and prioritize providing related dialogue content. The dialogue unit can also refer to the activities of the user's friends on social media and prioritize providing related dialogue content. This makes it possible to provide optimal dialogue content based on the user's social media activity. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest dialogue content.

[0103] The dialogue unit can customize the dialogue method by reflecting the user's past feedback during dialogue. Examples of the feedback include, but are not limited to, user ratings, comments, and survey results. The dialogue unit can, for example, suggest an optimal dialogue method based on the user's past feedback. The dialogue unit can also preferentially select a specific dialogue method based on the user's past feedback. The dialogue unit can also analyze the user's past feedback and optimize the dialogue algorithm. This makes it possible to provide an optimal dialogue method based on the user's past feedback. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI. For example, the dialogue unit can input feedback data into a generation AI and have the generation AI customize the dialogue method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and dialogue 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 receives a user's voice input or text input using the microphone 38B or keyboard of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input using a generation AI. The provision unit displays or outputs the analysis results as audio using, for example, the display 40A or speaker 40B of the smart device 14. The dialogue unit is realized, for example, by the control unit 46A of the smart device 14 and engages in a dialogue with the user using a generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, provision unit, and dialogue 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 receives a user's voice input or image input using the microphone 238 or camera 42 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input content using a generation AI. The provision unit outputs the analysis result as voice using, for example, the speaker 240 of the smart glasses 214. The dialogue unit is realized, for example, by the control unit 46A of the smart glasses 214, and engages in a dialogue with the user using a generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and dialogue 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 receives voice input and image input from the user using the microphone 238 or camera 42 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input content using a generation AI. The provision unit displays or outputs the analysis results as audio using, for example, the display 343 or speaker 240 of the headset type terminal 314. The dialogue unit is realized, for example, by the control unit 46A of the headset type terminal 314, and engages in dialogue with the user using a generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and dialogue unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input and image input from the user using the microphone 238 and camera 42 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's input content using a generation AI. The provision unit outputs or displays the analysis results by voice using, for example, the speaker 240 or display device of the robot 414. The dialogue unit is realized, for example, by the control unit 46A of the robot 414, and engages in dialogue with the user using a generation AI.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can estimate the user's health condition based on the user's input content and adjust the input reception method based on the estimated health condition. For example, if the user is tired, simple voice input can be preferentially received. If the user is healthy, detailed text input can be received. If the user is ill, input can be temporarily stopped and the user can be encouraged to rest. This makes it possible to provide an optimal input reception method according to the user's health condition.

[0106] The analysis unit can estimate the user's hobbies and interests based on the user's input, and customize the analysis results based on the estimated hobbies and interests. For example, if the user is interested in sports, analysis results related to sports can be provided preferentially. Also, if the user is interested in music, analysis results related to music can be provided. Also, if the user is interested in travel, analysis results related to travel can be provided. This makes it possible to provide optimal analysis results according to the user's hobbies and interests.

[0107] The providing unit can estimate the user's learning style based on the user's input, and adjust the information providing method based on the estimated learning style. For example, if the user has a visual learning style, the information can be provided using visually easy-to-understand graphs and diagrams. If the user has an auditory learning style, the information can be provided by voice. If the user has an experiential learning style, the information can be provided in an interactive manner. This makes it possible to provide the optimal information providing method according to the user's learning style.

[0108] The dialogue unit can estimate the user's communication style based on the user's input content and adjust the dialogue method based on the estimated communication style. For example, if the user has a direct communication style, the dialogue can be concise and clear. If the user has an indirect communication style, the dialogue can be polite and detailed. If the user has an emotional communication style, the dialogue can be emotionally sensitive. This makes it possible to provide the optimal dialogue method according to the user's communication style.

[0109] The reception unit can estimate the user's lifestyle rhythm based on the user's input content and adjust the timing of receiving input based on the estimated lifestyle rhythm. For example, if the user has a nocturnal lifestyle rhythm, input can be preferentially received at night. Also, if the user has a morning lifestyle rhythm, input can be preferentially received in the morning. Also, if the user has an irregular lifestyle rhythm, input can be received at flexible timing. This makes it possible to provide the optimal input reception timing according to the user's lifestyle rhythm.

[0110] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, positive feedback can be given priority. Also, if the user is relaxed, detailed feedback can be given. Also, if the user is in a hurry, brief feedback can be given. In this way, it is possible to provide the optimal feedback method according to the user's emotions.

[0111] The providing unit can estimate the user's emotion and adjust the tone of the information to be provided based on the estimated user's emotion. For example, if the user is feeling stressed, the information can be provided in a calm tone. If the user is relaxed, the information can be provided in a bright tone. If the user is in a hurry, the information can be provided in a quick and concise tone. In this way, the information providing tone can be optimal according to the user's emotion.

[0112] The dialogue unit can estimate the user's emotions and adjust the pace of the dialogue based on the estimated user's emotions. For example, if the user is feeling stressed, the dialogue can be conducted at a slow pace. If the user is relaxed, the dialogue can be conducted at a normal pace. If the user is in a hurry, the dialogue can be conducted at a fast pace. This makes it possible to provide an optimal dialogue pace according to the user's emotions.

[0113] The reception unit can estimate the user's emotion and customize the input reception method based on the estimated user's emotion. For example, if the user is feeling stressed, simple voice input can be preferentially received. If the user is relaxed, detailed text input can be received. If the user is in a hurry, a quick input method can be received. This makes it possible to provide an optimal input reception method according to the user's emotion.

[0114] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, it can provide simple, visually easy-to-understand analysis results. If the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that focus on the main points. This makes it possible to provide the optimal analysis result display method according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives user input. User input includes voice input, text input, image input, etc. For example, voice input is received using a microphone, text input is received using a keyboard, and image input is received using a camera. Voice input is converted into text data using voice recognition technology. Step 2: The analysis unit uses the generation AI to analyze the input received by the reception unit. The analysis is performed using methods such as natural language processing, sentiment analysis, and data mining. For example, the input content can be analyzed using text generation AI (LLM), and input content including audio and images can be analyzed using multimodal generation AI. Furthermore, the generation AI can also be used to analyze the sentiment of the input content. Step 3: The providing unit provides the analysis results obtained by the analysis unit. The provision is performed by methods such as text display, audio output, and notification. For example, the analysis results may be displayed on the screen as text, output as audio from a speaker, and sent to a smartphone as a notification. Step 4: The dialogue unit uses the generation AI to conduct a dialogue based on the analysis results provided by the provision unit. The dialogue can be conducted using methods such as a chatbot, voice assistant, or interactive UI. For example, a text generation AI (LLM) can be used to conduct a dialogue, and a multimodal generation AI can be used to conduct a dialogue that includes voice and images. Furthermore, the generation AI can be used to conduct a dialogue that corresponds to the user's emotions.

[0117] 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.

[0118] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0147] 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 input from a user; an analysis unit that analyzes the input received by the reception unit; a providing unit that provides the analysis results obtained by the analysis unit; a dialogue unit that conducts dialogue based on the analysis result provided by the providing unit; Equipped with A system characterized by:

2. The reception unit Users record what made them happy, what they disliked, and their worries by voice or manual input.

2. The system of claim 1.

3. The analysis unit Analyze user input to identify when they feel that way 2. The system of claim 1.

4. The providing unit Based on the analysis results, we propose specific solutions to your concerns.

2. The system of claim 1.

5. The dialogue unit Dialogue with users to help them sort out or reset their feelings 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the optimal reception method 2. The system of claim 1.

8. The reception unit As input is received, it filters it based on the user's current life situation and interests.

2. The system of claim 1.

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