system

Abstract

The system according to the embodiment aims to provide advice corresponding to the individual worries and goals of the user. [Solution] A system according to an embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs a user's concerns or goals. The analyzing unit analyzes the information input by the receiving unit. The generating unit generates advice based on the information analyzed by the analyzing unit. The providing unit provides the advice generated by the generating 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 technology has the drawback of being expensive to obtain advice tailored to individual situations, and privacy concerns make it difficult to disclose detailed concerns.

[0005] The system according to the embodiment aims to provide advice corresponding to the individual worries and goals of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs a user's concerns or goals. The analyzing unit analyzes the information input by the receiving unit. The generating unit generates advice based on the information analyzed by the analyzing unit. The providing unit provides the advice generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide advice that corresponds to the individual worries and goals of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An advice providing system according to an embodiment of the present invention uses AI to provide appropriate advice to a user regarding their concerns and goals. In the advice providing system, a user inputs their concerns and goals, and AI analyzes the information to generate and provide appropriate advice. For example, a user inputs concerns and goals such as "I want to reduce work stress" or "I want to live a healthy life." Next, AI analyzes the input information and generates advice tailored to the user's situation. The generated advice is provided to the user, allowing the user to obtain specific solutions. This allows users to resolve their concerns at a lower cost than one-on-one human coaching while protecting their privacy. This allows the advice providing system to provide specific advice to the user regarding their concerns and goals. For example, by providing specific methods for reducing work stress or advice on diet and exercise for a healthy lifestyle, the user's quality of life can be improved. Furthermore, because privacy is protected, users can freely discuss things they would not discuss with a human. This allows users to face their concerns and goals with peace of mind.

[0029] The advice providing system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit inputs a user's worries or goals. The user's worries and goals include, but are not limited to, work-related worries and health goals. The receiving unit allows the user to input, for example, specific worries or goals they want to achieve. The analysis unit analyzes the information input by the receiving unit. The analysis may be performed using, for example, but not limited to, text analysis or sentiment analysis. The analysis unit understands information about the user's worries and goals using, for example, natural language processing technology, and generates appropriate advice. The generation unit generates advice based on the information analyzed by the analysis unit. The advice may be provided in, for example, a sentence format or a list format, for example, but not limited to, examples. The generation unit provides, for example, specific methods for reducing work stress and diet and exercise advice for living a healthy lifestyle. The provision unit provides the advice generated by the generation unit to the user. The advice may be provided by, for example, but not limited to, email, app notification, or other methods. The provision unit provides the generated advice to the user. As a result, the advice providing system according to the embodiment can provide appropriate advice for the user's worries and goals.

[0030] The reception unit allows the user to input their concerns or goals they want to achieve. For example, the reception unit allows the user to input specific concerns or goals they want to achieve. For example, the user can input concerns or goals such as "I want to reduce work stress" or "I want to live a healthy life." By inputting the user's specific concerns or goals, more appropriate advice can be provided. 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 text data entered by the user to a generation AI, which then analyzes the text data.

[0031] The analysis unit can understand information related to the user's concerns or goals and generate advice. The analysis unit can understand information related to the user's concerns or goals using, for example, natural language processing technology and generate appropriate advice. For example, the analysis unit can analyze the user's input content using text analysis technology and extract information related to the concerns or goals. The analysis unit can also analyze the user's emotions using emotion analysis technology and generate appropriate advice. For example, the analysis unit can infer emotions from the user's input content and generate advice based on those emotions. This makes it possible to generate appropriate advice for the user's concerns or goals. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI, which can then analyze the data.

[0032] The generation unit can provide methods for reducing work stress or dietary or exercise advice for living a healthy life. The generation unit can provide, for example, specific methods for reducing work stress. For example, the generation unit can provide relaxation techniques or time management methods. The generation unit can also provide dietary or exercise advice for living a healthy life. For example, the generation unit can provide methods for a balanced diet or regular exercise. This makes it possible to provide appropriate advice for the user's specific concerns or goals. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input data related to the user's concerns or goals into the generation AI, and have the generation AI generate the advice.

[0033] The providing unit can provide the generated advice to the user. The providing unit provides the generated advice to the user, for example. The provision can be performed by, for example, email or app notification, but is not limited to these examples. For example, the providing unit can send the generated advice to the user by email. The providing unit can also provide the advice to the user through app notification. In this way, by providing the generated advice to the user, the user can obtain a specific solution to their worries or goals. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can provide the generated advice to the user by a generation AI.

[0034] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates worries or goals that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest worries or goals to be input during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned 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 past input data into a generation AI, which can then suggest the optimal input method.

[0035] The reception unit can filter the input content based on the user's current situation and environment. For example, when the user is at work, the reception unit can prioritize displaying work-related concerns and goals. Furthermore, when the user is at home, the reception unit can also prioritize displaying home- and health-related concerns and goals. Furthermore, when the user is on the move, the reception unit can prioritize displaying simple questions that can be entered in a short time. This allows for more appropriate advice to be generated by providing input content that is appropriate for the user's current situation and environment. 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 the user's current location information into the generation AI, which can then filter the input content.

[0036] The reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the user's worries and goals using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the user's worries and goals using a keyboard or touch panel. Furthermore, if the user selects image input, the reception unit can also input the user's worries and goals using image analysis technology. This improves input efficiency by providing the optimal means depending on the user's input method. Some or all of the above-mentioned 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 data to a generation AI, which then selects the optimal input means.

[0037] The reception unit can prioritize acquiring highly relevant input content taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize displaying concerns and goals related to that area. Furthermore, when the user is traveling, the reception unit can prioritize displaying concerns and goals related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize displaying concerns and goals related to home or health. This allows more appropriate advice to be generated by providing highly relevant input content based on the user's geographical location information. 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 location information data to the generation AI, which can then acquire highly relevant input content.

[0038] The reception unit can analyze the user's social media activity and acquire related input content. The reception unit can, for example, analyze content posted by the user on social media and suggest related concerns and goals. The reception unit can also suggest related concerns and goals by referring to the activities of the user's friends on social media. Furthermore, the reception unit can also suggest related concerns and goals based on the user's check-in information on social media. This allows for more appropriate advice to be generated by providing related input content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI, which can then acquire related input content.

[0039] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit preferentially suggests an input method that the user has used favorably in the past. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. This improves input efficiency by customizing the input method based on the user's past feedback. 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 past feedback data into a generation AI, which can then customize the input method.

[0040] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit can improve the accuracy of the analysis by, for example, referring to data related to the user's past worries and goals. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. Furthermore, the analysis unit can analyze the user's past behavioral patterns and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past data into a generation AI, which can then improve the accuracy of the analysis.

[0041] The analysis unit can perform analysis taking into account the user's attribute information. The analysis unit can generate appropriate advice taking into account the user's age and gender, for example. The analysis unit can also generate appropriate advice taking into account the user's occupation and lifestyle. Furthermore, the analysis unit can generate appropriate advice taking into account the user's health condition and hobbies. In this way, by performing analysis based on the user's attribute information, more appropriate advice can be generated. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's attribute information into a generation AI, and the generation AI can perform the analysis.

[0042] During analysis, the analysis unit can weight the analysis based on the frequency of user input. For example, the analysis unit can assign a higher weight to worries or goals that the user frequently inputs. The analysis unit can also adjust the weight of the analysis based on the frequency of past input by the user. Furthermore, the analysis unit can set an analysis priority according to the frequency of user input. In this way, more appropriate advice can be generated by weighting the analysis based on the frequency of user input. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's input frequency data to a generation AI, and the generation AI can weight the analysis.

[0043] The analysis unit can take the user's geographical distribution into consideration when performing the analysis. For example, if the user is in a specific area, the analysis unit can prioritize analyzing concerns and goals related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing concerns and goals related to the travel destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing concerns and goals related to home or health. This allows for analysis based on the user's geographical distribution to generate more appropriate advice. 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 geographical distribution data into a generation AI, which can then perform the analysis.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research papers related to the user's concerns and goals. The analysis unit can also improve the accuracy of the analysis by referring to past research results related to the user's concerns and goals. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to specialized books related to the user's concerns and goals. In this way, the accuracy of the analysis is improved by referring to related literature. 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 related literature data into a generation AI, which can improve the accuracy of the analysis.

[0045] The analysis unit can perform the analysis taking into account the user's market value. The analysis unit can perform the analysis taking into account market value, for example, based on the user's occupation and skills. The analysis unit can also perform the analysis taking into account market value based on the user's past performance and evaluations. Furthermore, the analysis unit can perform the analysis taking into account market value, taking into account the user's future career path. This allows more appropriate advice to be generated by taking into account the user's market value. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's market value data into a generation AI, and have the generation AI perform the analysis.

[0046] When generating advice, the generation unit can adjust the level of detail of the advice based on the user's level of importance. For example, if the user has a serious concern, the generation unit can provide detailed advice. Furthermore, if the user has a minor concern, the generation unit can also provide concise advice. Furthermore, the generation unit can adjust the level of detail of the advice based on the user's past feedback. In this way, by adjusting the level of detail of the advice according to the user's level of importance, more appropriate advice can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of importance data into the generation AI, and the generation AI can adjust the level of detail of the advice.

[0047] When generating advice, the generation unit can apply different advice algorithms depending on the user's category. For example, if the user has work-related concerns, the generation unit can apply an advice algorithm specialized for work. Furthermore, if the user has health-related concerns, the generation unit can also apply an advice algorithm specialized for health. Furthermore, if the user has interpersonal concerns, the generation unit can also apply an advice algorithm specialized for interpersonal relationships. In this way, by applying an advice algorithm according to the user's category, more appropriate advice can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's category data into the generation AI, and the generation AI can apply the advice algorithm.

[0048] When generating advice, the generation unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the generation unit analyzes the results of advice the user has received in the past and provides more appropriate advice for similar situations. The generation unit can also adjust the advice algorithm based on the user's past advice results. Furthermore, the generation unit can also refer to the user's past advice results and apply successful advice patterns. In this way, the accuracy of the advice is improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past advice result data into the generation AI, and the generation AI can improve the accuracy of the advice.

[0049] When generating advice, the generation unit can determine the priority of advice based on the time of user submission. For example, if a user submits an urgent problem, the generation unit can provide advice with priority. Furthermore, if a user submits a long-term goal, the generation unit can provide advice that can be put off. Furthermore, the generation unit can dynamically adjust the priority of advice based on the time of user submission. In this way, more appropriate advice can be provided by determining the priority of advice based on the time of user submission. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user submission time data into the generation AI, and the generation AI can determine the priority of advice.

[0050] When generating advice, the generation unit can adjust the order of advice based on the user's relevance. For example, the generation unit can prioritize providing advice that is most relevant to the user's current situation. The generation unit can also prioritize providing highly relevant advice based on the user's past behavioral patterns. Furthermore, the generation unit can also prioritize providing advice that is most relevant to the user's current concerns or goals. In this way, by adjusting the order of advice based on the user's relevance, more appropriate advice can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI, and the generation AI can adjust the order of advice.

[0051] When generating advice, the generation unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can provide advice that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can provide easy-to-understand advice that avoids technical terminology. Furthermore, the generation unit can adjust the content of the advice according to the user's level of expertise. In this way, more appropriate advice can be provided by adjusting the content of the advice according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terminology in the advice.

[0052] When providing advice, the providing unit can select the optimal delivery method by referring to the user's past feedback. For example, the providing unit preferentially suggests delivery methods that the user has used favorably in the past. The providing unit can also customize the delivery method based on the user's past feedback. Furthermore, the providing unit can also optimize the delivery procedure by reflecting the user's past feedback. This allows more appropriate advice to be provided by selecting the optimal delivery method based on the user's past feedback. Some or all of the above-described 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 the user's past feedback data into a generation AI, which can select the optimal delivery method.

[0053] When providing advice, the providing unit can customize the content to be provided according to the user's current task. For example, if the user is at work, the providing unit can prioritize providing work-related advice. Also, if the user is on vacation, the providing unit can prioritize providing advice on relaxation and refreshment. Furthermore, if the user is working on a specific project, the providing unit can provide advice related to that project. In this way, by customizing the content to be provided according to the user's current task, more appropriate advice can be provided. 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 the user's current task data into a generating AI, and the generating AI can customize the content to be provided.

[0054] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. For example, if the user provides feedback on the advice provided, the providing unit improves the advice providing method based on that feedback. The providing unit can also analyze the user feedback and optimize the advice providing method. Furthermore, the providing unit can also adjust the advice providing procedure by reflecting the user feedback. In this way, more appropriate advice can be provided by improving the advice providing method based on the user feedback. Some or all of the above-described 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 user feedback data into a generation AI, which can then improve the advice providing method.

[0055] When providing advice, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows more appropriate advice to be provided by selecting the optimal delivery method based on the user's device information. Some or all of the above-described 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 the user's device information into a generation AI, which can select the optimal delivery method.

[0056] When providing advice, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the advice based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide advice in that language. This allows for more appropriate advice to be provided by providing advice in multiple languages ​​according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generating AI, which can then provide advice in multiple languages.

[0057] The providing unit can analyze the user's lifestyle rhythm when providing advice and suggest the optimal timing for providing the advice. For example, if the user is a morning person, the providing unit can provide advice in the morning hours. Furthermore, if the user is a night owl, the providing unit can also provide advice in the evening hours. Furthermore, the providing unit can analyze the user's lifestyle rhythm and dynamically adjust the optimal timing for providing the advice. This allows more appropriate advice to be provided by suggesting the optimal timing for providing the advice based on the user's lifestyle rhythm. 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 the user's lifestyle rhythm data into a generating AI, which can then suggest the optimal timing for providing the advice.

[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 automatically suggest related past advice based on the user's input. For example, if the user inputs "I want to reduce work stress," the reception unit can suggest advice based on advice previously provided to users with similar concerns. Also, if the user inputs "I want to live a healthy life," the reception unit can automatically display past health advice. Furthermore, the reception unit can suggest related topics and categories based on the user's input. This allows the user to receive more appropriate advice while referring to past advice.

[0060] The analysis unit can collect related external data based on the user's input and use it for analysis. For example, if the user inputs "I want to reduce work stress," the analysis unit can collect the latest research papers and articles on stress management and use it for analysis. Similarly, if the user inputs "I want to live a healthy life," the analysis unit can collect the latest health-related data and use it for analysis. Furthermore, the analysis unit can collect related statistical data and market research data based on the user's input and use it for analysis. This allows the system to provide more accurate advice to address the user's concerns and goals.

[0061] The generating unit can generate multiple advice options based on the user's input and provide the user with choices. For example, if the user inputs "I want to reduce work stress," the generating unit can generate multiple advice options such as relaxation techniques, time management methods, and ways to improve the work environment. Alternatively, if the user inputs "I want to live a healthy life," the generating unit can generate multiple advice options such as meal plans, exercise plans, and ways to improve sleep. Furthermore, the generating unit can provide a combination of short-term and long-term advice based on the user's input, allowing the user to select the advice that best suits them.

[0062] The providing unit can collect user feedback in real time and evaluate the effectiveness of the advice. For example, after the user implements the advice, the providing unit can provide feedback on the effectiveness of the advice. The providing unit can also improve the content of the advice based on the user feedback. Furthermore, the providing unit can analyze the user feedback and quantitatively evaluate the effectiveness of the advice. This allows the user to receive more appropriate advice while checking the effectiveness of the advice.

[0063] The providing unit can collect user feedback in real time and evaluate the effectiveness of the advice. For example, after the user implements the advice, the providing unit can provide feedback on the effectiveness of the advice. The providing unit can also improve the content of the advice based on the user feedback. Furthermore, the providing unit can analyze the user feedback and quantitatively evaluate the effectiveness of the advice. This allows the user to receive more appropriate advice while checking the effectiveness of the advice.

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

[0065] Step 1: The reception unit inputs the user's worries or goals. The user's worries and goals include, but are not limited to, work-related worries, health goals, etc. The reception unit allows the user to input, for example, specific worries or goals they want to achieve. Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed by, for example, but not limited to, methods such as text analysis and sentiment analysis. The analysis unit uses, for example, natural language processing technology to understand information about the user's concerns and goals and generate appropriate advice. Step 3: The generator generates advice based on the information analyzed by the analyzer. The advice may be provided in, for example, a sentence format or a list format, but is not limited to these examples. The generator may provide, for example, specific methods for reducing work stress, or advice on diet and exercise for living a healthy lifestyle. Step 4: The providing unit provides the advice generated by the generating unit to the user. The advice may be provided by, for example, but not limited to, an email or an app notification. The providing unit provides the generated advice to the user, for example.

[0066] (Example 2) An advice providing system according to an embodiment of the present invention uses AI to provide appropriate advice to a user regarding their concerns and goals. In the advice providing system, a user inputs their concerns and goals, and AI analyzes the information to generate and provide appropriate advice. For example, a user inputs concerns and goals such as "I want to reduce work stress" or "I want to live a healthy life." Next, AI analyzes the input information and generates advice tailored to the user's situation. The generated advice is provided to the user, allowing the user to obtain specific solutions. This allows users to resolve their concerns at a lower cost than one-on-one human coaching while protecting their privacy. This allows the advice providing system to provide specific advice to the user regarding their concerns and goals. For example, by providing specific methods for reducing work stress or advice on diet and exercise for a healthy lifestyle, the user's quality of life can be improved. Furthermore, because privacy is protected, users can freely discuss things they would not discuss with a human. This allows users to face their concerns and goals with peace of mind.

[0067] The advice providing system according to the embodiment includes a receiving unit, an analysis unit, a generation unit, and a provision unit. The receiving unit inputs a user's worries or goals. The user's worries and goals include, but are not limited to, work-related worries and health goals. The receiving unit allows the user to input, for example, specific worries or goals they want to achieve. The analysis unit analyzes the information input by the receiving unit. The analysis may be performed using, for example, but not limited to, text analysis or sentiment analysis. The analysis unit understands information about the user's worries and goals using, for example, natural language processing technology, and generates appropriate advice. The generation unit generates advice based on the information analyzed by the analysis unit. The advice may be provided in, for example, a sentence format or a list format, for example, but not limited to, examples. The generation unit provides, for example, specific methods for reducing work stress and diet and exercise advice for living a healthy lifestyle. The provision unit provides the advice generated by the generation unit to the user. The advice may be provided by, for example, but not limited to, email, app notification, or other methods. The provision unit provides the generated advice to the user. As a result, the advice providing system according to the embodiment can provide appropriate advice for the user's worries and goals.

[0068] The reception unit allows the user to input their concerns or goals they want to achieve. For example, the reception unit allows the user to input specific concerns or goals they want to achieve. For example, the user can input concerns or goals such as "I want to reduce work stress" or "I want to live a healthy life." By inputting the user's specific concerns or goals, more appropriate advice can be provided. 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 text data entered by the user to a generation AI, which then analyzes the text data.

[0069] The analysis unit can understand information related to the user's concerns or goals and generate advice. The analysis unit can understand information related to the user's concerns or goals using, for example, natural language processing technology and generate appropriate advice. For example, the analysis unit can analyze the user's input content using text analysis technology and extract information related to the concerns or goals. The analysis unit can also analyze the user's emotions using emotion analysis technology and generate appropriate advice. For example, the analysis unit can infer emotions from the user's input content and generate advice based on those emotions. This makes it possible to generate appropriate advice for the user's concerns or goals. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's input data into a generation AI, which can then analyze the data.

[0070] The generation unit can provide methods for reducing work stress or dietary or exercise advice for living a healthy life. The generation unit can provide, for example, specific methods for reducing work stress. For example, the generation unit can provide relaxation techniques or time management methods. The generation unit can also provide dietary or exercise advice for living a healthy life. For example, the generation unit can provide methods for a balanced diet or regular exercise. This makes it possible to provide appropriate advice for the user's specific concerns or goals. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input data related to the user's concerns or goals into the generation AI, and have the generation AI generate the advice.

[0071] The providing unit can provide the generated advice to the user. The providing unit provides the generated advice to the user, for example. The provision can be performed by, for example, email or app notification, but is not limited to these examples. For example, the providing unit can send the generated advice to the user by email. The providing unit can also provide the advice to the user through app notification. In this way, by providing the generated advice to the user, the user can obtain a specific solution to their worries or goals. Some or all of the above-mentioned processing in the providing unit can be performed, for example, using AI, or can be performed without using AI. For example, the providing unit can provide the generated advice to the user by a generation AI.

[0072] The reception unit can estimate the user's emotions and adjust the display method of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and allow the user to quickly input their concerns and goals. This allows the input interface to be adjusted according to the user's emotions, providing a more appropriate input environment. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or without AI. For example, the reception unit can input the user's facial expression data into a generation AI, which can then estimate the emotion.

[0073] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates worries or goals that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest worries or goals to be input during a specific time period based on the user's past input history. This improves input efficiency by suggesting the optimal input method based on the user's past input history. Some or all of the above-mentioned 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 past input data into a generation AI, which can then suggest the optimal input method.

[0074] The reception unit can filter the input content based on the user's current situation and environment. For example, when the user is at work, the reception unit can prioritize displaying work-related concerns and goals. Furthermore, when the user is at home, the reception unit can also prioritize displaying home- and health-related concerns and goals. Furthermore, when the user is on the move, the reception unit can prioritize displaying simple questions that can be entered in a short time. This allows for more appropriate advice to be generated by providing input content that is appropriate for the user's current situation and environment. 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 the user's current location information into the generation AI, which can then filter the input content.

[0075] The reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the user's worries and goals using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the user's worries and goals using a keyboard or touch panel. Furthermore, if the user selects image input, the reception unit can also input the user's worries and goals using image analysis technology. This improves input efficiency by providing the optimal means depending on the user's input method. Some or all of the above-mentioned 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 data to a generation AI, which then selects the optimal input means.

[0076] The reception unit can estimate the user's emotions and prioritize input content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can prioritize displaying input content related to stress reduction. Furthermore, if the user is relaxed, the reception unit can prioritize displaying input content related to long-term goals. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying input content related to short-term solutions. This allows for more appropriate advice to be provided by prioritizing input content according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI, which then estimates the emotion.

[0077] The reception unit can prioritize acquiring highly relevant input content taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize displaying concerns and goals related to that area. Furthermore, when the user is traveling, the reception unit can prioritize displaying concerns and goals related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize displaying concerns and goals related to home or health. This allows more appropriate advice to be generated by providing highly relevant input content based on the user's geographical location information. 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 location information data to the generation AI, which can then acquire highly relevant input content.

[0078] The reception unit can analyze the user's social media activity and acquire related input content. The reception unit can, for example, analyze content posted by the user on social media and suggest related concerns and goals. The reception unit can also suggest related concerns and goals by referring to the activities of the user's friends on social media. Furthermore, the reception unit can also suggest related concerns and goals based on the user's check-in information on social media. This allows for more appropriate advice to be generated by providing related input content based on the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI, which can then acquire related input content.

[0079] The reception unit can customize the input method by reflecting the user's past feedback. For example, the reception unit preferentially suggests an input method that the user has used favorably in the past. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can also optimize the input procedure by reflecting the user's past feedback. This improves input efficiency by customizing the input method based on the user's past feedback. 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 past feedback data into a generation AI, which can then customize the input method.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize applying an analysis algorithm related to stress reduction. Furthermore, if the user is relaxed, the analysis unit can also apply an analysis algorithm related to long-term goals. Furthermore, if the user is in a hurry, the analysis unit can also apply an analysis algorithm related to short-term solutions. By adjusting the analysis algorithm according to the user's emotions, more appropriate advice can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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, which can then adjust the analysis algorithm.

[0081] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past data. The analysis unit can improve the accuracy of the analysis by, for example, referring to data related to the user's past worries and goals. The analysis unit can also adjust the analysis algorithm based on the user's past feedback. Furthermore, the analysis unit can analyze the user's past behavioral patterns and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past data into a generation AI, which can then improve the accuracy of the analysis.

[0082] The analysis unit can perform analysis taking into account the user's attribute information. The analysis unit can generate appropriate advice taking into account the user's age and gender, for example. The analysis unit can also generate appropriate advice taking into account the user's occupation and lifestyle. Furthermore, the analysis unit can generate appropriate advice taking into account the user's health condition and hobbies. In this way, by performing analysis based on the user's attribute information, more appropriate advice can be generated. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's attribute information into a generation AI, and the generation AI can perform the analysis.

[0083] During analysis, the analysis unit can weight the analysis based on the frequency of user input. For example, the analysis unit can assign a higher weight to worries or goals that the user frequently inputs. The analysis unit can also adjust the weight of the analysis based on the frequency of past input by the user. Furthermore, the analysis unit can set an analysis priority according to the frequency of user input. In this way, more appropriate advice can be generated by weighting the analysis based on the frequency of user input. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's input frequency data to a generation AI, and the generation AI can weight the analysis.

[0084] 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, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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 emotion data into the generation AI, and the generation AI can adjust the display method of the analysis results.

[0085] The analysis unit can take the user's geographical distribution into consideration when performing the analysis. For example, if the user is in a specific area, the analysis unit can prioritize analyzing concerns and goals related to that area. Furthermore, if the user is traveling, the analysis unit can prioritize analyzing concerns and goals related to the travel destination. Furthermore, if the user is at home, the analysis unit can prioritize analyzing concerns and goals related to home or health. This allows for analysis based on the user's geographical distribution to generate more appropriate advice. 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 geographical distribution data into a generation AI, which can then perform the analysis.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related literature. For example, the analysis unit can improve the accuracy of the analysis by referring to the latest research papers related to the user's concerns and goals. The analysis unit can also improve the accuracy of the analysis by referring to past research results related to the user's concerns and goals. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to specialized books related to the user's concerns and goals. In this way, the accuracy of the analysis is improved by referring to related literature. 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 related literature data into a generation AI, which can improve the accuracy of the analysis.

[0087] The analysis unit can perform the analysis taking into account the user's market value. The analysis unit can perform the analysis taking into account market value, for example, based on the user's occupation and skills. The analysis unit can also perform the analysis taking into account market value based on the user's past performance and evaluations. Furthermore, the analysis unit can perform the analysis taking into account market value, taking into account the user's future career path. This allows more appropriate advice to be generated by taking into account the user's market value. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's market value data into a generation AI, and have the generation AI perform the analysis.

[0088] The generation unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can provide advice in gentle language. Furthermore, if the user is relaxed, the generation unit can provide advice with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can provide concise, to-the-point advice. This allows for more appropriate advice to be provided by adjusting the way the advice is presented according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI, which can then adjust the way the advice is presented.

[0089] When generating advice, the generation unit can adjust the level of detail of the advice based on the user's level of importance. For example, if the user has a serious concern, the generation unit can provide detailed advice. Furthermore, if the user has a minor concern, the generation unit can also provide concise advice. Furthermore, the generation unit can adjust the level of detail of the advice based on the user's past feedback. In this way, by adjusting the level of detail of the advice according to the user's level of importance, more appropriate advice can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of importance data into the generation AI, and the generation AI can adjust the level of detail of the advice.

[0090] When generating advice, the generation unit can apply different advice algorithms depending on the user's category. For example, if the user has work-related concerns, the generation unit can apply an advice algorithm specialized for work. Furthermore, if the user has health-related concerns, the generation unit can also apply an advice algorithm specialized for health. Furthermore, if the user has interpersonal concerns, the generation unit can also apply an advice algorithm specialized for interpersonal relationships. In this way, by applying an advice algorithm according to the user's category, more appropriate advice can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's category data into the generation AI, and the generation AI can apply the advice algorithm.

[0091] When generating advice, the generation unit can improve the accuracy of the advice by referring to the user's past advice results. For example, the generation unit analyzes the results of advice the user has received in the past and provides more appropriate advice for similar situations. The generation unit can also adjust the advice algorithm based on the user's past advice results. Furthermore, the generation unit can also refer to the user's past advice results and apply successful advice patterns. In this way, the accuracy of the advice is improved by referring to the user's past advice results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past advice result data into the generation AI, and the generation AI can improve the accuracy of the advice.

[0092] The generation unit can estimate the user's emotions and adjust the length of advice based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the generation unit can provide longer advice with detailed explanations. Furthermore, if the user is in a hurry, the generation unit can provide concise, quick advice. This allows for adjusting the length of advice according to the user's emotions, thereby providing more appropriate advice. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's emotion data into the generation AI, and the generation AI can adjust the length of the advice.

[0093] When generating advice, the generation unit can determine the priority of advice based on the time of user submission. For example, if a user submits an urgent problem, the generation unit can provide advice with priority. Furthermore, if a user submits a long-term goal, the generation unit can provide advice that can be put off. Furthermore, the generation unit can dynamically adjust the priority of advice based on the time of user submission. In this way, more appropriate advice can be provided by determining the priority of advice based on the time of user submission. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user submission time data into the generation AI, and the generation AI can determine the priority of advice.

[0094] When generating advice, the generation unit can adjust the order of advice based on the user's relevance. For example, the generation unit can prioritize providing advice that is most relevant to the user's current situation. The generation unit can also prioritize providing highly relevant advice based on the user's past behavioral patterns. Furthermore, the generation unit can also prioritize providing advice that is most relevant to the user's current concerns or goals. In this way, by adjusting the order of advice based on the user's relevance, more appropriate advice can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user relevance data into the generation AI, and the generation AI can adjust the order of advice.

[0095] When generating advice, the generation unit can adjust the use of technical terminology in the advice according to the user's level of expertise. For example, if the user has technical expertise, the generation unit can provide advice that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can provide easy-to-understand advice that avoids technical terminology. Furthermore, the generation unit can adjust the content of the advice according to the user's level of expertise. In this way, more appropriate advice can be provided by adjusting the content of the advice according to the user's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terminology in the advice.

[0096] The providing unit can estimate the user's emotions and adjust the way in which advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice using gentle language. Furthermore, if the user is relaxed, the providing unit can provide advice including detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide concise, to-the-point advice. This allows for more appropriate advice to be provided by adjusting the way in which advice is provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI, and the generation AI can adjust the way in which advice is provided.

[0097] When providing advice, the providing unit can select the optimal delivery method by referring to the user's past feedback. For example, the providing unit preferentially suggests delivery methods that the user has used favorably in the past. The providing unit can also customize the delivery method based on the user's past feedback. Furthermore, the providing unit can also optimize the delivery procedure by reflecting the user's past feedback. This allows more appropriate advice to be provided by selecting the optimal delivery method based on the user's past feedback. Some or all of the above-described 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 the user's past feedback data into a generation AI, which can select the optimal delivery method.

[0098] When providing advice, the providing unit can customize the content to be provided according to the user's current task. For example, if the user is at work, the providing unit can prioritize providing work-related advice. Also, if the user is on vacation, the providing unit can prioritize providing advice on relaxation and refreshment. Furthermore, if the user is working on a specific project, the providing unit can provide advice related to that project. In this way, by customizing the content to be provided according to the user's current task, more appropriate advice can be provided. 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 the user's current task data into a generating AI, and the generating AI can customize the content to be provided.

[0099] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. For example, if the user provides feedback on the advice provided, the providing unit improves the advice providing method based on that feedback. The providing unit can also analyze the user feedback and optimize the advice providing method. Furthermore, the providing unit can also adjust the advice providing procedure by reflecting the user feedback. In this way, more appropriate advice can be provided by improving the advice providing method based on the user feedback. Some or all of the above-described 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 user feedback data into a generation AI, which can then improve the advice providing method.

[0100] The providing unit can estimate the user's emotions and adjust the order in which advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing advice related to stress reduction. Furthermore, if the user is relaxed, the providing unit can prioritize providing advice related to long-term goals. Furthermore, if the user is in a hurry, the providing unit can prioritize providing advice related to short-term solutions. This allows for adjusting the order in which advice is provided according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned 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 the generation AI can adjust the order in which advice is provided.

[0101] When providing advice, the providing unit can select the optimal delivery method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows more appropriate advice to be provided by selecting the optimal delivery method based on the user's device information. Some or all of the above-described 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 the user's device information into a generation AI, which can select the optimal delivery method.

[0102] When providing advice, the providing unit can make the provided content multilingual according to the user's language setting. The providing unit, for example, automatically sets the language of the advice based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide advice in that language. This allows for more appropriate advice to be provided by providing advice in multiple languages ​​according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's language setting data into a generating AI, which can then provide advice in multiple languages.

[0103] The providing unit can analyze the user's lifestyle rhythm when providing advice and suggest the optimal timing for providing the advice. For example, if the user is a morning person, the providing unit can provide advice in the morning hours. Furthermore, if the user is a night owl, the providing unit can also provide advice in the evening hours. Furthermore, the providing unit can analyze the user's lifestyle rhythm and dynamically adjust the optimal timing for providing the advice. This allows more appropriate advice to be provided by suggesting the optimal timing for providing the advice based on the user's lifestyle rhythm. 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 the user's lifestyle rhythm data into a generating AI, which can then suggest the optimal timing for providing the advice. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs the user's concerns and goals using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit provides the generated advice to the user using the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs the user's concerns and goals using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit provides the generated advice to the user using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision 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 inputs the user's worries and goals using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit provides the generated advice to the user using the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit inputs the user's worries and goals using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates advice based on the analysis results. The provision unit provides the generated advice to the user using the speaker 240 of the robot 414.

[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 automatically suggest related past advice based on the user's input. For example, if the user inputs "I want to reduce work stress," the reception unit can suggest advice based on advice previously provided to users with similar concerns. Also, if the user inputs "I want to live a healthy life," the reception unit can automatically display past health advice. Furthermore, the reception unit can suggest related topics and categories based on the user's input. This allows the user to receive more appropriate advice while referring to past advice.

[0106] The analysis unit can collect related external data based on the user's input and use it for analysis. For example, if the user inputs "I want to reduce work stress," the analysis unit can collect the latest research papers and articles on stress management and use it for analysis. Similarly, if the user inputs "I want to live a healthy life," the analysis unit can collect the latest health-related data and use it for analysis. Furthermore, the analysis unit can collect related statistical data and market research data based on the user's input and use it for analysis. This allows the system to provide more accurate advice to address the user's concerns and goals.

[0107] The generating unit can generate multiple advice options based on the user's input and provide the user with choices. For example, if the user inputs "I want to reduce work stress," the generating unit can generate multiple advice options such as relaxation techniques, time management methods, and ways to improve the work environment. Alternatively, if the user inputs "I want to live a healthy life," the generating unit can generate multiple advice options such as meal plans, exercise plans, and ways to improve sleep. Furthermore, the generating unit can provide a combination of short-term and long-term advice based on the user's input, allowing the user to select the advice that best suits them.

[0108] The providing unit can collect user feedback in real time and evaluate the effectiveness of the advice. For example, after the user implements the advice, the providing unit can provide feedback on the effectiveness of the advice. The providing unit can also improve the content of the advice based on the user feedback. Furthermore, the providing unit can analyze the user feedback and quantitatively evaluate the effectiveness of the advice. This allows the user to receive more appropriate advice while checking the effectiveness of the advice.

[0109] The providing unit can estimate the user's emotions and adjust the timing of providing advice based on the estimated user's emotions. For example, if the user is feeling stressed, advice can be provided at a time when the user is able to relax. Also, if the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice that focuses on the main points can be provided. In this way, more appropriate advice can be provided by adjusting the timing of providing advice according to the user's emotions.

[0110] The reception unit can estimate the user's emotions and provide feedback on the input content based on the estimated user emotions. For example, if the user is feeling stressed, an encouraging message can be displayed in response to the input content. If the user is relaxed, detailed feedback can be provided. Furthermore, if the user is in a hurry, concise feedback that focuses on the main points can be provided. In this way, by providing feedback on the input content according to the user's emotions, a more appropriate input environment can be provided.

[0111] 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, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided.

[0112] The generation unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the advice can be provided in gentle language. If the user is relaxed, the advice can be provided with detailed explanations. Furthermore, if the user is in a hurry, the advice can be provided in a concise and to-the-point manner. In this way, by adjusting the way the advice is expressed according to the user's emotions, more appropriate advice can be provided.

[0113] The providing unit can estimate the user's emotions and adjust the way in which advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the advice can be provided in gentle language. If the user is relaxed, the advice can be provided with detailed explanations. Furthermore, if the user is in a hurry, the advice can be provided in a concise and to-the-point manner. In this way, by adjusting the way in which advice is provided according to the user's emotions, more appropriate advice can be provided.

[0114] The providing unit can collect user feedback in real time and evaluate the effectiveness of the advice. For example, after the user implements the advice, the providing unit can provide feedback on the effectiveness of the advice. The providing unit can also improve the content of the advice based on the user feedback. Furthermore, the providing unit can analyze the user feedback and quantitatively evaluate the effectiveness of the advice. This allows the user to receive more appropriate advice while checking the effectiveness of the advice.

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

[0116] Step 1: The reception unit inputs the user's worries or goals. The user's worries and goals include, but are not limited to, work-related worries, health goals, etc. The reception unit allows the user to input, for example, specific worries or goals they want to achieve. Step 2: The analysis unit analyzes the information input by the reception unit. The analysis is performed by, for example, but not limited to, methods such as text analysis and sentiment analysis. The analysis unit uses, for example, natural language processing technology to understand information about the user's concerns and goals and generate appropriate advice. Step 3: The generator generates advice based on the information analyzed by the analyzer. The advice may be provided in, for example, a sentence format or a list format, but is not limited to these examples. The generator may provide, for example, specific methods for reducing work stress, or advice on diet and exercise for living a healthy lifestyle. Step 4: The providing unit provides the advice generated by the generating unit to the user. The advice may be provided by, for example, but not limited to, an email or an app notification. The providing unit provides the generated advice to the user, for example.

[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 a 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 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 system comprising: a reception unit for inputting a user's concerns or goals; an analysis unit for analyzing the information input by the reception unit; a generation unit for generating advice based on the information analyzed by the analysis unit; and a provision unit for providing the advice generated by the generation unit.

2. The system according to claim 1 , wherein the reception unit inputs a user's worries or goals that the user wants to achieve.

3. The system of claim 1 , wherein the analysis unit understands information about a user's concerns or goals and generates advice.

4. The system according to claim 1 , wherein the generating unit provides advice on methods for reducing work stress or diet or exercise for living a healthy life.

5. The providing unit Providing generated advice to the user 2. The system of claim 1.

6. The reception unit The system estimates the user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

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

8. The reception unit Filter input based on the user's current situation or environment 2. The system of claim 1.

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