System

Abstract

An object of a system according to an embodiment is to provide personalized advice based on information of a user and to improve the advice by reflecting the feedback.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and an improvement unit. The collection unit collects information of a user. The analysis unit analyzes the information collected by the collection unit. The providing unit provides advice based on the analysis result obtained by the analyzing unit. The feedback unit receives feedback on the advice provided by the providing unit. The improvement unit improves the advice based on the feedback received by the feedback unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the problem that they do not adequately provide personalized advice based on user information and do not adequately implement the process of improving the system by reflecting that feedback.

[0005] The system according to the embodiment aims to provide personalized advice based on user information and to improve the advice by reflecting the user's feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and an improvement unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The feedback unit receives feedback on the advice provided by the provision unit. The improvement unit improves the advice based on the feedback received by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized advice based on the user's information and improve the advice by reflecting the user's feedback. [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 innovative platform according to an embodiment of the present invention provides a personalized "buddy" tailored to a user's lifestyle, health status, hobbies, learning, and business goals. This system enriches and streamlines the user's life by collecting user information, analyzing it with a generation AI, providing advice, receiving feedback, and improving the advice. For example, a user inputs information such as their daily diet, exercise habits, hobby activities, learning progress, and business goals. This information is then input into the generation AI. The generation AI then analyzes the collected information and provides optimal advice and suggestions to the user. For example, the generation AI may provide appropriate diet and exercise advice based on their health status, suggest events and activities related to their hobbies, create a study plan based on their learning progress, or suggest strategies for achieving their business goals. Furthermore, the generation AI receives user feedback and continuously improves the advice and suggestions it provides. For example, a user can provide feedback on the advice provided, and the generation AI can use that feedback to improve the accuracy of the advice. This allows the innovative platform to provide a wide range of services across the user's life. For example, the system can enrich and streamline the user's life in various areas, such as health management, hobby support, learning assistance, and business consulting. Furthermore, by using generative AI, it is possible to add new functions according to user needs and evolve the platform. For example, adding new health management functions, learning support functions, business strategy proposal functions, etc. can further enrich users' lives.

[0029] An innovative platform according to an embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and an improvement unit. The collection unit collects user information. The user information may include, but is not limited to, lifestyle, health status, hobbies, learning, and business goals. For example, the collection unit allows the user to input information such as daily dietary habits, exercise habits, hobby activities, learning progress, and business goals. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI may provide appropriate diet and exercise advice based on the user's health status. The analysis unit may also suggest events and activities related to hobbies. The analysis unit may also create a study plan based on the user's learning progress. The analysis unit may also suggest strategies for achieving business goals. The provision unit provides advice based on the analysis results obtained by the analysis unit. The advice may be provided in the form of, but is not limited to, a text message, audio instructions, or a video guide. For example, the providing unit provides advice on appropriate diet and exercise based on the user's health status. The providing unit can also suggest events and activities related to hobbies. The providing unit can also create a study plan based on the user's learning progress. The providing unit can also suggest strategies toward business goals. The feedback unit receives feedback on the advice provided by the providing unit. The feedback is received in the form of, for example, but not limited to, a user's rating, comments, behavioral data, etc. For example, the feedback unit can provide feedback on the advice provided by the user. The improvement unit improves the advice based on the feedback received by the feedback unit. The improvement is performed by, for example, but not limited to, adjusting the algorithm, updating the advice content, etc. For example, the improvement unit improves the accuracy of the advice based on the user's feedback. As a result, the innovative platform according to the embodiment can enrich and streamline the user's life.

[0030] The collection unit can collect at least one of the user's lifestyle, health status, hobbies, learning, and business goals. For example, the collection unit allows the user to input daily dietary habits, exercise habits, hobby activities, learning progress, business goals, etc. Examples of lifestyle include, but are not limited to, eating habits, exercise habits, and sleep patterns. Examples of health include, but are not limited to, weight, blood pressure, and heart rate. Examples of hobbies include, but are not limited to, sports, music, and reading. Examples of learning include, but are not limited to, study subjects, study methods, and learning goals. Examples of business goals include, but are not limited to, sales targets, marketing strategies, and project plans. By collecting a variety of user information, more personalized advice can be provided. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the information input by the user into a generation AI and cause the generation AI to collect the information.

[0031] The analysis unit can analyze the collected information and provide appropriate advice or suggestions to the user. The analysis unit can analyze the collected information using, for example, a generation AI. The analysis can be performed using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. For example, the generation AI can provide appropriate dietary and exercise advice based on the user's health status. The analysis unit can also suggest events and activities related to hobbies. The analysis unit can also create a study plan based on the user's learning progress. The analysis unit can also suggest strategies toward business goals. In this way, by analyzing the collected information, optimal advice and suggestions can be provided to the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI and have the generation AI perform the analysis.

[0032] The providing unit can provide appropriate diet or exercise advice based on the health condition based on the analysis results. The providing unit, for example, provides appropriate diet or exercise advice based on the health condition based on the analysis results. Appropriate diet or exercise advice based on the health condition includes, but is not limited to, calorie-restricted diets and strength training plans. For example, the providing unit can suggest a calorie-restricted diet menu based on the user's health condition. The providing unit can also suggest a strength training plan based on the user's health condition. The providing unit can also suggest an aerobic exercise plan based on the user's health condition. This supports the user's health by providing appropriate advice based on the health condition. 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 analysis results to a generating AI and cause the generating AI to generate advice.

[0033] The providing unit can suggest events and activities related to the hobby based on the analysis results. The providing unit, for example, suggests events and activities related to the hobby based on the analysis results. Hobby-related events and activities include, but are not limited to, concerts, sporting events, workshops, etc. For example, the providing unit can suggest concert information based on the user's hobbies. The providing unit can also suggest sporting event information based on the user's hobbies. The providing unit can also suggest workshop information based on the user's hobbies. This enriches the user's life by suggesting events and activities related to the hobby. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to suggest events and activities.

[0034] The providing unit can create a study plan according to the learning progress based on the analysis results. The providing unit, for example, creates a study plan according to the learning progress based on the analysis results. The study plan according to the learning progress includes, for example, weekly study goals and a review schedule, but is not limited to these examples. For example, the providing unit sets weekly study goals based on the user's learning progress. The providing unit can also set a review schedule based on the user's learning progress. The providing unit can also set study priorities based on the user's learning progress. This makes the user's learning more efficient by creating a study plan according to the learning progress. 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 inputs the analysis results to a generating AI and causes the generating AI to create a study plan.

[0035] The provision unit can propose a strategy toward the business goal based on the analysis results. The provision unit, for example, proposes a strategy toward the business goal based on the analysis results. Strategies toward the business goal include, but are not limited to, a marketing strategy, a sales strategy, and a project management strategy. For example, the provision unit proposes a marketing strategy based on the user's business goal. The provision unit can also propose a sales strategy based on the user's business goal. The provision unit can also propose a project management strategy based on the user's business goal. This supports the user's business by proposing a strategy toward the business goal. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the analysis results to a generation AI and cause the generation AI to propose a business strategy.

[0036] The feedback unit can receive feedback from the user and provide it to the improvement unit. The feedback unit, for example, receives feedback on the advice provided by the providing unit. The feedback is received in the form of, for example, a user's rating, comments, behavioral data, etc., but is not limited to these examples. For example, the feedback unit can provide feedback on the advice provided by the user. In this way, by receiving feedback from the user, the accuracy of the advice is improved. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback to a generation AI and cause the generation AI to analyze the feedback.

[0037] The improvement unit can improve the accuracy of the advice based on the feedback. The improvement unit improves the advice based on, for example, feedback received by the feedback unit. The improvement can be performed by, for example, adjusting an algorithm, updating the advice content, or the like, but is not limited to these examples. For example, the improvement unit improves the accuracy of the advice based on the user's feedback. By improving the accuracy of the advice based on the feedback, more appropriate advice can be provided to the user. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the feedback to the generation AI and cause the generation AI to improve the advice.

[0038] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal information collection method. Past behavioral history includes, but is not limited to, past activity logs and behavioral pattern analysis. For example, the collection unit prioritizes selecting an information collection method that the user has frequently used in the past. The collection unit can also analyze the user's behavioral patterns and suggest the most efficient information collection method. The collection unit can also select the optimal information collection method for a specific time period from the user's past behavioral history. In this way, the optimal information collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history data into a generation AI and cause the generation AI to select an information collection method.

[0039] The collection unit may filter information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit may filter information based on the user's current living situation and areas of interest when collecting information. Examples of current living situations include, but are not limited to, work status, family status, and health status. Examples of areas of interest include, but are not limited to, hobbies, study topics, and business fields. For example, the collection unit may prioritize collecting information related to a project the user is currently working on. The collection unit may also filter and collect related news and articles based on the user's areas of interest. The collection unit may also filter and collect necessary information according to the user's living situation (e.g., health status, work status). This allows for more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the user's living situation and areas of interest to a generation AI and have the generation AI filter the information.

[0040] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. Input methods include, but are not limited to, voice input, text input, and image input. For example, the collection unit collects information using voice recognition technology when the user uses voice input. Furthermore, the collection unit can collect information using text analysis technology when the user uses text input. Furthermore, the collection unit can collect information using image recognition technology when the user uses image input. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and cause the generation AI to select the optimal collection means.

[0041] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, the collection unit prioritizes collecting event information related to the user's current location. The collection unit can also prioritize collecting information about nearby stores and services based on the user's geographical location. The collection unit can also prioritize collecting local news and weather information based on the user's location information. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without using, AI. For example, the collection unit can input the user's geographical location data into a generation AI and cause the generation AI to collect information.

[0042] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. Social media activities include, but are not limited to, post content, the number of likes, and the number of followers. For example, the collection unit can analyze the post content of accounts the user follows on social media and collect related information. The collection unit can also collect information that the user is likely to be interested in based on the user's social media activity history. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect information.

[0043] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. Past feedback includes, but is not limited to, user ratings, comments, and behavioral data. For example, the collection unit adjusts the information collection method based on feedback provided by the user in the past. The collection unit can also prioritize the use of a preferred information collection method based on the user's past feedback. The collection unit can also improve the accuracy of information collection by reflecting the user's feedback. In this way, the accuracy of information collection is improved by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information collection method.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, uses a generation AI to adjust the level of detail of the analysis based on the importance of the information during analysis. The importance of the information includes, but is not limited to, the user's level of interest and the urgency of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit, for example, uses a generation AI to apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, health information, learning information, and business information. For example, the analysis unit applies a health analysis algorithm to health information. The analysis unit can also apply a learning analysis algorithm to learning information. The analysis unit can also apply a business analysis algorithm to business information. This allows for applying different analysis algorithms depending on the category of information, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0046] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by, for example, using a generation AI. Past analysis results include, but are not limited to, past reports, analysis logs, and user feedback. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also improve the analysis method by referring to the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0047] The analysis unit can determine the analysis priority based on the time of information submission during analysis. The analysis unit, for example, uses a generation AI to determine the analysis priority based on the time of information submission during analysis. The time of information submission includes, for example, the submission date and time, the submission frequency, and the submission timing, but is not limited to these examples. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the analysis priority based on the time of information submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, uses a generation AI to adjust the order of analysis based on the relevance of information during analysis. The relevance of information includes, for example, co-occurrence frequency, correlation, and user interest level, but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0049] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit, for example, uses a generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past feedback. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simpler terms. The analysis unit can also adjust the way the analysis is presented according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms in the analysis.

[0050] The providing unit can adjust the level of detail of the advice based on the importance of the information when providing the advice. The providing unit, for example, uses a generation AI to adjust the level of detail of the advice based on the importance of the information when providing the advice. The importance of the information includes, but is not limited to, the user's level of interest and the urgency of the information. For example, the providing unit provides detailed advice for information with high importance. The providing unit can also provide concise advice for information with low importance. The providing unit can also determine the priority of the advice according to the importance of the information. This enables efficient advice by adjusting the level of detail of the advice based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0051] The providing unit can apply different advice algorithms depending on the category of information when providing advice. The providing unit, for example, uses a generation AI to apply different advice algorithms depending on the category of information when providing advice. Information categories include, but are not limited to, health information, learning information, and business information. For example, the providing unit applies a health advice algorithm to health information. The providing unit can also apply a learning advice algorithm to learning information. The providing unit can also apply a business advice algorithm to business information. In this way, by applying different advice algorithms depending on the category of information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0052] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. The providing unit, for example, uses a generation AI to improve the accuracy of advice by referring to the user's past advice results when providing advice. Past advice results include, but are not limited to, past reports, advice logs, and user feedback. For example, the providing unit adjusts the advice algorithm based on the user's past advice results. The providing unit can also improve the accuracy of advice from the user's past advice results. The providing unit can also improve the advice method by referring to the user's past advice results. In this way, the accuracy of advice is improved by referring to the user's past advice results. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of advice.

[0053] The providing unit can determine the priority of advice based on the time of information submission when providing advice. The providing unit, for example, uses a generation AI to determine the priority of advice based on the time of information submission when providing advice. The time of information submission includes, for example, the submission date and time, the submission frequency, and the submission timing, but is not limited to these examples. For example, the providing unit prioritizes the most recent information in the advice. The providing unit can also provide advice for information that was submitted earlier at a later date. The providing unit can also adjust the advice schedule based on the submission time. This enables efficient advice by determining the priority of advice based on the time of information submission. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information submission time data into the generation AI and cause the generation AI to determine the priority of advice.

[0054] The providing unit can adjust the order of advice based on the relevance of information when providing advice. The providing unit, for example, uses a generation AI to adjust the order of advice based on the relevance of information when providing advice. The relevance of information includes, for example, co-occurrence frequency, correlation, and user interest level, but is not limited to these examples. For example, the providing unit prioritizes highly relevant information in the advice. The providing unit can also provide advice for less relevant information at a later date. The providing unit can also adjust the order of advice based on the relevance of information. This enables efficient advice by adjusting the order of advice based on the relevance of information. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of advice.

[0055] The providing unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. The providing unit, for example, uses a generation AI to adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past feedback. For example, the providing unit can provide advice that uses a lot of technical terms if the user has technical knowledge. Furthermore, the providing unit can also provide advice in simple language if the user does not have technical knowledge. Furthermore, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. This allows for more appropriate advice to be provided by adjusting the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms in the advice.

[0056] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. The feedback unit, for example, uses a generation AI to select the optimal collection method by referring to the user's past feedback history when collecting feedback. Past feedback history includes, but is not limited to, past ratings, comments, and behavioral data. For example, the feedback unit preferentially uses the form of feedback previously provided by the user. The feedback unit can also select the optimal collection method from the user's past feedback history. The feedback unit can also improve the accuracy of feedback collection based on the user's past feedback history. In this way, the optimal feedback collection method can be selected by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI and cause the generation AI to select the collection method.

[0057] The feedback unit can customize the collection means based on the user's current living situation when collecting feedback. The feedback unit, for example, uses a generation AI to customize the collection means based on the user's current living situation when collecting feedback. The current living situation includes, but is not limited to, work status, family status, and health status. For example, the feedback unit can provide a brief feedback form when the user is busy. The feedback unit can also provide a detailed feedback form when the user is relaxed. The feedback unit can also adjust the feedback collection means according to the user's living situation. This allows more appropriate feedback to be collected by customizing the collection means based on the user's current living situation. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's living situation data into the generation AI and cause the generation AI to customize the collection means.

[0058] The feedback unit can select the optimal collection method by taking into account the user's geographical location information when collecting feedback. The feedback unit, for example, uses a generation AI to select the optimal collection method by taking into account the user's geographical location information when collecting feedback. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, the feedback unit prioritizes collecting feedback related to the user's current location. The feedback unit can also collect feedback about nearby stores and services based on the user's geographical location. The feedback unit can also collect feedback about local events and services based on the user's location information. This allows the optimal feedback collection method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's geographical location data into the generation AI and cause the generation AI to select the collection method.

[0059] The feedback unit can analyze the user's social media activity and suggest collection means when collecting feedback. The feedback unit can, for example, use a generation AI to analyze the user's social media activity and suggest collection means when collecting feedback. Social media activity includes, but is not limited to, post content, the number of likes, and the number of followers. For example, the feedback unit can analyze the post content of accounts the user follows on social media to collect relevant feedback. The feedback unit can also collect feedback that is likely to be of interest to the user based on the user's social media activity history. The feedback unit can also collect relevant feedback based on the activity of the user's friends on social media. In this way, relevant feedback can be efficiently collected by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of collection means.

[0060] The improvement unit can analyze the user's past feedback and select the optimal improvement method when making an improvement. The improvement unit, for example, uses a generation AI to analyze the user's past feedback and select the optimal improvement method when making an improvement. Past feedback includes, for example, user ratings, comments, behavioral data, etc., but is not limited to these examples. For example, the improvement unit adjusts the improvement method based on the user's past feedback. The improvement unit can also select the optimal improvement method from the user's past feedback. The improvement unit can also improve the improvement method by referring to the user's past feedback. In this way, the optimal improvement method can be selected by analyzing the user's past feedback. Some or all of the above-mentioned processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's past feedback data into the generation AI and have the generation AI select an improvement method.

[0061] The improvement unit can customize the improvement measures based on the user's current living situation during improvement. The improvement unit, for example, uses a generation AI to customize the improvement measures based on the user's current living situation during improvement. Examples of current living situations include, but are not limited to, work status, family status, and health status. For example, the improvement unit can provide a concise improvement measure when the user is busy. The improvement unit can also provide a detailed improvement measure when the user is relaxed. The improvement unit can also adjust the improvement measures according to the user's living situation. This allows for customizing the improvement measures based on the user's current living situation, thereby providing a more appropriate improvement method. Some or all of the above-described processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's living situation data into the generation AI and have the generation AI customize the improvement measures.

[0062] The improvement unit can improve the improvement method by reflecting user feedback during improvement. The improvement unit, for example, uses a generation AI to improve the improvement method by reflecting user feedback during improvement. Feedback includes, for example, user ratings, comments, behavioral data, etc., but is not limited to these examples. For example, the improvement unit adjusts the improvement method based on user feedback. The improvement unit can also select an optimal improvement method from user feedback. The improvement unit can also improve the improvement method by referring to user feedback. In this way, the improvement method is continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user feedback data into the generation AI and have the generation AI execute improvements to the improvement method.

[0063] The improvement unit can select the optimal improvement method by taking into account the user's geographical location information when making an improvement. The improvement unit, for example, uses a generation AI to select the optimal improvement method by taking into account the user's geographical location information when making an improvement. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, the improvement unit prioritizes providing improvement methods related to the user's current location. The improvement unit can also provide improvement methods related to nearby stores and services based on the user's geographical location. The improvement unit can also provide improvement methods related to local events and services based on the user's location information. This allows the optimal improvement method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the improvement unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's geographical location data into the generation AI and cause the generation AI to select an improvement method.

[0064] The improvement unit can analyze the user's social media activity and suggest improvement measures during improvement. The improvement unit, for example, uses a generation AI to analyze the user's social media activity and suggest improvement measures during improvement. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the improvement unit analyzes the content of posts from accounts the user follows on social media and suggests relevant improvement measures. The improvement unit can also suggest improvement measures that the user might be interested in based on the user's social media activity history. The improvement unit can also suggest relevant improvement measures based on the activities of the user's friends on social media. In this way, relevant improvement measures can be efficiently suggested by analyzing the user's social media activity. Some or all of the above-described processing in the improvement unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of improvement measures.

[0065] The improvement unit can customize the improvement method by reflecting the user's past feedback when making an improvement. The improvement unit, for example, uses a generation AI to customize the improvement method by reflecting the user's past feedback when making an improvement. Past feedback includes, but is not limited to, user ratings, comments, and behavioral data. For example, the improvement unit adjusts the improvement method based on the user's past feedback. The improvement unit can also select an optimal improvement method from the user's past feedback. The improvement unit can also improve the improvement method by referring to the user's past feedback. In this way, the improvement method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's past feedback data into the generation AI and have the generation AI customize the improvement method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The analysis unit can analyze the user's past behavioral history and select the optimal analysis algorithm. For example, it can prioritize the selection of an analysis algorithm that the user has frequently used in the past. The analysis unit can also analyze the user's behavioral patterns and suggest the most efficient analysis algorithm. The analysis unit can also select the optimal analysis algorithm for a specific time period from the user's past behavioral history. In this way, the optimal analysis algorithm can be selected by analyzing the user's past behavioral history. 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 behavioral history data into the generation AI and have the generation AI select the analysis algorithm.

[0068] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, event information related to the user's current location can be prioritized. The providing unit can also prioritize providing information about nearby stores and services based on the user's geographical location. The providing unit can also prioritize providing local news and weather information based on the user's location information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to a generation AI and cause the generation AI to provide information.

[0069] The feedback unit can analyze the user's social media activity and collect relevant feedback. For example, it can analyze the content posted by accounts the user follows on social media and collect relevant feedback. The feedback unit can also collect feedback that is likely to be of interest to the user based on the user's social media activity history. The feedback unit can also collect relevant feedback by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant feedback can be efficiently collected. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media data into a generation AI and cause the generation AI to collect feedback.

[0070] The improvement unit can analyze the user's past feedback and select the optimal improvement method. For example, the improvement unit adjusts the improvement method based on feedback provided by the user in the past. The improvement unit can also select the optimal improvement method from the user's past feedback. The improvement unit can also improve the improvement method by referring to the user's past feedback. In this way, the optimal improvement method can be selected by analyzing the user's past feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's past feedback data into the generation AI and have the generation AI select an improvement method.

[0071] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting information related to a project the user is currently working on. The collection unit can also filter and collect related news and articles based on the user's areas of interest. The collection unit can also filter and collect necessary information according to the user's living situation (e.g., health condition, work situation). This allows more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI filter the information.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects user information, including information about the user's lifestyle, health status, hobbies, learning, and business goals. The collection unit allows the user to input information about their daily diet, exercise habits, hobby activities, learning progress, and business goals. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI may provide appropriate dietary and exercise advice based on the user's health status, suggest events and activities related to hobbies, create a study plan based on the user's learning progress, and propose strategies to achieve business goals. Step 3: The provider provides advice based on the analysis results obtained by the analyzer. The advice is provided in the form of text messages, audio instructions, video guides, etc. Examples of advice include advice on appropriate diet and exercise based on health status, suggestions for events and activities related to hobbies, study plans based on learning progress, and strategy suggestions for business goals. Step 4: The feedback unit receives feedback on the advice provided by the providing unit. The feedback is received in the form of a user's rating, comment, behavioral data, etc. For example, the user can provide feedback on the provided advice. Step 5: The improvement unit improves the advice based on the feedback received by the feedback unit. Improvements are made by adjusting the algorithm, updating the advice content, etc. For example, the accuracy of advice can be improved based on user feedback.

[0074] (Example 2) An innovative platform according to an embodiment of the present invention provides a personalized "buddy" tailored to a user's lifestyle, health status, hobbies, learning, and business goals. This system enriches and streamlines the user's life by collecting user information, analyzing it with a generation AI, providing advice, receiving feedback, and improving the advice. For example, a user inputs information such as their daily diet, exercise habits, hobby activities, learning progress, and business goals. This information is then input into the generation AI. The generation AI then analyzes the collected information and provides optimal advice and suggestions to the user. For example, the generation AI may provide appropriate diet and exercise advice based on their health status, suggest events and activities related to their hobbies, create a study plan based on their learning progress, or suggest strategies for achieving their business goals. Furthermore, the generation AI receives user feedback and continuously improves the advice and suggestions it provides. For example, a user can provide feedback on the advice provided, and the generation AI can use that feedback to improve the accuracy of the advice. This allows the innovative platform to provide a wide range of services across the user's life. For example, the system can enrich and streamline the user's life in various areas, such as health management, hobby support, learning assistance, and business consulting. Furthermore, by using generative AI, it is possible to add new functions according to user needs and evolve the platform. For example, adding new health management functions, learning support functions, business strategy proposal functions, etc. can further enrich users' lives.

[0075] An innovative platform according to an embodiment includes a collection unit, an analysis unit, a provision unit, a feedback unit, and an improvement unit. The collection unit collects user information. The user information may include, but is not limited to, lifestyle, health status, hobbies, learning, and business goals. For example, the collection unit allows the user to input information such as daily dietary habits, exercise habits, hobby activities, learning progress, and business goals. The analysis unit uses a generation AI to analyze the information collected by the collection unit. The analysis may be performed using, but is not limited to, methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI may provide appropriate diet and exercise advice based on the user's health status. The analysis unit may also suggest events and activities related to hobbies. The analysis unit may also create a study plan based on the user's learning progress. The analysis unit may also suggest strategies for achieving business goals. The provision unit provides advice based on the analysis results obtained by the analysis unit. The advice may be provided in the form of, but is not limited to, a text message, audio instructions, or a video guide. For example, the providing unit provides advice on appropriate diet and exercise based on the user's health status. The providing unit can also suggest events and activities related to hobbies. The providing unit can also create a study plan based on the user's learning progress. The providing unit can also suggest strategies toward business goals. The feedback unit receives feedback on the advice provided by the providing unit. The feedback is received in the form of, for example, but not limited to, a user's rating, comments, behavioral data, etc. For example, the feedback unit can provide feedback on the advice provided by the user. The improvement unit improves the advice based on the feedback received by the feedback unit. The improvement is performed by, for example, but not limited to, adjusting the algorithm, updating the advice content, etc. For example, the improvement unit improves the accuracy of the advice based on the user's feedback. As a result, the innovative platform according to the embodiment can enrich and streamline the user's life.

[0076] The collection unit can collect at least one of the user's lifestyle, health status, hobbies, learning, and business goals. For example, the collection unit allows the user to input daily dietary habits, exercise habits, hobby activities, learning progress, business goals, etc. Examples of lifestyle include, but are not limited to, eating habits, exercise habits, and sleep patterns. Examples of health include, but are not limited to, weight, blood pressure, and heart rate. Examples of hobbies include, but are not limited to, sports, music, and reading. Examples of learning include, but are not limited to, study subjects, study methods, and learning goals. Examples of business goals include, but are not limited to, sales targets, marketing strategies, and project plans. By collecting a variety of user information, more personalized advice can be provided. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the information input by the user into a generation AI and cause the generation AI to collect the information.

[0077] The analysis unit can analyze the collected information and provide appropriate advice or suggestions to the user. The analysis unit can analyze the collected information using, for example, a generation AI. The analysis can be performed using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. For example, the generation AI can provide appropriate dietary and exercise advice based on the user's health status. The analysis unit can also suggest events and activities related to hobbies. The analysis unit can also create a study plan based on the user's learning progress. The analysis unit can also suggest strategies toward business goals. In this way, by analyzing the collected information, optimal advice and suggestions can be provided to the user. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the collected information into the generation AI and have the generation AI perform the analysis.

[0078] The providing unit can provide appropriate diet or exercise advice based on the health condition based on the analysis results. The providing unit, for example, provides appropriate diet or exercise advice based on the health condition based on the analysis results. Appropriate diet or exercise advice based on the health condition includes, but is not limited to, calorie-restricted diets and strength training plans. For example, the providing unit can suggest a calorie-restricted diet menu based on the user's health condition. The providing unit can also suggest a strength training plan based on the user's health condition. The providing unit can also suggest an aerobic exercise plan based on the user's health condition. This supports the user's health by providing appropriate advice based on the health condition. 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 analysis results to a generating AI and cause the generating AI to generate advice.

[0079] The providing unit can suggest events and activities related to the hobby based on the analysis results. The providing unit, for example, suggests events and activities related to the hobby based on the analysis results. Hobby-related events and activities include, but are not limited to, concerts, sporting events, workshops, etc. For example, the providing unit can suggest concert information based on the user's hobbies. The providing unit can also suggest sporting event information based on the user's hobbies. The providing unit can also suggest workshop information based on the user's hobbies. This enriches the user's life by suggesting events and activities related to the hobby. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the analysis results to a generation AI and cause the generation AI to suggest events and activities.

[0080] The providing unit can create a study plan according to the learning progress based on the analysis results. The providing unit, for example, creates a study plan according to the learning progress based on the analysis results. The study plan according to the learning progress includes, for example, weekly study goals and a review schedule, but is not limited to these examples. For example, the providing unit sets weekly study goals based on the user's learning progress. The providing unit can also set a review schedule based on the user's learning progress. The providing unit can also set study priorities based on the user's learning progress. This makes the user's learning more efficient by creating a study plan according to the learning progress. 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 inputs the analysis results to a generating AI and causes the generating AI to create a study plan.

[0081] The provision unit can propose a strategy toward the business goal based on the analysis results. The provision unit, for example, proposes a strategy toward the business goal based on the analysis results. Strategies toward the business goal include, but are not limited to, a marketing strategy, a sales strategy, and a project management strategy. For example, the provision unit proposes a marketing strategy based on the user's business goal. The provision unit can also propose a sales strategy based on the user's business goal. The provision unit can also propose a project management strategy based on the user's business goal. This supports the user's business by proposing a strategy toward the business goal. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the analysis results to a generation AI and cause the generation AI to propose a business strategy.

[0082] The feedback unit can receive feedback from the user and provide it to the improvement unit. The feedback unit, for example, receives feedback on the advice provided by the providing unit. The feedback is received in the form of, for example, a user's rating, comments, behavioral data, etc., but is not limited to these examples. For example, the feedback unit can provide feedback on the advice provided by the user. In this way, by receiving feedback from the user, the accuracy of the advice is improved. Some or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's feedback to a generation AI and cause the generation AI to analyze the feedback.

[0083] The improvement unit can improve the accuracy of the advice based on the feedback. The improvement unit improves the advice based on, for example, feedback received by the feedback unit. The improvement can be performed by, for example, adjusting an algorithm, updating the advice content, or the like, but is not limited to these examples. For example, the improvement unit improves the accuracy of the advice based on the user's feedback. By improving the accuracy of the advice based on the feedback, more appropriate advice can be provided to the user. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the feedback to the generation AI and cause the generation AI to improve the advice.

[0084] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the collection unit collects information during a relaxing time. If the user is excited, the collection unit can immediately start collecting information and acquire data in real time. If the user is tired, the collection unit can collect information after a rest. This allows information to be collected at a more appropriate time by adjusting the timing of information collection according to the user's emotions. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the timing of information collection.

[0085] The collection unit can analyze the user's past behavioral history and select the optimal information collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal information collection method. Past behavioral history includes, but is not limited to, past activity logs and behavioral pattern analysis. For example, the collection unit prioritizes selecting an information collection method that the user has frequently used in the past. The collection unit can also analyze the user's behavioral patterns and suggest the most efficient information collection method. The collection unit can also select the optimal information collection method for a specific time period from the user's past behavioral history. In this way, the optimal information collection method can be selected by analyzing the user's past behavioral history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past behavioral history data into a generation AI and cause the generation AI to select an information collection method.

[0086] The collection unit may filter information based on the user's current living situation and areas of interest when collecting information. For example, the collection unit may filter information based on the user's current living situation and areas of interest when collecting information. Examples of current living situations include, but are not limited to, work status, family status, and health status. Examples of areas of interest include, but are not limited to, hobbies, study topics, and business fields. For example, the collection unit may prioritize collecting information related to a project the user is currently working on. The collection unit may also filter and collect related news and articles based on the user's areas of interest. The collection unit may also filter and collect necessary information according to the user's living situation (e.g., health status, work status). This allows for more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input data on the user's living situation and areas of interest to a generation AI and have the generation AI filter the information.

[0087] The collection unit can select the optimal collection means depending on the user's input method when collecting information. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting information. Input methods include, but are not limited to, voice input, text input, and image input. For example, the collection unit collects information using voice recognition technology when the user uses voice input. Furthermore, the collection unit can collect information using text analysis technology when the user uses text input. Furthermore, the collection unit can collect information using image recognition technology when the user uses image input. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and cause the generation AI to select the optimal collection means.

[0088] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of information to be collected based on the estimated user emotions. Specific criteria for determining the priority of information include, but are not limited to, importance, urgency, and relevance. For example, if the user is stressed, the collection unit can prioritize collecting information that helps the user relax. Furthermore, if the user is excited, the collection unit can prioritize collecting information that piques the user's interest. Furthermore, if the user is tired, the collection unit can prioritize collecting information that helps the user refresh. Thus, by determining the priority of information based on the user's emotions, more appropriate information can be preferentially collected. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of information.

[0089] The collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information when collecting information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information when collecting information. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, the collection unit prioritizes collecting event information related to the user's current location. The collection unit can also prioritize collecting information about nearby stores and services based on the user's geographical location. The collection unit can also prioritize collecting local news and weather information based on the user's location information. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing by the collection unit may be performed using, or without using, AI. For example, the collection unit can input the user's geographical location data into a generation AI and cause the generation AI to collect information.

[0090] The collection unit can analyze the user's social media activities and collect related information when collecting information. For example, the collection unit can analyze the user's social media activities and collect related information when collecting information. Social media activities include, but are not limited to, post content, the number of likes, and the number of followers. For example, the collection unit can analyze the post content of accounts the user follows on social media and collect related information. The collection unit can also collect information that the user is likely to be interested in based on the user's social media activity history. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect information.

[0091] The collection unit can customize the collection method by reflecting the user's past feedback when collecting information. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting information. Past feedback includes, but is not limited to, user ratings, comments, and behavioral data. For example, the collection unit adjusts the information collection method based on feedback provided by the user in the past. The collection unit can also prioritize the use of a preferred information collection method based on the user's past feedback. The collection unit can also improve the accuracy of information collection by reflecting the user's feedback. In this way, the accuracy of information collection is improved by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the information collection method.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and adjust the presentation method of the analysis based on the estimated user's emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. The analysis unit can also provide visually appealing analysis results when the user is excited. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during analysis. The analysis unit, for example, uses a generation AI to adjust the level of detail of the analysis based on the importance of the information during analysis. The importance of the information includes, but is not limited to, the user's level of interest and the urgency of the information. For example, the analysis unit performs a detailed analysis of information with high importance. The analysis unit can also perform a concise analysis of information with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0094] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. The analysis unit, for example, uses a generation AI to apply different analysis algorithms depending on the category of information during analysis. Information categories include, but are not limited to, health information, learning information, and business information. For example, the analysis unit applies a health analysis algorithm to health information. The analysis unit can also apply a learning analysis algorithm to learning information. The analysis unit can also apply a business analysis algorithm to business information. This allows for applying different analysis algorithms depending on the category of information, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0095] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by, for example, using a generation AI. Past analysis results include, but are not limited to, past reports, analysis logs, and user feedback. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also improve the analysis method by referring to the user's past analysis results. This improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and adjust the length of the analysis based on the estimated user emotions. Specific criteria for adjusting the length of the analysis include, but are not limited to, short-term analysis, long-term analysis, and real-time analysis. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. This allows for adjusting the length of the analysis based on the user's emotions to provide a more appropriate analysis result. Some or all of the above-described processing in the analysis unit can be performed using, or without, the generation AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0097] The analysis unit can determine the analysis priority based on the time of information submission during analysis. The analysis unit, for example, uses a generation AI to determine the analysis priority based on the time of information submission during analysis. The time of information submission includes, for example, the submission date and time, the submission frequency, and the submission timing, but is not limited to these examples. For example, the analysis unit prioritizes analysis of the most recent information. The analysis unit can also postpone analysis of information submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This enables efficient analysis by determining the analysis priority based on the time of information submission. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input information submission time data into the generation AI and have the generation AI determine the analysis priority.

[0098] The analysis unit can adjust the order of analysis based on the relevance of information during analysis. The analysis unit, for example, uses a generation AI to adjust the order of analysis based on the relevance of information during analysis. The relevance of information includes, for example, co-occurrence frequency, correlation, and user interest level, but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant information. The analysis unit can also postpone analysis of less relevant information. The analysis unit can also adjust the order of analysis based on the relevance of information. This enables efficient analysis by adjusting the order of analysis based on the relevance of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input information relevance data to the generation AI and have the generation AI adjust the order of analysis.

[0099] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit, for example, uses a generation AI to adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past feedback. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simpler terms. The analysis unit can also adjust the way the analysis is presented according to the user's level of expertise. This allows for more appropriate analysis results to be provided by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terms in the analysis.

[0100] The providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. The providing unit can estimate the user's emotions using, for example, a generation AI and adjust the way the advice is expressed based on the estimated user's emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the providing unit can provide detailed advice when the user is relaxed. Furthermore, the providing unit can provide concise advice that focuses on the main points when the user is in a hurry. Furthermore, the providing unit can provide visually appealing advice when the user is excited. This allows for more appropriate advice to be provided by adjusting the way the advice is expressed based on the user's emotions. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is expressed.

[0101] The providing unit can adjust the level of detail of the advice based on the importance of the information when providing the advice. The providing unit, for example, uses a generation AI to adjust the level of detail of the advice based on the importance of the information when providing the advice. The importance of the information includes, but is not limited to, the user's level of interest and the urgency of the information. For example, the providing unit provides detailed advice for information with high importance. The providing unit can also provide concise advice for information with low importance. The providing unit can also determine the priority of the advice according to the importance of the information. This enables efficient advice by adjusting the level of detail of the advice based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information importance data to the generation AI and cause the generation AI to adjust the level of detail of the advice.

[0102] The providing unit can apply different advice algorithms depending on the category of information when providing advice. The providing unit, for example, uses a generation AI to apply different advice algorithms depending on the category of information when providing advice. Information categories include, but are not limited to, health information, learning information, and business information. For example, the providing unit applies a health advice algorithm to health information. The providing unit can also apply a learning advice algorithm to learning information. The providing unit can also apply a business advice algorithm to business information. In this way, by applying different advice algorithms depending on the category of information, more appropriate advice can be provided. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information category data to the generation AI and cause the generation AI to apply the advice algorithm.

[0103] The providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. The providing unit, for example, uses a generation AI to improve the accuracy of advice by referring to the user's past advice results when providing advice. Past advice results include, but are not limited to, past reports, advice logs, and user feedback. For example, the providing unit adjusts the advice algorithm based on the user's past advice results. The providing unit can also improve the accuracy of advice from the user's past advice results. The providing unit can also improve the advice method by referring to the user's past advice results. In this way, the accuracy of advice is improved by referring to the user's past advice results. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input the user's past advice result data into the generation AI and cause the generation AI to improve the accuracy of advice.

[0104] The providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. The providing unit can, for example, use a generation AI to estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. Specific criteria for adjusting the length of the advice include, but are not limited to, short advice, long advice, and step-by-step guides. For example, the providing unit can provide short and to-the-point advice when the user is in a hurry. The providing unit can also provide detailed advice when the user is relaxed. The providing unit can also provide visually appealing advice when the user is excited. This allows for more appropriate advice to be provided by adjusting the length of the advice based on the user's emotions. Some or all of the above-described processing in the providing unit can be performed using, or without, the generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0105] The providing unit can determine the priority of advice based on the time of information submission when providing advice. The providing unit, for example, uses a generation AI to determine the priority of advice based on the time of information submission when providing advice. The time of information submission includes, for example, the submission date and time, the submission frequency, and the submission timing, but is not limited to these examples. For example, the providing unit prioritizes the most recent information in the advice. The providing unit can also provide advice for information that was submitted earlier at a later date. The providing unit can also adjust the advice schedule based on the submission time. This enables efficient advice by determining the priority of advice based on the time of information submission. Some or all of the above-described processing in the providing unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information submission time data into the generation AI and cause the generation AI to determine the priority of advice.

[0106] The providing unit can adjust the order of advice based on the relevance of information when providing advice. The providing unit, for example, uses a generation AI to adjust the order of advice based on the relevance of information when providing advice. The relevance of information includes, for example, co-occurrence frequency, correlation, and user interest level, but is not limited to these examples. For example, the providing unit prioritizes highly relevant information in the advice. The providing unit can also provide advice for less relevant information at a later date. The providing unit can also adjust the order of advice based on the relevance of information. This enables efficient advice by adjusting the order of advice based on the relevance of information. Some or all of the above-described processing in the providing unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the providing unit can input information relevance data to the generation AI and cause the generation AI to adjust the order of advice.

[0107] The providing unit can adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. The providing unit, for example, uses a generation AI to adjust the use of technical terms in the advice according to the user's level of expertise when providing the advice. Examples of the level of expertise include, but are not limited to, the user's occupation, educational background, and past feedback. For example, the providing unit can provide advice that uses a lot of technical terms if the user has technical knowledge. Furthermore, the providing unit can also provide advice in simple language if the user does not have technical knowledge. Furthermore, the providing unit can adjust the way the advice is expressed according to the user's level of expertise. This allows for more appropriate advice to be provided by adjusting the use of technical terms in the advice according to the user's level of expertise. Some or all of the above-described processing by the providing unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms in the advice.

[0108] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The feedback unit can estimate the user's emotions using, for example, a generation AI and adjust the feedback collection method based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the feedback unit can request detailed feedback when the user is relaxed. The feedback unit can also request concise feedback when the user is in a hurry. The feedback unit can also provide a visually appealing feedback form when the user is excited. This allows for more appropriate feedback to be collected by adjusting the feedback collection method based on the user's emotions. Some or all of the above-described processing in the feedback unit can be performed using, or without, the generation AI. For example, the feedback unit can input user emotion data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0109] The feedback unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. The feedback unit, for example, uses a generation AI to select the optimal collection method by referring to the user's past feedback history when collecting feedback. Past feedback history includes, but is not limited to, past ratings, comments, and behavioral data. For example, the feedback unit preferentially uses the form of feedback previously provided by the user. The feedback unit can also select the optimal collection method from the user's past feedback history. The feedback unit can also improve the accuracy of feedback collection based on the user's past feedback history. In this way, the optimal feedback collection method can be selected by referring to the user's past feedback history. Some or all of the above-described processing in the feedback unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's past feedback history data into the generation AI and cause the generation AI to select the collection method.

[0110] The feedback unit can customize the collection means based on the user's current living situation when collecting feedback. The feedback unit, for example, uses a generation AI to customize the collection means based on the user's current living situation when collecting feedback. The current living situation includes, but is not limited to, work status, family status, and health status. For example, the feedback unit can provide a brief feedback form when the user is busy. The feedback unit can also provide a detailed feedback form when the user is relaxed. The feedback unit can also adjust the feedback collection means according to the user's living situation. This allows more appropriate feedback to be collected by customizing the collection means based on the user's current living situation. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's living situation data into the generation AI and cause the generation AI to customize the collection means.

[0111] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. The feedback unit can estimate the user's emotions using, for example, a generation AI and determine the priority of feedback based on the estimated user emotions. Specific criteria for determining the priority of feedback include, but are not limited to, importance, urgency, and relevance. For example, if the user is stressed, the feedback unit can prioritize collecting feedback that helps the user relax. Furthermore, if the user is excited, the feedback unit can prioritize collecting interesting feedback. Furthermore, if the user is tired, the feedback unit can prioritize collecting refreshing feedback. Thus, by determining the priority of feedback based on the user's emotions, more appropriate feedback can be prioritized. Some or all of the above-described processing in the feedback unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input user emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0112] The feedback unit can select the optimal collection method by taking into account the user's geographical location information when collecting feedback. The feedback unit, for example, uses a generation AI to select the optimal collection method by taking into account the user's geographical location information when collecting feedback. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, the feedback unit prioritizes collecting feedback related to the user's current location. The feedback unit can also collect feedback about nearby stores and services based on the user's geographical location. The feedback unit can also collect feedback about local events and services based on the user's location information. This allows the optimal feedback collection method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's geographical location data into the generation AI and cause the generation AI to select the collection method.

[0113] The feedback unit can analyze the user's social media activity and suggest collection means when collecting feedback. The feedback unit can, for example, use a generation AI to analyze the user's social media activity and suggest collection means when collecting feedback. Social media activity includes, but is not limited to, post content, the number of likes, and the number of followers. For example, the feedback unit can analyze the post content of accounts the user follows on social media to collect relevant feedback. The feedback unit can also collect feedback that is likely to be of interest to the user based on the user's social media activity history. The feedback unit can also collect relevant feedback based on the activity of the user's friends on social media. In this way, relevant feedback can be efficiently collected by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the feedback unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of collection means.

[0114] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user emotions. The improvement unit can estimate the user's emotions using, for example, a generation AI and adjust the improvement method based on the estimated user emotions. Specific methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the improvement unit can provide a detailed improvement method when the user is relaxed. Furthermore, the improvement unit can provide a concise improvement method that focuses on the main points when the user is in a hurry. Furthermore, the improvement unit can provide a visually appealing improvement method when the user is excited. This allows for a more appropriate improvement method to be provided by adjusting the improvement method based on the user's emotions. Some or all of the above-described processing in the improvement unit can be performed using, or without, the generation AI. For example, the improvement unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the improvement method.

[0115] The improvement unit can analyze the user's past feedback and select the optimal improvement method when making an improvement. The improvement unit, for example, uses a generation AI to analyze the user's past feedback and select the optimal improvement method when making an improvement. Past feedback includes, for example, user ratings, comments, behavioral data, etc., but is not limited to these examples. For example, the improvement unit adjusts the improvement method based on the user's past feedback. The improvement unit can also select the optimal improvement method from the user's past feedback. The improvement unit can also improve the improvement method by referring to the user's past feedback. In this way, the optimal improvement method can be selected by analyzing the user's past feedback. Some or all of the above-mentioned processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's past feedback data into the generation AI and have the generation AI select an improvement method.

[0116] The improvement unit can customize the improvement measures based on the user's current living situation during improvement. The improvement unit, for example, uses a generation AI to customize the improvement measures based on the user's current living situation during improvement. Examples of current living situations include, but are not limited to, work status, family status, and health status. For example, the improvement unit can provide a concise improvement measure when the user is busy. The improvement unit can also provide a detailed improvement measure when the user is relaxed. The improvement unit can also adjust the improvement measures according to the user's living situation. This allows for customizing the improvement measures based on the user's current living situation, thereby providing a more appropriate improvement method. Some or all of the above-described processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's living situation data into the generation AI and have the generation AI customize the improvement measures.

[0117] The improvement unit can improve the improvement method by reflecting user feedback during improvement. The improvement unit, for example, uses a generation AI to improve the improvement method by reflecting user feedback during improvement. Feedback includes, for example, user ratings, comments, behavioral data, etc., but is not limited to these examples. For example, the improvement unit adjusts the improvement method based on user feedback. The improvement unit can also select an optimal improvement method from user feedback. The improvement unit can also improve the improvement method by referring to user feedback. In this way, the improvement method is continuously improved by reflecting user feedback. Some or all of the above-mentioned processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input user feedback data into the generation AI and have the generation AI execute improvements to the improvement method.

[0118] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated user emotions. The improvement unit can estimate the user's emotions using, for example, a generation AI and determine the priority of improvements based on the estimated user emotions. Specific criteria for determining the priority of improvements include, but are not limited to, importance, urgency, and relevance. For example, if the user is stressed, the improvement unit can prioritize providing improvement methods that will help the user relax. Furthermore, if the user is excited, the improvement unit can prioritize providing improvement methods that will attract the user's attention. Furthermore, if the user is tired, the improvement unit can prioritize providing improvement methods that will help the user refresh. Thus, by determining the priority of improvements based on the user's emotions, more appropriate improvement methods can be prioritized. Some or all of the above-described processing in the improvement unit can be performed using, or without, the generation AI. For example, the improvement unit can input user emotion data into the generation AI and have the generation AI determine the priority of improvements.

[0119] The improvement unit can select the optimal improvement method by taking into account the user's geographical location information when making an improvement. The improvement unit, for example, uses a generation AI to select the optimal improvement method by taking into account the user's geographical location information when making an improvement. Geographical location information includes, but is not limited to, GPS data, address information, and location history. For example, the improvement unit prioritizes providing improvement methods related to the user's current location. The improvement unit can also provide improvement methods related to nearby stores and services based on the user's geographical location. The improvement unit can also provide improvement methods related to local events and services based on the user's location information. This allows the optimal improvement method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the improvement unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's geographical location data into the generation AI and cause the generation AI to select an improvement method.

[0120] The improvement unit can analyze the user's social media activity and suggest improvement measures during improvement. The improvement unit, for example, uses a generation AI to analyze the user's social media activity and suggest improvement measures during improvement. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the improvement unit analyzes the content of posts from accounts the user follows on social media and suggests relevant improvement measures. The improvement unit can also suggest improvement measures that the user might be interested in based on the user's social media activity history. The improvement unit can also suggest relevant improvement measures based on the activities of the user's friends on social media. In this way, relevant improvement measures can be efficiently suggested by analyzing the user's social media activity. Some or all of the above-described processing in the improvement unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's social media data into the generation AI and have the generation AI execute the suggestion of improvement measures.

[0121] The improvement unit can customize the improvement method by reflecting the user's past feedback when making an improvement. The improvement unit, for example, uses a generation AI to customize the improvement method by reflecting the user's past feedback when making an improvement. Past feedback includes, but is not limited to, user ratings, comments, and behavioral data. For example, the improvement unit adjusts the improvement method based on the user's past feedback. The improvement unit can also select an optimal improvement method from the user's past feedback. The improvement unit can also improve the improvement method by referring to the user's past feedback. In this way, the improvement method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the improvement unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the improvement unit can input the user's past feedback data into the generation AI and have the generation AI customize the improvement method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, feedback unit, and improvement unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit provides advice via, for example, the output device 40 of the smart device 14. The feedback unit receives user feedback via, for example, the reception device 38 of the smart device 14 and transmits the feedback to the data processing device 12. The improvement unit improves the advice based on the feedback via, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, feedback unit, and improvement unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit provides advice, for example, via the speaker 240 of the smart glasses 214. The feedback unit receives user feedback, for example, via the microphone 238 of the smart glasses 214, and transmits the feedback to the data processing device 12. The improvement unit improves the advice based on the feedback, for example, via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, feedback unit, and improvement unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit provides advice via, for example, the speaker 240 of the headset type terminal 314. The feedback unit receives user feedback via, for example, the microphone 238 of the headset type terminal 314 and transmits the feedback to the data processing device 12. The improvement unit improves the advice based on the feedback via, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, feedback unit, and improvement unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI. The provision unit provides advice, for example, via the speaker 240 of the robot 414. The feedback unit receives user feedback, for example, via the microphone 238 of the robot 414, and transmits the feedback to the data processing device 12. The improvement unit improves the advice based on the feedback, for example, via the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize analyzing information that helps the user relax. Also, if the user is excited, it can prioritize analyzing information that piques the user's interest. Also, if the user is tired, it can prioritize analyzing information that helps the user refresh. In this way, by determining the analysis priorities based on the user's emotions, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI determine the analysis priorities.

[0124] The providing unit can estimate the user's emotions and adjust the format of advice based on the estimated user's emotions. For example, if the user is relaxed, detailed advice can be provided. If the user is in a hurry, concise advice that gets to the point can be provided. If the user is excited, visually appealing advice can be provided. In this way, by adjusting the format of advice based on the user's emotions, more appropriate advice can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the format of advice.

[0125] The feedback unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be requested. If the user is in a hurry, brief feedback can be requested. If the user is excited, a visually appealing feedback form can be provided. This allows for more appropriate feedback to be collected by adjusting the feedback collection method based on the user's emotions. Some or all of the above-described processing in the feedback unit may be performed using, or without, a generation AI. For example, the feedback unit can input user emotion data into the generation AI and cause the generation AI to adjust the feedback collection method.

[0126] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user's emotions. For example, if the user is relaxed, a detailed improvement method can be provided. If the user is in a hurry, a concise improvement method that focuses on the main points can be provided. If the user is excited, a visually appealing improvement method can be provided. In this way, by adjusting the improvement method based on the user's emotions, a more appropriate improvement method can be provided. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the improvement unit can input the user's emotion data into the generation AI and have the generation AI adjust the improvement method.

[0127] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, information collection can be performed during a relaxing time. Also, if the user is excited, information collection can be started immediately and data can be acquired in real time. Also, if the user is tired, information collection can be performed after the user has rested. In this way, by adjusting the timing of information collection according to the user's emotions, information can be collected at a more appropriate time. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0128] The analysis unit can analyze the user's past behavioral history and select the optimal analysis algorithm. For example, it can prioritize the selection of an analysis algorithm that the user has frequently used in the past. The analysis unit can also analyze the user's behavioral patterns and suggest the most efficient analysis algorithm. The analysis unit can also select the optimal analysis algorithm for a specific time period from the user's past behavioral history. In this way, the optimal analysis algorithm can be selected by analyzing the user's past behavioral history. 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 behavioral history data into the generation AI and have the generation AI select the analysis algorithm.

[0129] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. For example, event information related to the user's current location can be prioritized. The providing unit can also prioritize providing information about nearby stores and services based on the user's geographical location. The providing unit can also prioritize providing local news and weather information based on the user's location information. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to a generation AI and cause the generation AI to provide information.

[0130] The feedback unit can analyze the user's social media activity and collect relevant feedback. For example, it can analyze the content posted by accounts the user follows on social media and collect relevant feedback. The feedback unit can also collect feedback that is likely to be of interest to the user based on the user's social media activity history. The feedback unit can also collect relevant feedback by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, relevant feedback can be efficiently collected. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's social media data into a generation AI and cause the generation AI to collect feedback.

[0131] The improvement unit can analyze the user's past feedback and select the optimal improvement method. For example, the improvement unit adjusts the improvement method based on feedback provided by the user in the past. The improvement unit can also select the optimal improvement method from the user's past feedback. The improvement unit can also improve the improvement method by referring to the user's past feedback. In this way, the optimal improvement method can be selected by analyzing the user's past feedback. Some or all of the above-mentioned processing in the improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the improvement unit can input the user's past feedback data into the generation AI and have the generation AI select an improvement method.

[0132] When collecting information, the collection unit can filter the information based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting information related to a project the user is currently working on. The collection unit can also filter and collect related news and articles based on the user's areas of interest. The collection unit can also filter and collect necessary information according to the user's living situation (e.g., health condition, work situation). This allows more relevant information to be collected by filtering information based on the user's current living situation and areas of interest. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI filter the information.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The collection unit collects user information, including information about the user's lifestyle, health status, hobbies, learning, and business goals. The collection unit allows the user to input information about their daily diet, exercise habits, hobby activities, learning progress, and business goals. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI may provide appropriate dietary and exercise advice based on the user's health status, suggest events and activities related to hobbies, create a study plan based on the user's learning progress, and propose strategies to achieve business goals. Step 3: The provider provides advice based on the analysis results obtained by the analyzer. The advice is provided in the form of text messages, audio instructions, video guides, etc. Examples of advice include advice on appropriate diet and exercise based on health status, suggestions for events and activities related to hobbies, study plans based on learning progress, and strategy suggestions for business goals. Step 4: The feedback unit receives feedback on the advice provided by the providing unit. The feedback is received in the form of a user's rating, comment, behavioral data, etc. For example, the user can provide feedback on the provided advice. Step 5: The improvement unit improves the advice based on the feedback received by the feedback unit. Improvements are made by adjusting the algorithm, updating the advice content, etc. For example, the accuracy of advice can be improved based on user feedback.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; a feedback unit that receives feedback on the advice provided by the providing unit; an improvement unit that improves the advice based on the feedback received by the feedback unit. A system characterized by:

2. The collecting unit Collect information about at least one of the following: user's lifestyle, health, hobbies, learning, and business goals 2. The system of claim 1.

3. The analysis unit Analyze the collected information and provide appropriate advice or suggestions to users 2. The system of claim 1.

4. The providing unit Providing appropriate dietary or exercise advice based on health status based on analysis results 2. The system of claim 1.

5. The providing unit Suggesting hobby-related events and activities based on analysis results 2. The system of claim 1.

6. The providing unit Create a study plan based on the analysis results according to your learning progress 2. The system of claim 1.

7. The providing unit Propose strategies to achieve business goals based on analysis results 2. The system of claim 1.

8. The feedback unit Receive user feedback and provide it to the Improvement Team 2. The system of claim 1.

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