system

Abstract

The system according to the embodiment aims to analyze the behavioral patterns and characteristics of successful people and suggest actions suitable for the user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a suggestion unit. The collection unit collects data on successful people. The analysis unit analyzes the data collected by the collection unit and extracts the behavioral patterns and characteristics of successful people. The matching unit matches the data with user data based on the behavioral patterns and characteristics extracted by the analysis unit. The suggestion unit suggests actions based on the results of matching by the matching unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately analyze the behavioral patterns and characteristics of successful people individually and suggest actions that are appropriate for users, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze the behavioral patterns and characteristics of successful people and suggest actions suitable for the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a suggestion unit. The collection unit collects data on successful individuals. The analysis unit analyzes the data collected by the collection unit and extracts the behavioral patterns and characteristics of successful individuals. The matching unit matches the data with user data based on the behavioral patterns and characteristics extracted by the analysis unit. The suggestion unit suggests actions based on the results of matching by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the behavioral patterns and characteristics of successful people and suggest actions suitable for the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention reads data on successful individuals and compares it to determine optimal behavior. This system collects data on successful individuals, analyzes it using AI, and extracts their behavioral patterns and characteristics. Furthermore, the system compares the user's data with the data on successful individuals to suggest optimal behaviors and options. For example, detailed data on successful individuals' behavioral histories, options, and achievements is collected, and AI analyzes it to model the behavioral patterns and characteristics of successful individuals. Next, the system compares the user's data on successful individuals with data on their current situation, goals, resources, and other factors to suggest optimal behaviors and options. This allows the user to take optimal actions by leveraging the experience of successful individuals. This allows the user to take optimal actions by leveraging the experience of successful individuals. For example, utilizing data on successful business individuals can streamline project progress and increase the probability of success. Furthermore, learning the behavioral patterns and characteristics of successful individuals can improve one's own skills and knowledge.

[0029] The assessment system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a proposal unit. The collection unit collects data on successful individuals. The data on successful individuals includes, but is not limited to, behavioral history, options, and results. For example, the collection unit can record the behavioral history of successful individuals in detail and collect options and results. The collection unit can also collect detailed information on the progress and decisions of successful individuals' past projects. Furthermore, the collection unit can collect information on successful individuals' resource utilization methods and network information. The analysis unit analyzes the data collected by the collection unit and extracts the behavioral patterns and characteristics of successful individuals. The analysis unit, for example, uses AI to model the behavioral patterns and characteristics of successful individuals. The analysis unit can also adjust the level of analysis detail based on the importance of the behavioral patterns of successful individuals. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of successful individuals. The matching unit matches user data based on the behavioral patterns and characteristics extracted by the analysis unit. For example, the matching unit matches the data on successful individuals with data such as the user's current situation, goals, and resources. The matching unit can also estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. Furthermore, the matching unit can improve the accuracy of matching by taking into account the correlation between the user's data and the data of successful individuals. The suggestion unit suggests actions based on the results of matching by the matching unit. The suggestion unit, for example, suggests optimal actions or options for the user. The suggestion unit can also estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user emotions. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the behavioral patterns of successful individuals. This allows the determination system according to the embodiment to suggest optimal actions to the user based on the data of successful individuals.

[0030] The collection unit can collect data on the behavioral history, choices, and results of successful individuals. The collection unit, for example, records the behavioral history of successful individuals in detail. For example, the collection unit records what actions successful individuals took, as well as the date, time, and location. The collection unit can also collect the choices successful individuals made. For example, the collection unit collects what choices successful individuals made and the reasons for those choices. The collection unit can also collect the results of successful individuals. For example, the collection unit collects the goals successful individuals achieved and the results they obtained. By collecting detailed data on successful individuals, more accurate analysis is possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the behavioral history of successful individuals into AI and have the AI ​​analyze the behavioral history.

[0031] The suggestion unit can provide a text message or a dashboard display as a method of notifying the user of the suggestion. The suggestion unit, for example, sends a text message to the user. For example, the suggestion unit sends a text message to the user's smartphone to notify the user of the suggestion content. The suggestion unit can also provide a dashboard display. For example, the suggestion unit displays a dashboard on the user's computer or tablet to visually provide the suggestion content. This makes it easier for the user to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the suggestion content into AI and have the AI ​​generate a text message.

[0032] The collection unit can collect detailed information about the progress and decisions of successful individuals' past projects. For example, the collection unit records in detail what projects successful individuals have worked on in the past and their progress. For example, the collection unit collects the progress of each phase of the successful individuals' projects. The collection unit can also collect in detail what decisions successful individuals made at each stage of the project and those decisions. For example, the collection unit collects minutes of successful individuals' meetings and the reasons for decisions. The collection unit can also collect in detail the challenges successful individuals faced during the project and how they solved them. For example, the collection unit records the challenges successful individuals faced and their solutions. By collecting detailed project data about successful individuals, more accurate analysis is possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input successful individuals' project data into AI and have the AI ​​analyze the data.

[0033] The collection unit can collect information on successful individuals' resource utilization methods and network information. For example, the collection unit collects details of what resources successful individuals utilized and when. For example, the collection unit records the types of resources used by successful individuals and how they utilized them. The collection unit can also collect information on what kind of networks successful individuals built and how they utilized them. For example, the collection unit collects a list of successful individuals' associates and the structure of their networks. The collection unit can also collect information on how successful individuals optimized resources to progress projects. For example, the collection unit records the specific methods successful individuals used to optimize resources. By collecting information on successful individuals' resource utilization methods and network information, more accurate analysis is possible. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or can be performed without AI. For example, the collection unit can input successful individuals' resource utilization data into AI and have the AI ​​analyze the data.

[0034] The collection unit can organize and collect the behavioral histories of successful individuals along a timeline. For example, the collection unit organizes the behavioral histories of successful individuals along a timeline and collects detailed information on what actions they took at what time. For example, the collection unit organizes and collects the behavioral histories of successful individuals as time-series data. The collection unit can also organize and collect the behavioral histories of successful individuals according to the progress of the project. For example, the collection unit organizes the behavioral histories of successful individuals according to each phase of the project. The collection unit can also organize and collect the behavioral histories of successful individuals by date or time. For example, the collection unit classifies and collects the behavioral histories of successful individuals by date or time. By organizing the behavioral histories of successful individuals along a timeline, more accurate analysis is possible. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the behavioral history data of successful individuals into AI and have the AI ​​organize the data.

[0035] The collection unit can prioritize collecting highly relevant data by taking into account the geographic location information of successful individuals. For example, the collection unit prioritizes collecting data on projects that successful individuals have undertaken in a specific area. For example, the collection unit collects information on the progress and results of projects that successful individuals have undertaken in a specific area. The collection unit can also collect detailed information on the successful individuals' activity history in a specific area. For example, the collection unit records the date, time, and location of activities that successful individuals have undertaken in a specific area. The collection unit can also prioritize collecting information on the successful individuals' networks in a specific area. For example, the collection unit collects details of the networks that successful individuals have built in a specific area. This allows for more relevant data to be collected by taking into account the successful individuals' geographic location information. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the successful individuals' geographic location information into AI and have the AI ​​determine the priority of highly relevant data.

[0036] The collection unit can analyze the social media activities of successful individuals and collect related data. For example, the collection unit collects project progress information shared by successful individuals on social media. For example, the collection unit collects project progress and results posted by successful individuals on social media. The collection unit can also analyze the content posted by successful individuals on social media and collect related data. For example, the collection unit analyzes the content posted by successful individuals and extracts information related to the project. The collection unit can also collect social media network information of successful individuals. For example, the collection unit collects details of the networks built by successful individuals on social media. This makes it possible to collect more diverse data by analyzing the social media activities of successful individuals. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the social media data of successful individuals into AI and have the AI ​​analyze the data.

[0037] The collection unit can customize the collection method by reflecting the successful individual's past feedback. The collection unit customizes the collection method, for example, based on feedback provided in the past by the successful individual. For example, the collection unit analyzes the successful individual's feedback and adjusts the type and scope of data to be collected. The collection unit can also optimize the collection method by reflecting the successful individual's past feedback. For example, the collection unit improves the efficiency of the collection method based on the successful individual's feedback. The collection unit can also determine the priority of data to be collected based on the successful individual's feedback. For example, the collection unit analyzes the successful individual's feedback and prioritizes the collection of important data. This enables more efficient data collection by reflecting the successful individual's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the successful individual's feedback data into AI and have the AI ​​customize the collection method.

[0038] The analysis unit can adjust the level of detail of the analysis based on the importance of the successful person's behavioral patterns. For example, if the importance of the successful person's behavioral patterns is high, the analysis unit performs a detailed analysis and provides it to the user. For example, the analysis unit evaluates the importance of the successful person's behavioral patterns and performs a detailed analysis if the importance is high. Furthermore, the analysis unit can perform a concise analysis and provide it to the user if the importance of the successful person's behavioral patterns is low. For example, the analysis unit evaluates the importance of the successful person's behavioral patterns and performs a concise analysis if the importance is low. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the successful person's behavioral patterns. For example, the analysis unit evaluates the importance of the successful person's behavioral patterns in real time and adjusts the level of detail of the analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the successful person's behavioral patterns, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the successful person's behavioral pattern data into AI and have the AI ​​analyze the data.

[0039] The analysis unit can apply different analysis algorithms depending on the category of successful people. For example, in the case of a successful business person, the analysis unit applies a business-specific analysis algorithm. For example, the analysis unit uses a business-specific algorithm to analyze the data of the successful business person. Furthermore, in the case of a successful sports person, the analysis unit can apply a sports-specific analysis algorithm. For example, the analysis unit uses a sports-specific algorithm to analyze the data of the successful sports person. Furthermore, in the case of a successful academic field, the analysis unit can apply an academic-specific analysis algorithm. For example, the analysis unit uses an academic-specific algorithm to analyze the data of the successful academic person. In this way, by applying an analysis algorithm depending on the category of successful people, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the category data of successful people into AI and have the AI ​​select an appropriate analysis algorithm.

[0040] The analysis unit can improve the accuracy of the analysis by referring to the successful person's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the successful person's past analysis results. For example, the analysis unit refers to the successful person's past analysis results and reflects them in the current analysis. The analysis unit can also optimize the analysis algorithm by referring to the successful person's past analysis results. For example, the analysis unit adjusts the algorithm parameters based on the successful person's past analysis results. The analysis unit can also dynamically adjust the analysis accuracy by utilizing the successful person's past analysis results. For example, the analysis unit refers to the successful person's past analysis results in real time and adjusts the analysis accuracy. In this way, the analysis accuracy can be improved by referring to the successful person's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the successful person's past analysis result data into AI and have the AI ​​improve the analysis accuracy.

[0041] The analysis unit can determine the analysis priority based on the time of successful individual's data submission. For example, if the successful individual's data was submitted recently, the analysis unit prioritizes analysis. For example, the analysis unit evaluates the time of successful individual's data submission and prioritizes analysis of the most recently submitted data. The analysis unit can also lower the priority of the successful individual's data if the successful individual's data is old. For example, the analysis unit evaluates the time of successful individual's data submission and lowers the priority of older data. The analysis unit can also dynamically adjust the analysis priority based on the time of successful individual's data submission. For example, the analysis unit evaluates the time of successful individual's data submission in real time and adjusts the analysis priority. This enables more efficient analysis by determining the analysis priority based on the time of successful individual's data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time of successful individual's data submission into AI and have the AI ​​determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data of successful individuals. For example, if the data of successful individuals is highly relevant to the user's situation, the analysis unit prioritizes analysis. For example, the analysis unit evaluates the relevance of the data of successful individuals and prioritizes analysis of highly relevant data. The analysis unit can also postpone the order of analysis if the data of successful individuals is less relevant to the user's situation. For example, the analysis unit evaluates the relevance of the data of successful individuals and postpones the order of analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data of successful individuals. For example, the analysis unit evaluates the relevance of the data of successful individuals in real time and adjusts the order of analysis. This allows for adjusting the order of analysis based on the relevance of the data of successful individuals to provide more appropriate analysis results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the relevance of the data of successful individuals into AI and have the AI ​​determine the order of analysis.

[0043] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the successful person. For example, if the expertise level of the successful person is high, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit evaluates the expertise level of the successful person and uses a lot of technical terms if the expertise level is high. The analysis unit can also provide analysis results that avoid technical terms if the expertise level of the successful person is low. For example, the analysis unit evaluates the expertise level of the successful person and avoids technical terms if the expertise level is low. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the expertise level of the successful person. For example, the analysis unit evaluates the expertise level of the successful person in real time and adjusts the use of technical terms. This allows for adjusting the use of technical terms in the analysis according to the expertise level of the successful person, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the expertise level of the successful person into AI and have the AI ​​determine the use of technical terms.

[0044] The matching unit can improve the accuracy of matching by taking into account the correlation between the user's data and the data of successful individuals. The matching unit, for example, analyzes the correlation between the user's data and the data of successful individuals to improve the accuracy of matching. For example, the matching unit evaluates the correlation between the user's data and the data of successful individuals to improve the accuracy of matching. The matching unit can also optimize the matching algorithm by taking into account the correlation between the user's data and the data of successful individuals. For example, the matching unit adjusts the parameters of the matching algorithm based on the correlation between the user's data and the data of successful individuals. The matching unit can also dynamically adjust the accuracy of matching based on the correlation between the user's data and the data of successful individuals. For example, the matching unit evaluates the correlation between the user's data and the data of successful individuals in real time and adjusts the accuracy of matching. This allows the accuracy of matching to be improved by taking into account the correlation between the user's data and the data of successful individuals. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the correlation between the user's data and the data of successful individuals into AI to improve the accuracy of matching.

[0045] The matching unit can perform matching taking into account attribute information of the user. The matching unit performs matching taking into account attribute information such as the user's age and gender. For example, the matching unit evaluates the user's age and gender and reflects them in the matching. The matching unit can also perform matching taking into account attribute information such as the user's occupation and experience. For example, the matching unit evaluates the user's occupation and experience and reflects them in the matching. The matching unit can also optimize the matching algorithm based on the user's attribute information. For example, the matching unit adjusts parameters of the matching algorithm based on the user's attribute information. This enables more appropriate matching by taking into account the user's attribute information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's attribute information into AI to improve the accuracy of matching.

[0046] The matching unit can weight the matching based on the frequency of user data submission. For example, if a user frequently submits data, the matching unit can weight the data more highly to improve the accuracy of the matching. For example, the matching unit evaluates the frequency of user data submission and weights frequently submitted data more highly. Furthermore, if a user rarely submits data, the matching unit can weight the data less highly to balance the matching. For example, the matching unit evaluates the frequency of user data submission and weights infrequently submitted data less highly. Furthermore, the matching unit can dynamically adjust the matching weights based on the frequency of user data submission. For example, the matching unit evaluates the frequency of user data submission in real time and adjusts the matching weights. Thus, by weighting the matching based on the frequency of user data submission, the accuracy of the matching can be improved. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input the frequency of user data submission into AI and have the AI ​​determine the matching weights.

[0047] The matching unit can perform matching taking into account the geographical distribution of users. For example, if a user is in a specific region, the matching unit prioritizes matching data of successful people related to that region. For example, the matching unit evaluates the geographical distribution of users and prioritizes matching data of successful people related to that region. The matching unit can also optimize the matching algorithm taking into account the geographical distribution of users. For example, the matching unit adjusts parameters of the matching algorithm based on the geographical distribution of users. The matching unit can also improve the accuracy of matching based on the geographical distribution of users. For example, the matching unit evaluates the geographical distribution of users in real time and improves the accuracy of matching. This enables more appropriate matching by taking the geographical distribution of users into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the geographical distribution data of users into AI to improve the accuracy of matching.

[0048] The matching unit can improve the accuracy of matching by referring to the user's related literature. The matching unit improves the accuracy of matching, for example, based on the related literature referenced by the user. For example, the matching unit evaluates the user's related literature and reflects it in the matching. The matching unit can also optimize the matching algorithm by referring to the user's related literature. For example, the matching unit adjusts parameters of the matching algorithm based on the user's related literature. The matching unit can also dynamically adjust the accuracy of matching based on the user's related literature. For example, the matching unit evaluates the user's related literature in real time and adjusts the accuracy of matching. In this way, the accuracy of matching can be improved by referring to the user's related literature. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's related literature data into AI and use AI to improve the accuracy of matching.

[0049] The matching unit can perform matching taking into account the user's market value. For example, if the user's market value is high, the matching unit assigns a high weight to the data, thereby improving the accuracy of the matching. For example, the matching unit evaluates the user's market value and assigns a high weight to the data when the market value is high. Furthermore, if the user's market value is low, the matching unit can assign a low weight to the data to balance the matching. For example, the matching unit evaluates the user's market value and assigns a low weight to the data when the market value is low. Furthermore, the matching unit can dynamically adjust the matching weight based on the user's market value. For example, the matching unit evaluates the user's market value in real time and adjusts the matching weight. This enables more appropriate matching by taking the user's market value into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's market value data into AI, thereby improving the accuracy of the matching using AI.

[0050] The suggestion unit can adjust the level of detail of the proposal based on the importance of the successful person's behavioral patterns. For example, if the importance of the successful person's behavioral patterns is high, the suggestion unit makes a detailed proposal and provides it to the user. For example, the suggestion unit evaluates the importance of the successful person's behavioral patterns and makes a detailed proposal if the importance is high. The suggestion unit can also make a concise proposal and provide it to the user if the importance of the successful person's behavioral patterns is low. For example, the suggestion unit evaluates the importance of the successful person's behavioral patterns and makes a concise proposal if the importance is low. The suggestion unit can also dynamically adjust the level of detail of the proposal depending on the importance of the successful person's behavioral patterns. For example, the suggestion unit evaluates the importance of the successful person's behavioral patterns in real time and adjusts the level of detail of the proposal. This enables more appropriate suggestions to be made by adjusting the level of detail of the proposal based on the importance of the successful person's behavioral patterns. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the successful person's behavioral pattern data into AI and have the AI ​​determine the level of detail of the proposal.

[0051] The suggestion unit can apply different suggestion algorithms depending on the category of successful people. For example, in the case of a successful business person, the suggestion unit applies a business-specific suggestion algorithm. For example, the suggestion unit uses a business-specific suggestion algorithm based on data of successful business people. The suggestion unit can also apply a sports-specific suggestion algorithm to a successful sports person. For example, the suggestion unit uses a sports-specific suggestion algorithm based on data of successful sports people. The suggestion unit can also apply an academic-specific suggestion algorithm to a successful academic person. For example, the suggestion unit uses an academic-specific suggestion algorithm based on data of successful academic people. This enables more appropriate suggestions to be made by applying a suggestion algorithm depending on the category of successful people. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of successful people into AI and have the AI ​​select an appropriate suggestion algorithm.

[0052] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. The suggestion unit, for example, improves the accuracy of current suggestions based on the user's past suggestion results. For example, the suggestion unit refers to the user's past suggestion results and reflects them in the current suggestion. The suggestion unit can also optimize the suggestion algorithm by referring to the user's past suggestion results. For example, the suggestion unit adjusts algorithm parameters based on the user's past suggestion results. The suggestion unit can also dynamically adjust the accuracy of suggestions by utilizing the user's past suggestion results. For example, the suggestion unit refers to the user's past suggestion results in real time and adjusts the accuracy of suggestions. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI and cause the AI ​​to improve the accuracy of suggestions.

[0053] The suggestion unit can determine the priority of proposals based on the time of successful individual data submission. For example, the suggestion unit prioritizes proposals when the successful individual's data was submitted recently. For example, the suggestion unit evaluates the time of successful individual data submission and prioritizes the most recently submitted data. The suggestion unit can also lower the priority of proposals when the successful individual's data is old. For example, the suggestion unit evaluates the time of successful individual data submission and lowers the priority of older data. The suggestion unit can also dynamically adjust the priority of proposals based on the time of successful individual data submission. For example, the suggestion unit evaluates the time of successful individual data submission in real time and adjusts the priority of proposals. This enables more efficient proposals by determining the priority of proposals based on the time of successful individual data submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the time of successful individual data submission into AI and have the AI ​​determine the priority of proposals.

[0054] The suggestion unit can adjust the order of suggestions based on the relevance of the data of successful individuals. For example, if the data of successful individuals is highly relevant to the user's situation, the suggestion unit prioritizes suggestions. For example, the suggestion unit evaluates the relevance of the data of successful individuals and prioritizes proposing highly relevant data. The suggestion unit can also postpone the order of suggestions if the data of successful individuals is less relevant to the user's situation. For example, the suggestion unit evaluates the relevance of the data of successful individuals and postpones the order of suggestions of less relevant data. The suggestion unit can also dynamically adjust the order of suggestions based on the relevance of the data of successful individuals. For example, the suggestion unit evaluates the relevance of the data of successful individuals in real time and adjusts the order of suggestions. This enables more appropriate suggestions by adjusting the order of suggestions based on the relevance of the data of successful individuals. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the relevance of the data of successful individuals into AI and have the AI ​​determine the order of suggestions.

[0055] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, if the user's level of expertise is high, the suggestion unit makes suggestions that use a lot of technical terms. For example, the suggestion unit evaluates the user's level of expertise and uses a lot of technical terms when the user's level of expertise is high. The suggestion unit can also make suggestions that avoid technical terms when the user's level of expertise is low. For example, the suggestion unit evaluates the user's level of expertise and avoids technical terms when the user's level of expertise is low. The suggestion unit can also dynamically adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise in real time and adjusts the use of technical terms. This enables more appropriate suggestions by adjusting the use of technical terms in the suggestions depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise into AI and have the AI ​​determine the use of technical terms.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The suggestion unit can further adjust the format of the suggestion based on the user's learning style. For example, if the user has a visual learning style, the suggestion unit can make suggestions using graphs or charts. If the user has an auditory learning style, the suggestion unit can make suggestions using audio messages. Furthermore, if the user has a hands-on learning style, the suggestion unit can make suggestions showing specific action steps. This enables effective suggestions to be made according to the user's learning style.

[0058] The collection unit can further collect social network data of the user. For example, it can collect information about the types of people the user interacts with and their relationships. The collection unit can also evaluate the user's influence and trustworthiness based on the user's social network data. For example, if a user has many followers, it can evaluate the user as being highly influential. This makes it possible to collect data based on the user's social network.

[0059] The collection unit can further collect data based on the user's hobbies and interests. For example, if the user has a particular hobby, data on successful people related to that hobby is preferentially collected. The collection unit can also filter related data based on the user's interests. For example, if the user is interested in sports, data on successful people related to sports is collected. This makes it possible to collect data according to the user's hobbies and interests.

[0060] The analysis unit can further improve the accuracy of the analysis based on the user's past behavioral history. For example, it can analyze the actions the user has taken in the past and reflect the results in the current analysis. The analysis unit can also extract behavioral patterns based on the user's past behavioral history and improve the accuracy of suggestions. For example, it can analyze the behavioral patterns that have been successful for the user in the past and reflect them in suggestions for similar situations. This enables highly accurate analysis based on the user's past behavioral history.

[0061] The matching unit can further perform matching taking into account the user's cultural background. For example, if the user belongs to a specific cultural sphere, the matching unit will prioritize matching data of successful people related to that culture. The matching unit can also adjust the matching algorithm based on the user's cultural background. For example, if the user belongs to a different cultural sphere, the matching unit will apply matching criteria appropriate for that culture. This enables appropriate matching according to the user's cultural background.

[0062] The suggestion unit can further monitor the user's degree of goal achievement and adjust the frequency of suggestions. For example, if the user is approaching the goal, the frequency of suggestions can be reduced to enable the user to act independently. Alternatively, if the user is moving away from the goal, the frequency of suggestions can be increased to provide stronger support. Furthermore, the content of suggestions can be adjusted based on the user's degree of goal achievement. For example, if the user is approaching the goal, a suggestion for moving on to the next step can be made. This makes it possible to make effective suggestions according to the user's degree of goal achievement.

[0063] The suggestion unit can further customize the content of the suggestion based on user feedback. For example, if the user provides feedback on a suggested action, the suggestion unit can adjust the content of the suggestion based on the feedback. The suggestion unit can also change the format or presentation of the suggestion based on user feedback. For example, if the user prefers visual suggestions, the suggestion unit can provide suggestions using graphs or charts. This allows for customized suggestions based on user feedback.

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

[0065] Step 1: The collection department collects data on successful people. This data includes their behavioral history, choices, results, progress and decisions made on past projects, resource utilization methods, and network information. The collection department records and collects this data in detail. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the behavioral patterns and characteristics of successful people. The analysis unit uses AI to model the behavioral patterns and characteristics of successful people and adjusts the level of analysis detail based on the importance of the behavioral patterns. It also applies different analysis algorithms depending on the category of successful people. Step 3: The matching unit compares the user's data based on the behavioral patterns and characteristics extracted by the analysis unit. The matching unit compares data such as the user's current situation, goals, and resources with the data of successful people, estimates the user's emotions, and adjusts the matching criteria. The accuracy of the matching is also improved by taking into account the correlation between the user's data and the data of successful people. Step 4: The suggestion unit proposes actions based on the results of the comparison by the matching unit. The suggestion unit proposes optimal actions and options for the user, estimates the user's emotions, and adjusts the way the suggestions are presented. The suggestion unit also adjusts the level of detail of the suggestions based on the importance of the behavioral patterns of successful people.

[0066] (Example 2) A system according to an embodiment of the present invention reads data on successful individuals and compares it to determine optimal behavior. This system collects data on successful individuals, analyzes it using AI, and extracts their behavioral patterns and characteristics. Furthermore, the system compares the user's data with the data on successful individuals to suggest optimal behaviors and options. For example, detailed data on successful individuals' behavioral histories, options, and achievements is collected, and AI analyzes it to model the behavioral patterns and characteristics of successful individuals. Next, the system compares the user's data on successful individuals with data on their current situation, goals, resources, and other factors to suggest optimal behaviors and options. This allows the user to take optimal actions by leveraging the experience of successful individuals. This allows the user to take optimal actions by leveraging the experience of successful individuals. For example, utilizing data on successful business individuals can streamline project progress and increase the probability of success. Furthermore, learning the behavioral patterns and characteristics of successful individuals can improve one's own skills and knowledge.

[0067] The assessment system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a proposal unit. The collection unit collects data on successful individuals. The data on successful individuals includes, but is not limited to, behavioral history, options, and results. For example, the collection unit can record the behavioral history of successful individuals in detail and collect options and results. The collection unit can also collect detailed information on the progress and decisions of successful individuals' past projects. Furthermore, the collection unit can collect information on successful individuals' resource utilization methods and network information. The analysis unit analyzes the data collected by the collection unit and extracts the behavioral patterns and characteristics of successful individuals. The analysis unit, for example, uses AI to model the behavioral patterns and characteristics of successful individuals. The analysis unit can also adjust the level of analysis detail based on the importance of the behavioral patterns of successful individuals. Furthermore, the analysis unit can apply different analysis algorithms depending on the category of successful individuals. The matching unit matches user data based on the behavioral patterns and characteristics extracted by the analysis unit. For example, the matching unit matches the data on successful individuals with data such as the user's current situation, goals, and resources. The matching unit can also estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. Furthermore, the matching unit can improve the accuracy of matching by taking into account the correlation between the user's data and the data of successful individuals. The suggestion unit suggests actions based on the results of matching by the matching unit. The suggestion unit, for example, suggests optimal actions or options for the user. The suggestion unit can also estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user emotions. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the behavioral patterns of successful individuals. This allows the determination system according to the embodiment to suggest optimal actions to the user based on the data of successful individuals.

[0068] The collection unit can collect data on the behavioral history, choices, and results of successful individuals. The collection unit, for example, records the behavioral history of successful individuals in detail. For example, the collection unit records what actions successful individuals took, as well as the date, time, and location. The collection unit can also collect the choices successful individuals made. For example, the collection unit collects what choices successful individuals made and the reasons for those choices. The collection unit can also collect the results of successful individuals. For example, the collection unit collects the goals successful individuals achieved and the results they obtained. By collecting detailed data on successful individuals, more accurate analysis is possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the behavioral history of successful individuals into AI and have the AI ​​analyze the behavioral history.

[0069] The suggestion unit can provide a text message or a dashboard display as a method of notifying the user of the suggestion. The suggestion unit, for example, sends a text message to the user. For example, the suggestion unit sends a text message to the user's smartphone to notify the user of the suggestion content. The suggestion unit can also provide a dashboard display. For example, the suggestion unit displays a dashboard on the user's computer or tablet to visually provide the suggestion content. This makes it easier for the user to accept the suggestion. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit can input the suggestion content into AI and have the AI ​​generate a text message.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting data on successful candidates based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit delays the collection timing and collects data when the user is relaxed. For example, the collection unit monitors the user's emotions in real time and pauses collection when the user is feeling stressed. The collection unit can also collect data immediately when the user is relaxed, collecting data when the user is highly focused. For example, the collection unit analyzes the user's emotion data to detect when the user is relaxed. If the user is in a hurry, the collection unit can also advance the collection timing to quickly collect necessary data. For example, the collection unit detects a state of hurry based on the user's emotion data and quickly collects data. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0071] The collection unit can collect detailed information about the progress and decisions of successful individuals' past projects. For example, the collection unit records in detail what projects successful individuals have worked on in the past and their progress. For example, the collection unit collects the progress of each phase of the successful individuals' projects. The collection unit can also collect in detail what decisions successful individuals made at each stage of the project and those decisions. For example, the collection unit collects minutes of successful individuals' meetings and the reasons for decisions. The collection unit can also collect in detail the challenges successful individuals faced during the project and how they solved them. For example, the collection unit records the challenges successful individuals faced and their solutions. By collecting detailed project data about successful individuals, more accurate analysis is possible. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input successful individuals' project data into AI and have the AI ​​analyze the data.

[0072] The collection unit can collect information on successful individuals' resource utilization methods and network information. For example, the collection unit collects details of what resources successful individuals utilized and when. For example, the collection unit records the types of resources used by successful individuals and how they utilized them. The collection unit can also collect information on what kind of networks successful individuals built and how they utilized them. For example, the collection unit collects a list of successful individuals' associates and the structure of their networks. The collection unit can also collect information on how successful individuals optimized resources to progress projects. For example, the collection unit records the specific methods successful individuals used to optimize resources. By collecting information on successful individuals' resource utilization methods and network information, more accurate analysis is possible. Some or all of the above-described processing by the collection unit can be performed, for example, using AI, or can be performed without AI. For example, the collection unit can input successful individuals' resource utilization data into AI and have the AI ​​analyze the data.

[0073] The collection unit can organize and collect the behavioral histories of successful individuals along a timeline. For example, the collection unit organizes the behavioral histories of successful individuals along a timeline and collects detailed information on what actions they took at what time. For example, the collection unit organizes and collects the behavioral histories of successful individuals as time-series data. The collection unit can also organize and collect the behavioral histories of successful individuals according to the progress of the project. For example, the collection unit organizes the behavioral histories of successful individuals according to each phase of the project. The collection unit can also organize and collect the behavioral histories of successful individuals by date or time. For example, the collection unit classifies and collects the behavioral histories of successful individuals by date or time. By organizing the behavioral histories of successful individuals along a timeline, more accurate analysis is possible. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the behavioral history data of successful individuals into AI and have the AI ​​organize the data.

[0074] The collection unit can estimate the user's emotions and prioritize the data of successful users to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting less important data, thereby reducing the user's burden. For example, the collection unit analyzes the user's emotional data and collects less important data if the user is feeling stressed. Furthermore, if the user is relaxed, the collection unit can prioritize collecting more important data, thereby enabling efficient data collection. For example, the collection unit detects a relaxed state based on the user's emotional data and collects more important data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting the most important data and provide necessary information quickly. For example, the collection unit detects a state of hurry based on the user's emotional data and prioritizes collecting important data. This enables more appropriate data collection by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0075] The collection unit can prioritize collecting highly relevant data by taking into account the geographic location information of successful individuals. For example, the collection unit prioritizes collecting data on projects that successful individuals have undertaken in a specific area. For example, the collection unit collects information on the progress and results of projects that successful individuals have undertaken in a specific area. The collection unit can also collect detailed information on the successful individuals' activity history in a specific area. For example, the collection unit records the date, time, and location of activities that successful individuals have undertaken in a specific area. The collection unit can also prioritize collecting information on the successful individuals' networks in a specific area. For example, the collection unit collects details of the networks that successful individuals have built in a specific area. This allows for more relevant data to be collected by taking into account the successful individuals' geographic location information. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the successful individuals' geographic location information into AI and have the AI ​​determine the priority of highly relevant data.

[0076] The collection unit can analyze the social media activities of successful individuals and collect related data. For example, the collection unit collects project progress information shared by successful individuals on social media. For example, the collection unit collects project progress and results posted by successful individuals on social media. The collection unit can also analyze the content posted by successful individuals on social media and collect related data. For example, the collection unit analyzes the content posted by successful individuals and extracts information related to the project. The collection unit can also collect social media network information of successful individuals. For example, the collection unit collects details of the networks built by successful individuals on social media. This makes it possible to collect more diverse data by analyzing the social media activities of successful individuals. Some or all of the above-mentioned processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the social media data of successful individuals into AI and have the AI ​​analyze the data.

[0077] The collection unit can customize the collection method by reflecting the successful individual's past feedback. The collection unit customizes the collection method, for example, based on feedback provided in the past by the successful individual. For example, the collection unit analyzes the successful individual's feedback and adjusts the type and scope of data to be collected. The collection unit can also optimize the collection method by reflecting the successful individual's past feedback. For example, the collection unit improves the efficiency of the collection method based on the successful individual's feedback. The collection unit can also determine the priority of data to be collected based on the successful individual's feedback. For example, the collection unit analyzes the successful individual's feedback and prioritizes the collection of important data. This enables more efficient data collection by reflecting the successful individual's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the successful individual's feedback data into AI and have the AI ​​customize the collection method.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible analysis result. For example, the analysis unit analyzes the user's emotion data and provides a concise analysis result if the user is feeling stressed. The analysis unit can also provide a detailed analysis result if the user is relaxed, thereby deepening the user's understanding. For example, the analysis unit can detect a relaxed state based on the user's emotion data and provide a detailed analysis result. The analysis unit can also provide a concise analysis result that focuses on the main points if the user is in a hurry. For example, the analysis unit can detect a rushed state based on the user's emotion data and provide a concise analysis result. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the successful person's behavioral patterns. For example, if the importance of the successful person's behavioral patterns is high, the analysis unit performs a detailed analysis and provides it to the user. For example, the analysis unit evaluates the importance of the successful person's behavioral patterns and performs a detailed analysis if the importance is high. Furthermore, the analysis unit can perform a concise analysis and provide it to the user if the importance of the successful person's behavioral patterns is low. For example, the analysis unit evaluates the importance of the successful person's behavioral patterns and performs a concise analysis if the importance is low. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the successful person's behavioral patterns. For example, the analysis unit evaluates the importance of the successful person's behavioral patterns in real time and adjusts the level of detail of the analysis. In this way, by adjusting the level of detail of the analysis based on the importance of the successful person's behavioral patterns, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the successful person's behavioral pattern data into AI and have the AI ​​analyze the data.

[0080] The analysis unit can apply different analysis algorithms depending on the category of successful people. For example, in the case of a successful business person, the analysis unit applies a business-specific analysis algorithm. For example, the analysis unit uses a business-specific algorithm to analyze the data of the successful business person. Furthermore, in the case of a successful sports person, the analysis unit can apply a sports-specific analysis algorithm. For example, the analysis unit uses a sports-specific algorithm to analyze the data of the successful sports person. Furthermore, in the case of a successful academic field, the analysis unit can apply an academic-specific analysis algorithm. For example, the analysis unit uses an academic-specific algorithm to analyze the data of the successful academic person. In this way, by applying an analysis algorithm depending on the category of successful people, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without AI. For example, the analysis unit can input the category data of successful people into AI and have the AI ​​select an appropriate analysis algorithm.

[0081] The analysis unit can improve the accuracy of the analysis by referring to the successful person's past analysis results. The analysis unit, for example, improves the accuracy of the current analysis based on the successful person's past analysis results. For example, the analysis unit refers to the successful person's past analysis results and reflects them in the current analysis. The analysis unit can also optimize the analysis algorithm by referring to the successful person's past analysis results. For example, the analysis unit adjusts the algorithm parameters based on the successful person's past analysis results. The analysis unit can also dynamically adjust the analysis accuracy by utilizing the successful person's past analysis results. For example, the analysis unit refers to the successful person's past analysis results in real time and adjusts the analysis accuracy. In this way, the analysis accuracy can be improved by referring to the successful person's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the successful person's past analysis result data into AI and have the AI ​​improve the analysis accuracy.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit provides a short and concise analysis result. For example, the analysis unit analyzes the user's emotion data and provides a concise analysis result if the user is feeling stressed. The analysis unit can also provide a detailed analysis result if the user is relaxed, thereby deepening the user's understanding. For example, the analysis unit can detect a relaxed state based on the user's emotion data and provide a detailed analysis result. The analysis unit can also provide a concise and quick analysis result if the user is in a hurry. For example, the analysis unit can detect a hurried state based on the user's emotion data and provide a concise analysis result. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0083] The analysis unit can determine the analysis priority based on the time of successful individual's data submission. For example, if the successful individual's data was submitted recently, the analysis unit prioritizes analysis. For example, the analysis unit evaluates the time of successful individual's data submission and prioritizes analysis of the most recently submitted data. The analysis unit can also lower the priority of the successful individual's data if the successful individual's data is old. For example, the analysis unit evaluates the time of successful individual's data submission and lowers the priority of older data. The analysis unit can also dynamically adjust the analysis priority based on the time of successful individual's data submission. For example, the analysis unit evaluates the time of successful individual's data submission in real time and adjusts the analysis priority. This enables more efficient analysis by determining the analysis priority based on the time of successful individual's data submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time of successful individual's data submission into AI and have the AI ​​determine the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data of successful individuals. For example, if the data of successful individuals is highly relevant to the user's situation, the analysis unit prioritizes analysis. For example, the analysis unit evaluates the relevance of the data of successful individuals and prioritizes analysis of highly relevant data. The analysis unit can also postpone the order of analysis if the data of successful individuals is less relevant to the user's situation. For example, the analysis unit evaluates the relevance of the data of successful individuals and postpones the order of analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data of successful individuals. For example, the analysis unit evaluates the relevance of the data of successful individuals in real time and adjusts the order of analysis. This allows for adjusting the order of analysis based on the relevance of the data of successful individuals to provide more appropriate analysis results. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the relevance of the data of successful individuals into AI and have the AI ​​determine the order of analysis.

[0085] The analysis unit can adjust the use of technical terms in the analysis according to the expertise level of the successful person. For example, if the expertise level of the successful person is high, the analysis unit provides analysis results that use a lot of technical terms. For example, the analysis unit evaluates the expertise level of the successful person and uses a lot of technical terms if the expertise level is high. The analysis unit can also provide analysis results that avoid technical terms if the expertise level of the successful person is low. For example, the analysis unit evaluates the expertise level of the successful person and avoids technical terms if the expertise level is low. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the expertise level of the successful person. For example, the analysis unit evaluates the expertise level of the successful person in real time and adjusts the use of technical terms. This allows for adjusting the use of technical terms in the analysis according to the expertise level of the successful person, thereby providing more appropriate analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the expertise level of the successful person into AI and have the AI ​​determine the use of technical terms.

[0086] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit relaxes the matching criteria to perform matching that places less strain on the user. For example, the matching unit analyzes the user's emotion data and relaxes the matching criteria if the user is feeling stressed. Furthermore, if the user is relaxed, the matching unit can tighten the matching criteria to perform highly accurate matching. For example, the matching unit detects a relaxed state based on the user's emotion data and tightens the matching criteria. Furthermore, if the user is in a hurry, the matching unit can simplify the matching criteria to perform quick matching. For example, the matching unit detects a state of hurry based on the user's emotion data and simplify the matching criteria. This allows for more appropriate matching by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0087] The matching unit can improve the accuracy of matching by taking into account the correlation between the user's data and the data of successful individuals. The matching unit, for example, analyzes the correlation between the user's data and the data of successful individuals to improve the accuracy of matching. For example, the matching unit evaluates the correlation between the user's data and the data of successful individuals to improve the accuracy of matching. The matching unit can also optimize the matching algorithm by taking into account the correlation between the user's data and the data of successful individuals. For example, the matching unit adjusts the parameters of the matching algorithm based on the correlation between the user's data and the data of successful individuals. The matching unit can also dynamically adjust the accuracy of matching based on the correlation between the user's data and the data of successful individuals. For example, the matching unit evaluates the correlation between the user's data and the data of successful individuals in real time and adjusts the accuracy of matching. This allows the accuracy of matching to be improved by taking into account the correlation between the user's data and the data of successful individuals. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the correlation between the user's data and the data of successful individuals into AI to improve the accuracy of matching.

[0088] The matching unit can perform matching taking into account attribute information of the user. The matching unit performs matching taking into account attribute information such as the user's age and gender. For example, the matching unit evaluates the user's age and gender and reflects them in the matching. The matching unit can also perform matching taking into account attribute information such as the user's occupation and experience. For example, the matching unit evaluates the user's occupation and experience and reflects them in the matching. The matching unit can also optimize the matching algorithm based on the user's attribute information. For example, the matching unit adjusts parameters of the matching algorithm based on the user's attribute information. This enables more appropriate matching by taking into account the user's attribute information. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's attribute information into AI to improve the accuracy of matching.

[0089] The matching unit can weight the matching based on the frequency of user data submission. For example, if a user frequently submits data, the matching unit can weight the data more highly to improve the accuracy of the matching. For example, the matching unit evaluates the frequency of user data submission and weights frequently submitted data more highly. Furthermore, if a user rarely submits data, the matching unit can weight the data less highly to balance the matching. For example, the matching unit evaluates the frequency of user data submission and weights infrequently submitted data less highly. Furthermore, the matching unit can dynamically adjust the matching weights based on the frequency of user data submission. For example, the matching unit evaluates the frequency of user data submission in real time and adjusts the matching weights. Thus, by weighting the matching based on the frequency of user data submission, the accuracy of the matching can be improved. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input the frequency of user data submission into AI and have the AI ​​determine the matching weights.

[0090] The matching unit can estimate the user's emotions and adjust the order in which the matching results are displayed based on the estimated user emotions. For example, if the user is feeling stressed, the matching unit can display important results first, thereby reducing the user's burden. For example, the matching unit can analyze the user's emotion data and prioritize displaying important results when the user is feeling stressed. Furthermore, if the user is relaxed, the matching unit can display detailed results in an orderly manner to deepen the user's understanding. For example, the matching unit can detect a relaxed state based on the user's emotion data and display detailed results in an orderly manner. Furthermore, if the user is in a hurry, the matching unit can display results that highlight the main points first to provide information quickly. For example, the matching unit can detect a state of hurry based on the user's emotion data and prioritize displaying results that highlight the main points. This allows for more appropriate information to be provided by adjusting the order in which the matching results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0091] The matching unit can perform matching taking into account the geographical distribution of users. For example, if a user is in a specific region, the matching unit prioritizes matching data of successful people related to that region. For example, the matching unit evaluates the geographical distribution of users and prioritizes matching data of successful people related to that region. The matching unit can also optimize the matching algorithm taking into account the geographical distribution of users. For example, the matching unit adjusts parameters of the matching algorithm based on the geographical distribution of users. The matching unit can also improve the accuracy of matching based on the geographical distribution of users. For example, the matching unit evaluates the geographical distribution of users in real time and improves the accuracy of matching. This enables more appropriate matching by taking the geographical distribution of users into consideration. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the geographical distribution data of users into AI to improve the accuracy of matching.

[0092] The matching unit can improve the accuracy of matching by referring to the user's related literature. The matching unit improves the accuracy of matching, for example, based on the related literature referenced by the user. For example, the matching unit evaluates the user's related literature and reflects it in the matching. The matching unit can also optimize the matching algorithm by referring to the user's related literature. For example, the matching unit adjusts parameters of the matching algorithm based on the user's related literature. The matching unit can also dynamically adjust the accuracy of matching based on the user's related literature. For example, the matching unit evaluates the user's related literature in real time and adjusts the accuracy of matching. In this way, the accuracy of matching can be improved by referring to the user's related literature. Some or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's related literature data into AI and use AI to improve the accuracy of matching.

[0093] The matching unit can perform matching taking into account the user's market value. For example, if the user's market value is high, the matching unit assigns a high weight to the data, thereby improving the accuracy of the matching. For example, the matching unit evaluates the user's market value and assigns a high weight to the data when the market value is high. Furthermore, if the user's market value is low, the matching unit can assign a low weight to the data to balance the matching. For example, the matching unit evaluates the user's market value and assigns a low weight to the data when the market value is low. Furthermore, the matching unit can dynamically adjust the matching weight based on the user's market value. For example, the matching unit evaluates the user's market value in real time and adjusts the matching weight. This enables more appropriate matching by taking the user's market value into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's market value data into AI, thereby improving the accuracy of the matching using AI.

[0094] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit makes simple, highly visible suggestions. For example, the suggestion unit analyzes the user's emotion data and makes concise suggestions if the user is feeling stressed. The suggestion unit can also make detailed suggestions if the user is relaxed, thereby deepening the user's understanding. For example, the suggestion unit detects a relaxed state based on the user's emotion data and makes detailed suggestions. The suggestion unit can also make concise suggestions that focus on the main points if the user is in a hurry. For example, the suggestion unit detects a rushed state based on the user's emotion data and makes concise suggestions. This enables more appropriate suggestions to be made by adjusting the way suggestions are expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0095] The suggestion unit can adjust the level of detail of the proposal based on the importance of the successful person's behavioral patterns. For example, if the importance of the successful person's behavioral patterns is high, the suggestion unit makes a detailed proposal and provides it to the user. For example, the suggestion unit evaluates the importance of the successful person's behavioral patterns and makes a detailed proposal if the importance is high. The suggestion unit can also make a concise proposal and provide it to the user if the importance of the successful person's behavioral patterns is low. For example, the suggestion unit evaluates the importance of the successful person's behavioral patterns and makes a concise proposal if the importance is low. The suggestion unit can also dynamically adjust the level of detail of the proposal depending on the importance of the successful person's behavioral patterns. For example, the suggestion unit evaluates the importance of the successful person's behavioral patterns in real time and adjusts the level of detail of the proposal. This enables more appropriate suggestions to be made by adjusting the level of detail of the proposal based on the importance of the successful person's behavioral patterns. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the successful person's behavioral pattern data into AI and have the AI ​​determine the level of detail of the proposal.

[0096] The suggestion unit can apply different suggestion algorithms depending on the category of successful people. For example, in the case of a successful business person, the suggestion unit applies a business-specific suggestion algorithm. For example, the suggestion unit uses a business-specific suggestion algorithm based on data of successful business people. The suggestion unit can also apply a sports-specific suggestion algorithm to a successful sports person. For example, the suggestion unit uses a sports-specific suggestion algorithm based on data of successful sports people. The suggestion unit can also apply an academic-specific suggestion algorithm to a successful academic person. For example, the suggestion unit uses an academic-specific suggestion algorithm based on data of successful academic people. This enables more appropriate suggestions to be made by applying a suggestion algorithm depending on the category of successful people. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input category data of successful people into AI and have the AI ​​select an appropriate suggestion algorithm.

[0097] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results. The suggestion unit, for example, improves the accuracy of current suggestions based on the user's past suggestion results. For example, the suggestion unit refers to the user's past suggestion results and reflects them in the current suggestion. The suggestion unit can also optimize the suggestion algorithm by referring to the user's past suggestion results. For example, the suggestion unit adjusts algorithm parameters based on the user's past suggestion results. The suggestion unit can also dynamically adjust the accuracy of suggestions by utilizing the user's past suggestion results. For example, the suggestion unit refers to the user's past suggestion results in real time and adjusts the accuracy of suggestions. In this way, the accuracy of suggestions can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion result data into AI and cause the AI ​​to improve the accuracy of suggestions.

[0098] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make short, concise suggestions. For example, the suggestion unit can analyze the user's emotion data and make concise suggestions if the user is feeling stressed. Furthermore, if the user is relaxed, the suggestion unit can make detailed suggestions to deepen the user's understanding. For example, the suggestion unit can detect a relaxed state based on the user's emotion data and make detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make concise and quick suggestions. For example, the suggestion unit can detect a hurried state based on the user's emotion data and make concise suggestions. This allows for more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0099] The suggestion unit can determine the priority of proposals based on the time of successful individual data submission. For example, the suggestion unit prioritizes proposals when the successful individual's data was submitted recently. For example, the suggestion unit evaluates the time of successful individual data submission and prioritizes the most recently submitted data. The suggestion unit can also lower the priority of proposals when the successful individual's data is old. For example, the suggestion unit evaluates the time of successful individual data submission and lowers the priority of older data. The suggestion unit can also dynamically adjust the priority of proposals based on the time of successful individual data submission. For example, the suggestion unit evaluates the time of successful individual data submission in real time and adjusts the priority of proposals. This enables more efficient proposals by determining the priority of proposals based on the time of successful individual data submission. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the time of successful individual data submission into AI and have the AI ​​determine the priority of proposals.

[0100] The suggestion unit can adjust the order of suggestions based on the relevance of the data of successful individuals. For example, if the data of successful individuals is highly relevant to the user's situation, the suggestion unit prioritizes suggestions. For example, the suggestion unit evaluates the relevance of the data of successful individuals and prioritizes proposing highly relevant data. The suggestion unit can also postpone the order of suggestions if the data of successful individuals is less relevant to the user's situation. For example, the suggestion unit evaluates the relevance of the data of successful individuals and postpones the order of suggestions of less relevant data. The suggestion unit can also dynamically adjust the order of suggestions based on the relevance of the data of successful individuals. For example, the suggestion unit evaluates the relevance of the data of successful individuals in real time and adjusts the order of suggestions. This enables more appropriate suggestions by adjusting the order of suggestions based on the relevance of the data of successful individuals. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input the relevance of the data of successful individuals into AI and have the AI ​​determine the order of suggestions.

[0101] The suggestion unit can adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, if the user's level of expertise is high, the suggestion unit makes suggestions that use a lot of technical terms. For example, the suggestion unit evaluates the user's level of expertise and uses a lot of technical terms when the user's level of expertise is high. The suggestion unit can also make suggestions that avoid technical terms when the user's level of expertise is low. For example, the suggestion unit evaluates the user's level of expertise and avoids technical terms when the user's level of expertise is low. The suggestion unit can also dynamically adjust the use of technical terms in the suggestions depending on the user's level of expertise. For example, the suggestion unit evaluates the user's level of expertise in real time and adjusts the use of technical terms. This enables more appropriate suggestions by adjusting the use of technical terms in the suggestions depending on the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise into AI and have the AI ​​determine the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, comparison unit, and suggestion 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 data such as the behavioral history, options, and results of successful individuals using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to extract the behavioral patterns and characteristics of successful individuals. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares the user's data with the data of successful individuals. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests optimal actions and options to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, comparison unit, and suggestion 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 data such as the behavioral history, options, and results of successful individuals using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to extract the behavioral patterns and characteristics of successful individuals. The comparison unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and compares the user's data with the data of successful individuals. The suggestion unit, for example, is realized by the control unit 46A of the smart glasses 214 and suggests optimal actions and options to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, comparison unit, and suggestion unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects data such as the behavioral history, options, and results of successful individuals using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to extract the behavioral patterns and characteristics of successful individuals. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares the user's data with the data of successful individuals. The suggestion unit is realized, for example, by the control unit 46A of the headset terminal 314 and suggests optimal actions and options to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, comparison unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data such as the behavioral history, options, and results of successful individuals using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract the behavioral patterns and characteristics of successful individuals. The comparison unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and compares the user's data with the data of successful individuals. The suggestion unit is realized, for example, by the control unit 46A of the robot 414, and suggests optimal actions and options to the user.

[0102] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0103] The determination system may further include a feedback unit. The feedback unit collects results after the user performs the suggested action and provides them to the analysis unit. For example, the feedback unit may record successes and failures after the user performs the suggested action and transmit the records to the analysis unit. The feedback unit may also estimate the user's emotions and collect satisfaction and stress levels with the suggested action. For example, the feedback unit may collect emotional data after the user performs the suggested action and provide the data to the analysis unit. This allows the system to improve the accuracy of suggestions based on the user's feedback.

[0104] The collection unit can also collect health data of the user. For example, it collects data such as the user's heart rate, sleep patterns, and amount of exercise, and provides the collected data to the analysis unit. The collection unit can also estimate the user's physical condition and stress level based on the user's health data. For example, if the user's heart rate is high, it can estimate that the user is feeling stressed, and adjust the collection timing. This makes it possible to collect data according to the user's health condition.

[0105] The suggestion unit can further adjust the format of the suggestion based on the user's learning style. For example, if the user has a visual learning style, the suggestion unit can make suggestions using graphs or charts. If the user has an auditory learning style, the suggestion unit can make suggestions using audio messages. Furthermore, if the user has a hands-on learning style, the suggestion unit can make suggestions showing specific action steps. This enables effective suggestions to be made according to the user's learning style.

[0106] The collection unit can further collect social network data of the user. For example, it can collect information about the types of people the user interacts with and their relationships. The collection unit can also evaluate the user's influence and trustworthiness based on the user's social network data. For example, if a user has many followers, it can evaluate the user as being highly influential. This makes it possible to collect data based on the user's social network.

[0107] The collection unit can further collect data based on the user's hobbies and interests. For example, if the user has a particular hobby, data on successful people related to that hobby is preferentially collected. The collection unit can also filter related data based on the user's interests. For example, if the user is interested in sports, data on successful people related to sports is collected. This makes it possible to collect data according to the user's hobbies and interests.

[0108] The analysis unit can further improve the accuracy of the analysis based on the user's past behavioral history. For example, it can analyze the actions the user has taken in the past and reflect the results in the current analysis. The analysis unit can also extract behavioral patterns based on the user's past behavioral history and improve the accuracy of suggestions. For example, it can analyze the behavioral patterns that have been successful for the user in the past and reflect them in suggestions for similar situations. This enables highly accurate analysis based on the user's past behavioral history.

[0109] The matching unit can further perform matching taking into account the user's cultural background. For example, if the user belongs to a specific cultural sphere, the matching unit will prioritize matching data of successful people related to that culture. The matching unit can also adjust the matching algorithm based on the user's cultural background. For example, if the user belongs to a different cultural sphere, the matching unit will apply matching criteria appropriate for that culture. This enables appropriate matching according to the user's cultural background.

[0110] The suggestion unit can further monitor the user's degree of goal achievement and adjust the frequency of suggestions. For example, if the user is approaching the goal, the frequency of suggestions can be reduced to enable the user to act independently. Alternatively, if the user is moving away from the goal, the frequency of suggestions can be increased to provide stronger support. Furthermore, the content of suggestions can be adjusted based on the user's degree of goal achievement. For example, if the user is approaching the goal, a suggestion for moving on to the next step can be made. This makes it possible to make effective suggestions according to the user's degree of goal achievement.

[0111] The suggestion unit can further estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion timing can be delayed to make suggestions when the user is relaxed. Also, if the user is relaxed, the suggestion can be made immediately to make suggestions when the user is highly focused. Furthermore, if the user is in a hurry, the suggestion timing can be advanced to provide necessary information quickly. This makes it possible to make appropriate suggestions according to the user's emotions.

[0112] The suggestion unit can further customize the content of the suggestion based on user feedback. For example, if the user provides feedback on a suggested action, the suggestion unit can adjust the content of the suggestion based on the feedback. The suggestion unit can also change the format or presentation of the suggestion based on user feedback. For example, if the user prefers visual suggestions, the suggestion unit can provide suggestions using graphs or charts. This allows for customized suggestions based on user feedback.

[0113] The processing flow of the second embodiment will be briefly explained below.

[0114] Step 1: The collection department collects data on successful people. This data includes their behavioral history, choices, results, progress and decisions made on past projects, resource utilization methods, and network information. The collection department records and collects this data in detail. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the behavioral patterns and characteristics of successful people. The analysis unit uses AI to model the behavioral patterns and characteristics of successful people and adjusts the level of analysis detail based on the importance of the behavioral patterns. It also applies different analysis algorithms depending on the category of successful people. Step 3: The matching unit compares the user's data based on the behavioral patterns and characteristics extracted by the analysis unit. The matching unit compares data such as the user's current situation, goals, and resources with the data of successful people, estimates the user's emotions, and adjusts the matching criteria. The accuracy of the matching is also improved by taking into account the correlation between the user's data and the data of successful people. Step 4: The suggestion unit proposes actions based on the results of the comparison by the matching unit. The suggestion unit proposes optimal actions and options for the user, estimates the user's emotions, and adjusts the way the suggestions are presented. The suggestion unit also adjusts the level of detail of the suggestions based on the importance of the behavioral patterns of successful people.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0119] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] [Explanation of symbols]

[0187] 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 department that collects data on successful people; an analysis unit that analyzes the data collected by the collection unit and extracts behavioral patterns and characteristics of successful people; a matching unit that matches the behavioral patterns and characteristics extracted by the analysis unit with user data; a suggestion unit that suggests an action based on the result of the comparison by the comparison unit; A system characterized by:

2. The collecting unit Collect data on the behavioral history, choices, and outcomes of successful people 2. The system of claim 1.

3. The proposal unit Providing a text message or dashboard display as a way to notify users of suggestions 2. The system of claim 1.

4. The collecting unit Estimate user emotions and adjust the timing of collecting data on successful users based on the estimated user emotions.

2. The system of claim 1.

5. The collecting unit Collect detailed information on the progress and decisions of successful people's past projects.

2. The system of claim 1.

6. The collecting unit Collect information on how successful people utilize resources and their networks 2. The system of claim 1.

7. The collecting unit Organize and collect the behavioral history of successful people along a timeline 2. The system of claim 1.

8. The collecting unit Estimate user emotions and prioritize the data to be collected based on the estimated user emotions.

2. The system of claim 1.

Images