System

Abstract

An object of a system according to an embodiment is to visualize a value of daily activity of a user as a monetary value.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data of an activity history, preference, or social influence of a user. The analysis unit analyzes the data collected by the collection unit and calculates the monetary value of each activity. The providing unit provides the user with the monetary value calculated by the analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty in concretely grasping the value of a user's daily activities, and there are limitations to improving self-evaluation and time management.

[0005] The system according to the embodiment aims to visualize the value of a user's daily activities as a monetary value. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data on a user's activity history, preferences, and social influence. The analysis unit analyzes the data collected by the collection unit and calculates a monetary value for each activity. The provision unit provides the user with the monetary value calculated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the value of a user's daily activities as a monetary value. [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 value conversion system according to an embodiment of the present invention converts all actions and activities in a user's daily life into monetary value and provides the monetary value. The value conversion system collects data such as the user's activity history, preferences, and social influence, and uses AI to analyze this data to calculate the monetary value of each activity and provide it to the user. For example, the value conversion system collects detailed data such as the user's diet, exercise, and books read. Next, the value conversion system uses AI to analyze the collected data and calculate the monetary value of each activity. For example, the value conversion system converts the health benefits of exercise or the value of knowledge gained from reading into monetary value. Finally, the value conversion system provides the calculated monetary value to the user. This allows the user to visualize the value of their activities and use their time in the most valuable way. For example, if the health benefits of exercise are high, the user can prioritize exercise. Similarly, if the value of knowledge gained from reading is high, the user can prioritize reading. The value conversion system thus helps users use their time in the most valuable way, thereby improving the quality of their daily lives. For example, users can prioritize exercise to live a healthy life, reading to deepen their knowledge, or interacting with others to increase their social influence. This allows users to make better use of their time and live a more fulfilling life.

[0029] A value conversion system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data on a user's activity history, preferences, and social influence. For example, the collection unit can collect detailed data such as the user's diet, exercise, and books read. The collection unit can also collect data on the user's website browsing history, purchase history, hobbies, and other information. The collection unit can also collect data on the user's social influence, such as the number of followers, likes, and shares. The analysis unit analyzes the data collected by the collection unit and calculates the monetary value of each activity. For example, the analysis unit converts the health benefits of exercising or the value of knowledge gained from reading into monetary value. The analysis unit analyzes the data using specific algorithms and criteria to calculate the monetary value. For example, the analysis unit can analyze the data using machine learning algorithms or statistical methods. The provision unit provides the user with the monetary value calculated by the analysis unit. For example, the provision unit can provide feedback to the user using an application interface or a notification method. The providing unit can provide information to the user by a dashboard, a notification pop-up, a push notification, an email notification, etc. As a result, the value conversion system according to the embodiment can convert the user's activities into monetary value and support the user in using their time in the most valuable way.

[0030] The collection unit can collect data using a smartphone sensor or a wearable device. The collection unit can collect data using, for example, the smartphone's GPS, acceleration sensor, gyro sensor, etc. The collection unit can also collect data using a wearable device such as a smartwatch or fitness tracker. For example, the collection unit can collect user location information using the smartphone's GPS. The collection unit can also collect user exercise data using an acceleration sensor. Furthermore, the collection unit can collect health data such as the user's heart rate and number of steps using a smartwatch. This enables more detailed data collection by using a smartphone or a wearable device. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data acquired by the smartphone's sensor into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the data using specific algorithms and standards and calculate the monetary value of each activity. The analysis unit can analyze the data using, for example, a machine learning algorithm. For example, the analysis unit inputs the user's activity data and calculates the monetary value using a machine learning model. The analysis unit can also analyze the data using statistical methods. For example, the analysis unit statistically analyzes the user's activity data and calculates the monetary value. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit extracts patterns from the user's activity data and calculates the monetary value. This enables more accurate calculation of the monetary value by using specific algorithms and standards. 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 user's activity data to a generation AI and have the generation AI calculate the monetary value.

[0032] The providing unit can provide feedback to the user using an application interface or a notification method. The providing unit can provide feedback to the user using, for example, a dashboard. For example, the providing unit can display the monetary value of the user's activity on a dashboard, allowing the user to check the value of their activity at a glance. The providing unit can also provide feedback to the user using a notification pop-up. For example, the providing unit can display the monetary value of the user's activity in a notification pop-up, providing feedback to the user in real time. The providing unit can also provide feedback to the user using a push notification. For example, the providing unit can send the monetary value of the user's activity via a push notification, thereby immediately notifying the user. The providing unit can also provide feedback to the user using an email notification. For example, the providing unit can send the monetary value of the user's activity via email, providing detailed feedback to the user. This makes it possible to provide effective feedback to the user by using an application interface or a notification method. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's activity data into the generation AI and have the generation AI execute the feedback content.

[0033] The collection unit can anonymize data and implement security measures to protect the user's privacy. The collection unit can, for example, anonymize the data. For example, the collection unit deletes the user's personal information and anonymizes the data. The collection unit can also mask the data. For example, the collection unit masks the user's data to make it impossible to identify the individual. The collection unit can also implement security measures. For example, the collection unit encrypts the data to protect it from unauthorized access. The collection unit can also perform access control to limit access to the data. For example, the collection unit allows only specific users to access the data. In this way, the data anonymization and security measures can protect the user's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can have the generation AI perform the data anonymization and security measures.

[0034] The collection unit can analyze the user's past activity history and select the optimal data collection method. The collection unit can, for example, adjust the frequency of data collection based on activities the user frequently performed in the past. The collection unit can also concentrate data collection during specific time periods based on the user's past activity history. Furthermore, the collection unit can analyze the user's past activity patterns and select the most efficient data collection method. For example, the collection unit analyzes the user's past activity history and selects the optimal data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past activity history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past activity data into a generation AI and cause the generation AI to select the optimal data collection method.

[0035] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting data related to a project the user is currently working on. The collection unit can also filter health-related data based on the user's current health condition. Furthermore, the collection unit can collect relevant data based on the user's current interests. For example, the collection unit filters data based on the user's current living situation and areas of interest. This allows more relevant data to be collected by filtering the data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current living situation and areas of interest to the generation AI and have the generation AI filter the data.

[0036] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. For example, the collection unit selects the optimal collection means depending on the user's input method. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant data.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and collects related data. In this way, related data can be efficiently collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can adjust the frequency of data collection based on feedback provided by the user in the past. The collection unit can also preferentially use a specific data collection method based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal data collection method. For example, the collection unit customizes the collection method by reflecting the user's past feedback. In this way, the optimal data collection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the activity. For example, the analysis unit can perform a detailed analysis on important activities. The analysis unit can also perform a concise analysis on less important activities. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the activity. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the activity. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the activity. 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 activity importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of activity. For example, the analysis unit can apply an analysis algorithm that emphasizes health benefits to health-related activities. The analysis unit can also apply an analysis algorithm that emphasizes knowledge value to knowledge-related activities. The analysis unit can also apply an analysis algorithm that emphasizes influence to activities related to social influence. For example, the analysis unit applies different analysis algorithms depending on the category of activity. This enables more accurate analysis by applying different analysis algorithms depending on the activity category. 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 activity category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by extracting specific patterns from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the activity was performed. For example, the analysis unit can prioritize analysis of recently performed activities. The analysis unit can also lower the priority of analysis of past activities. Furthermore, the analysis unit can adjust the order of analysis depending on the time when the activity was performed. For example, the analysis unit determines the priority of analysis based on the time when the activity was performed. This enables efficient analysis by determining the priority of analysis based on the time when the activity was performed. 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 data on the time when the activity was performed to the generation AI and have the generation AI determine the priority of analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of activities. For example, the analysis unit can prioritize analysis of highly relevant activities. The analysis unit can also lower the priority of analysis of less relevant activities. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of activities. For example, the analysis unit adjusts the order of analysis based on the relevance of activities. This enables efficient analysis by adjusting the order of analysis based on the relevance of activities. 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 activity relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0045] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of more understandable analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0046] The providing unit can adjust the level of detail of the information provided based on the monetary value of the activity at the time of providing. For example, the providing unit can provide detailed information for activities with a high monetary value. The providing unit can also provide concise information for activities with a low monetary value. Furthermore, the providing unit can adjust the level of detail of the information to be provided according to the monetary value of the activity. For example, the providing unit adjusts the level of detail of the information provided based on the monetary value of the activity. This enables efficient information provision by adjusting the level of detail of the information provided based on the monetary value of the activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input monetary value data of the activity to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0047] The providing unit can improve the accuracy of the information provided by referring to the user's past feedback when providing the information. The providing unit can, for example, adjust the content of the information to be provided based on the user's past feedback. The providing unit can also preferentially use a specific information provision method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and suggest an optimal information provision method. For example, the providing unit can improve the accuracy of the information provided by referring to the user's past feedback. In this way, the accuracy of the information provided can be improved by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0048] The providing unit can customize the provided content based on the user's current living situation at the time of providing the content. For example, the providing unit can provide information related to a project the user is currently working on. The providing unit can also provide health-related information based on the user's current health condition. Furthermore, the providing unit can provide relevant information based on the user's current interests and concerns. For example, the providing unit customizes the provided content based on the user's current living situation. In this way, by customizing the provided content based on the user's current living situation, more relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current living situation data into a generating AI and cause the generating AI to customize the provided content.

[0049] The providing unit can provide optimal information by taking into account the user's geographical location information. For example, when the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing information related to the user's home. For example, the providing unit provides optimal information by taking into account the user's geographical location information. In this way, optimal information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to the generation AI and cause the generation AI to provide optimal information.

[0050] The providing unit can analyze the user's social media activities and provide the related information at the time of providing. The providing unit can provide the related information based on, for example, information shared by the user on social media. The providing unit can also analyze the user's social media activity patterns and provide the related information. Furthermore, the providing unit can provide the related information by referring to the activities of the user's friends on social media. For example, the providing unit analyzes the user's social media activities and provides the related information. In this way, the related information can be efficiently provided by analyzing the user's social media activities. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide the related information.

[0051] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit can adjust the frequency of information provision based on feedback provided by the user in the past. The providing unit can also preferentially use a specific information providing method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and suggest an optimal information providing method. For example, the providing unit customizes the information providing method by reflecting the user's past feedback. In this way, the optimal information providing method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the information providing method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The value conversion system can also collect social evaluations of a user's activities and reflect those evaluations in the monetary value in the analysis unit. For example, if a user performs volunteer work, the system collects evaluations and comments from other users about the activity and converts those evaluations into monetary value in the analysis unit. The system can also collect reactions to information shared by the user on social media (number of likes, shares, comments, etc.) and reflect those reactions in the monetary value in the analysis unit. Furthermore, the system can collect evaluations from other participants of events or seminars that the user attended and convert those evaluations into monetary value in the analysis unit. This allows for a more comprehensive value assessment by reflecting social evaluations of the user's activities in the monetary value.

[0054] The collection unit can collect environmental data related to the user's activities, and the analysis unit can reflect that data in the monetary value. For example, weather data (temperature, humidity, wind speed, etc.) when the user exercises can be collected, and the analysis unit can evaluate the effectiveness of the exercise based on that data. The collection unit can also collect the ambient noise level and lighting brightness when the user reads, and the analysis unit can evaluate the level of concentration while reading based on that data. Furthermore, data on the origin and nutritional value of ingredients when the user eats can be collected, and the analysis unit can evaluate the health benefits of the meal based on that data. In this way, by reflecting environmental data related to the user's activities in the monetary value, more precise value assessments can be made.

[0055] The analysis unit can also evaluate the long-term impact of a user's activities and reflect that impact in monetary value. For example, it can evaluate the long-term health benefits of a user exercising regularly and convert those benefits into monetary value. It can also evaluate the accumulation of knowledge and improvement of skills gained by a user continuing to read and reflect those effects in monetary value. It can also evaluate the expansion of personal connections and improvement of social influence gained by a user engaging in social activities over a long period of time and convert those effects into monetary value. In this way, by reflecting the long-term impact of a user's activities in monetary value, a more comprehensive value assessment is possible.

[0056] The providing unit can also customize feedback for a user's activity based on the user's level of goal achievement. For example, when providing the effect of exercise as a monetary value for a health goal set by the user, feedback according to the level of goal achievement can be provided. Also, when providing the effect of reading as a monetary value for a learning goal set by the user, feedback according to the level of goal achievement can be provided. Furthermore, when providing the effect of social activities as a monetary value for a social goal set by the user, feedback according to the level of goal achievement can be provided. In this way, more effective support can be provided by customizing feedback based on the user's level of goal achievement.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects data on the user's activity history, preferences, and social influence. For example, detailed data such as the user's diet, exercise, and books read is collected. Data on the user's website browsing history, purchase history, and hobbies is also collected. Data on the user's social influence, such as the number of followers, likes, and shares, is also collected. Step 2: The analysis unit analyzes the data collected by the collection unit and calculates the monetary value of each activity. For example, the health benefits of exercising or the value of knowledge gained from reading can be converted into monetary value. The analysis unit analyzes the data using specific algorithms and standards, and calculates the monetary value using machine learning algorithms and statistical methods. Step 3: The providing unit provides the user with the monetary value calculated by the analysis unit. For example, the providing unit provides feedback to the user using the application interface or notification method. The providing unit provides information to the user by using methods such as a dashboard, notification pop-up, push notification, or email notification.

[0059] (Example 2) A value conversion system according to an embodiment of the present invention converts all actions and activities in a user's daily life into monetary value and provides the monetary value. The value conversion system collects data such as the user's activity history, preferences, and social influence, and uses AI to analyze this data to calculate the monetary value of each activity and provide it to the user. For example, the value conversion system collects detailed data such as the user's diet, exercise, and books read. Next, the value conversion system uses AI to analyze the collected data and calculate the monetary value of each activity. For example, the value conversion system converts the health benefits of exercise or the value of knowledge gained from reading into monetary value. Finally, the value conversion system provides the calculated monetary value to the user. This allows the user to visualize the value of their activities and use their time in the most valuable way. For example, if the health benefits of exercise are high, the user can prioritize exercise. Similarly, if the value of knowledge gained from reading is high, the user can prioritize reading. The value conversion system thus helps users use their time in the most valuable way, thereby improving the quality of their daily lives. For example, users can prioritize exercise to live a healthy life, reading to deepen their knowledge, or interacting with others to increase their social influence. This allows users to make better use of their time and live a more fulfilling life.

[0060] A value conversion system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects data on a user's activity history, preferences, and social influence. For example, the collection unit can collect detailed data such as the user's diet, exercise, and books read. The collection unit can also collect data on the user's website browsing history, purchase history, hobbies, and other information. The collection unit can also collect data on the user's social influence, such as the number of followers, likes, and shares. The analysis unit analyzes the data collected by the collection unit and calculates the monetary value of each activity. For example, the analysis unit converts the health benefits of exercising or the value of knowledge gained from reading into monetary value. The analysis unit analyzes the data using specific algorithms and criteria to calculate the monetary value. For example, the analysis unit can analyze the data using machine learning algorithms or statistical methods. The provision unit provides the user with the monetary value calculated by the analysis unit. For example, the provision unit can provide feedback to the user using an application interface or a notification method. The providing unit can provide information to the user by a dashboard, a notification pop-up, a push notification, an email notification, etc. As a result, the value conversion system according to the embodiment can convert the user's activities into monetary value and support the user in using their time in the most valuable way.

[0061] The collection unit can collect data using a smartphone sensor or a wearable device. The collection unit can collect data using, for example, the smartphone's GPS, acceleration sensor, gyro sensor, etc. The collection unit can also collect data using a wearable device such as a smartwatch or fitness tracker. For example, the collection unit can collect user location information using the smartphone's GPS. The collection unit can also collect user exercise data using an acceleration sensor. Furthermore, the collection unit can collect health data such as the user's heart rate and number of steps using a smartwatch. This enables more detailed data collection by using a smartphone or a wearable device. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data acquired by the smartphone's sensor into a generation AI and have the generation AI analyze the data.

[0062] The analysis unit can analyze the data using specific algorithms and standards and calculate the monetary value of each activity. The analysis unit can analyze the data using, for example, a machine learning algorithm. For example, the analysis unit inputs the user's activity data and calculates the monetary value using a machine learning model. The analysis unit can also analyze the data using statistical methods. For example, the analysis unit statistically analyzes the user's activity data and calculates the monetary value. The analysis unit can also analyze the data using data mining technology. For example, the analysis unit extracts patterns from the user's activity data and calculates the monetary value. This enables more accurate calculation of the monetary value by using specific algorithms and standards. 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 user's activity data to a generation AI and have the generation AI calculate the monetary value.

[0063] The providing unit can provide feedback to the user using an application interface or a notification method. The providing unit can provide feedback to the user using, for example, a dashboard. For example, the providing unit can display the monetary value of the user's activity on a dashboard, allowing the user to check the value of their activity at a glance. The providing unit can also provide feedback to the user using a notification pop-up. For example, the providing unit can display the monetary value of the user's activity in a notification pop-up, providing feedback to the user in real time. The providing unit can also provide feedback to the user using a push notification. For example, the providing unit can send the monetary value of the user's activity via a push notification, thereby immediately notifying the user. The providing unit can also provide feedback to the user using an email notification. For example, the providing unit can send the monetary value of the user's activity via email, providing detailed feedback to the user. This makes it possible to provide effective feedback to the user by using an application interface or a notification method. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's activity data into the generation AI and have the generation AI execute the feedback content.

[0064] The collection unit can anonymize data and implement security measures to protect the user's privacy. The collection unit can, for example, anonymize the data. For example, the collection unit deletes the user's personal information and anonymizes the data. The collection unit can also mask the data. For example, the collection unit masks the user's data to make it impossible to identify the individual. The collection unit can also implement security measures. For example, the collection unit encrypts the data to protect it from unauthorized access. The collection unit can also perform access control to limit access to the data. For example, the collection unit allows only specific users to access the data. In this way, the data anonymization and security measures can protect the user's privacy. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can have the generation AI perform the data anonymization and security measures.

[0065] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can collect data during a relaxing time. Furthermore, if the user is relaxed, the collection unit can also collect data in real time while the user is active. Furthermore, if the user is in a hurry, the collection unit can adjust the schedule to collect data later. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection according to the emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0066] The collection unit can analyze the user's past activity history and select the optimal data collection method. The collection unit can, for example, adjust the frequency of data collection based on activities the user frequently performed in the past. The collection unit can also concentrate data collection during specific time periods based on the user's past activity history. Furthermore, the collection unit can analyze the user's past activity patterns and select the most efficient data collection method. For example, the collection unit analyzes the user's past activity history and selects the optimal data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past activity history. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's past activity data into a generation AI and cause the generation AI to select the optimal data collection method.

[0067] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit can prioritize collecting data related to a project the user is currently working on. The collection unit can also filter health-related data based on the user's current health condition. Furthermore, the collection unit can collect relevant data based on the user's current interests. For example, the collection unit filters data based on the user's current living situation and areas of interest. This allows more relevant data to be collected by filtering the data based on the user's current living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's current living situation and areas of interest to the generation AI and have the generation AI filter the data.

[0068] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user is using text input, the collection unit can also prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can also prioritize collecting image data. For example, the collection unit selects the optimal collection means depending on the user's input method. This enables efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data to a generation AI and have the generation AI select the optimal collection means.

[0069] The collection unit can estimate the user's emotions and prioritize data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to activities. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting important data. For example, the collection unit estimates the user's emotions and prioritizes data to be collected based on the emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows for prioritized data collection based on the user's emotions, thereby enabling more important data to be prioritized. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the data priorities.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to the user's home. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant data.

[0071] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the user's social media activities and collects related data. In this way, related data can be efficiently collected by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into a generation AI and cause the generation AI to collect related data.

[0072] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can adjust the frequency of data collection based on feedback provided by the user in the past. The collection unit can also preferentially use a specific data collection method based on the user's past feedback. Furthermore, the collection unit can analyze the user's past feedback and suggest an optimal data collection method. For example, the collection unit customizes the collection method by reflecting the user's past feedback. In this way, the optimal data collection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0073] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. Furthermore, the analysis unit can provide analysis results that focus on the main points when the user is in a hurry. For example, the analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the emotions. The 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. This allows the analysis presentation to be adjusted based on the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation of the analysis.

[0074] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the activity. For example, the analysis unit can perform a detailed analysis on important activities. The analysis unit can also perform a concise analysis on less important activities. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance of the activity. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the activity. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the activity. 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 activity importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0075] During analysis, the analysis unit can apply different analysis algorithms depending on the category of activity. For example, the analysis unit can apply an analysis algorithm that emphasizes health benefits to health-related activities. The analysis unit can also apply an analysis algorithm that emphasizes knowledge value to knowledge-related activities. The analysis unit can also apply an analysis algorithm that emphasizes influence to activities related to social influence. For example, the analysis unit applies different analysis algorithms depending on the category of activity. This enables more accurate analysis by applying different analysis algorithms depending on the activity category. 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 activity category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0076] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by extracting specific patterns from the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI or without AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is stressed. Furthermore, the analysis unit can perform a concise analysis when the user is in a hurry. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the emotions. The 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. This allows for adjusting the length of the analysis based on the user's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the time when the activity was performed. For example, the analysis unit can prioritize analysis of recently performed activities. The analysis unit can also lower the priority of analysis of past activities. Furthermore, the analysis unit can adjust the order of analysis depending on the time when the activity was performed. For example, the analysis unit determines the priority of analysis based on the time when the activity was performed. This enables efficient analysis by determining the priority of analysis based on the time when the activity was performed. 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 data on the time when the activity was performed to the generation AI and have the generation AI determine the priority of analysis.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of activities. For example, the analysis unit can prioritize analysis of highly relevant activities. The analysis unit can also lower the priority of analysis of less relevant activities. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of activities. For example, the analysis unit adjusts the order of analysis based on the relevance of activities. This enables efficient analysis by adjusting the order of analysis based on the relevance of activities. 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 activity relevance data to a generation AI and cause the generation AI to adjust the order of analysis.

[0080] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. For example, the analysis unit adjusts the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of more understandable analysis results by adjusting the use of technical terminology in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI execute the use of technical terminology.

[0081] The providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is relaxed. The providing unit can also provide concise information when the user is stressed. Furthermore, the providing unit can provide information that is concise when the user is in a hurry. For example, the providing unit can estimate the user's emotions and adjust the presentation method of the information to be provided based on the emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This enables more appropriate information to be provided by adjusting the presentation method of the information based on the user's emotions. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the information.

[0082] The providing unit can adjust the level of detail of the information provided based on the monetary value of the activity at the time of providing. For example, the providing unit can provide detailed information for activities with a high monetary value. The providing unit can also provide concise information for activities with a low monetary value. Furthermore, the providing unit can adjust the level of detail of the information to be provided according to the monetary value of the activity. For example, the providing unit adjusts the level of detail of the information provided based on the monetary value of the activity. This enables efficient information provision by adjusting the level of detail of the information provided based on the monetary value of the activity. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input monetary value data of the activity to the generating AI and cause the generating AI to adjust the level of detail of the information provided.

[0083] The providing unit can improve the accuracy of the information provided by referring to the user's past feedback when providing the information. The providing unit can, for example, adjust the content of the information to be provided based on the user's past feedback. The providing unit can also preferentially use a specific information provision method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and suggest an optimal information provision method. For example, the providing unit can improve the accuracy of the information provided by referring to the user's past feedback. In this way, the accuracy of the information provided can be improved by referring to the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generation AI and cause the generation AI to improve the accuracy of the information provided.

[0084] The providing unit can customize the provided content based on the user's current living situation at the time of providing the content. For example, the providing unit can provide information related to a project the user is currently working on. The providing unit can also provide health-related information based on the user's current health condition. Furthermore, the providing unit can provide relevant information based on the user's current interests and concerns. For example, the providing unit customizes the provided content based on the user's current living situation. In this way, by customizing the provided content based on the user's current living situation, more relevant information can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's current living situation data into a generating AI and cause the generating AI to customize the provided content.

[0085] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing information related to relaxation. Furthermore, if the user is relaxed, the providing unit can prioritize providing information related to activities. Furthermore, if the user is in a hurry, the providing unit can prioritize providing important information. For example, the providing unit can estimate the user's emotions and determine the priority of information to be provided based on the emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows information to be prioritized based on the user's emotions, thereby providing more important information preferentially. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0086] The providing unit can provide optimal information by taking into account the user's geographical location information. For example, when the user is in a specific area, the providing unit can prioritize providing information related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing information related to the user's home. For example, the providing unit provides optimal information by taking into account the user's geographical location information. In this way, optimal information can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location data to the generation AI and cause the generation AI to provide optimal information.

[0087] The providing unit can analyze the user's social media activities and provide the related information at the time of providing. The providing unit can provide the related information based on, for example, information shared by the user on social media. The providing unit can also analyze the user's social media activity patterns and provide the related information. Furthermore, the providing unit can provide the related information by referring to the activities of the user's friends on social media. For example, the providing unit analyzes the user's social media activities and provides the related information. In this way, the related information can be efficiently provided by analyzing the user's social media activities. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to provide the related information.

[0088] The providing unit can customize the information providing method by reflecting the user's past feedback when providing information. For example, the providing unit can adjust the frequency of information provision based on feedback provided by the user in the past. The providing unit can also preferentially use a specific information providing method based on the user's past feedback. Furthermore, the providing unit can analyze the user's past feedback and suggest an optimal information providing method. For example, the providing unit customizes the information providing method by reflecting the user's past feedback. In this way, the optimal information providing method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into the generating AI and cause the generating AI to customize the information providing method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 can collect data on the user's activity history, preferences, and social influence using the camera 42 and microphone 38B of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the monetary value of each activity. For example, the provision unit is realized by the control unit 46A of the smart device 14, and provides the calculated monetary value to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision 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 can collect data on the user's activity history, preferences, and social influence using the camera 42 and microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the monetary value of each activity. For example, the provision unit is realized by the control unit 46A of the smart glasses 214, and provides the calculated monetary value to the user. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect data on the user's activity history, preferences, and social influence using the camera 42 and microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the monetary value of each activity. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314, and provides the calculated monetary value to the user. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision 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 can collect data on the user's activity history, preferences, and social influence using the camera 42 and microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and calculates the monetary value of each activity. For example, the provision unit is realized by the control unit 46A of the robot 414, and provides the calculated monetary value to the user.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The value conversion system can also collect social evaluations of a user's activities and reflect those evaluations in the monetary value in the analysis unit. For example, if a user performs volunteer work, the system collects evaluations and comments from other users about the activity and converts those evaluations into monetary value in the analysis unit. The system can also collect reactions to information shared by the user on social media (number of likes, shares, comments, etc.) and reflect those reactions in the monetary value in the analysis unit. Furthermore, the system can collect evaluations from other participants of events or seminars that the user attended and convert those evaluations into monetary value in the analysis unit. This allows for a more comprehensive value assessment by reflecting social evaluations of the user's activities in the monetary value.

[0091] The collection unit can collect environmental data related to the user's activities, and the analysis unit can reflect that data in the monetary value. For example, weather data (temperature, humidity, wind speed, etc.) when the user exercises can be collected, and the analysis unit can evaluate the effectiveness of the exercise based on that data. The collection unit can also collect the ambient noise level and lighting brightness when the user reads, and the analysis unit can evaluate the level of concentration while reading based on that data. Furthermore, data on the origin and nutritional value of ingredients when the user eats can be collected, and the analysis unit can evaluate the health benefits of the meal based on that data. In this way, by reflecting environmental data related to the user's activities in the monetary value, more precise value assessments can be made.

[0092] The analysis unit can also evaluate the long-term impact of a user's activities and reflect that impact in monetary value. For example, it can evaluate the long-term health benefits of a user exercising regularly and convert those benefits into monetary value. It can also evaluate the accumulation of knowledge and improvement of skills gained by a user continuing to read and reflect those effects in monetary value. It can also evaluate the expansion of personal connections and improvement of social influence gained by a user engaging in social activities over a long period of time and convert those effects into monetary value. In this way, by reflecting the long-term impact of a user's activities in monetary value, a more comprehensive value assessment is possible.

[0093] The providing unit can also customize feedback for a user's activity based on the user's level of goal achievement. For example, when providing the effect of exercise as a monetary value for a health goal set by the user, feedback according to the level of goal achievement can be provided. Also, when providing the effect of reading as a monetary value for a learning goal set by the user, feedback according to the level of goal achievement can be provided. Furthermore, when providing the effect of social activities as a monetary value for a social goal set by the user, feedback according to the level of goal achievement can be provided. In this way, more effective support can be provided by customizing feedback based on the user's level of goal achievement.

[0094] The collection unit can also estimate the user's emotions regarding the activity and adjust the data collection method based on the emotions. For example, if the user has positive emotions when exercising, detailed exercise data can be collected. Also, if the user has negative emotions when reading, collection of reading data can be refrained from. Furthermore, if the user is relaxed when eating, detailed meal data can be collected. In this way, by adjusting the data collection method based on the user's emotions, more appropriate data collection is possible.

[0095] The analysis unit can also estimate the user's emotions and adjust the way in which the analysis results are presented based on those emotions. For example, if the user is feeling stressed, the analysis results can be presented concisely. If the user is relaxed, the analysis results can be presented in detail. Furthermore, if the user is in a hurry, the analysis results can be presented in a concise manner. In this way, by adjusting the way in which the analysis results are presented based on the user's emotions, more appropriate information can be provided.

[0096] The providing unit can also estimate the user's emotions and adjust the content of the information to be provided based on the emotions. For example, if the user is feeling stressed, information related to relaxation can be provided. Also, if the user is relaxed, information related to activities can be provided. Furthermore, if the user is in a hurry, important information can be provided with priority. In this way, by adjusting the content of the information to be provided based on the user's emotions, more appropriate information can be provided.

[0097] The collection unit can also estimate the user's emotions and adjust the frequency of data collection based on those emotions. For example, if the user has positive emotions, the frequency of data collection can be increased. Also, if the user has negative emotions, the frequency of data collection can be decreased. Furthermore, if the user is relaxed, the frequency of data collection can be adjusted appropriately. In this way, adjusting the frequency of data collection based on the user's emotions enables more appropriate data collection.

[0098] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on those emotions. For example, if the user has positive emotions, important analysis can be prioritized. If the user has negative emotions, simple analysis can be prioritized. Furthermore, if the user is relaxed, normal analysis can be performed. Thus, by determining the priority of analysis based on the user's emotions, more efficient analysis becomes possible.

[0099] The providing unit can also estimate the user's emotions and adjust the format of the information to be provided based on the user's emotions. For example, if the user is feeling stressed, the information can be provided in a visually easy-to-understand format. If the user is relaxed, the information can be provided in a detailed text format. Furthermore, if the user is in a hurry, the main points can be provided in bullet points. In this way, by adjusting the format of the information to be provided based on the user's emotions, more appropriate information can be provided.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects data on the user's activity history, preferences, and social influence. For example, detailed data such as the user's diet, exercise, and books read is collected. Data on the user's website browsing history, purchase history, and hobbies is also collected. Data on the user's social influence, such as the number of followers, likes, and shares, is also collected. Step 2: The analysis unit analyzes the data collected by the collection unit and calculates the monetary value of each activity. For example, the health benefits of exercising or the value of knowledge gained from reading can be converted into monetary value. The analysis unit analyzes the data using specific algorithms and standards, and calculates the monetary value using machine learning algorithms and statistical methods. Step 3: The providing unit provides the user with the monetary value calculated by the analysis unit. For example, the providing unit provides feedback to the user using the application interface or notification method. The providing unit provides information to the user by using methods such as a dashboard, notification pop-up, push notification, or email notification.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 AI 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data on user activity history, preferences, and social influence; an analysis unit that analyzes the data collected by the collection unit and calculates a monetary value of each activity; a providing unit that provides the user with the monetary value calculated by the analyzing unit. A system characterized by:

2. The collecting unit Collect data using smartphone sensors and wearable devices 2. The system of claim 1.

3. The analysis unit Analyze the data using specific algorithms and criteria to calculate a monetary value for each activity 2. The system of claim 1.

4. The providing unit Providing feedback to the user through application interfaces and notification methods 2. The system of claim 1.

5. The collecting unit Anonymize data and implement security measures to protect user privacy 2. The system of claim 1.

6. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

7. The collecting unit Analyze users' past activity history and select the most appropriate data collection method 2. The system of claim 1.

8. The collecting unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

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