system

Abstract

The system according to the embodiment aims to analyze a life log and provide suggestions that are useful for anti-aging. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a point provision unit. The collection unit collects life logs. The analysis unit analyzes the life logs collected by the collection unit. The suggestion unit provides suggestions useful for anti-aging based on the analysis results obtained by the analysis unit. The point provision unit provides points when a product is purchased based on the suggestion provided by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately utilized life logs to provide users with suggestions useful for anti-aging, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a life log and provide suggestions that are useful for anti-aging. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a point provision unit. The collection unit collects life logs. The analysis unit analyzes the life logs collected by the collection unit. The suggestion unit provides suggestions useful for anti-aging based on the analysis results obtained by the analysis unit. The point provision unit provides points when a product is purchased based on the suggestion provided by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a life log and provide suggestions that are useful for anti-aging. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An anti-aging support system according to an embodiment of the present invention utilizes built-in functions of a smartphone to collect a user's life log and provide suggestions useful for anti-aging. The system collects the user's life log (e.g., number of steps, sleep time, dietary content, etc.) through sensors and apps on the smartphone. The system then analyzes the collected life log to evaluate the user's health condition and lifestyle. Based on the analysis results, the system provides the user with suggestions useful for anti-aging. For example, an appropriate exercise plan may be suggested for a user who is not getting enough exercise, and a balanced meal plan may be suggested for a user whose diet is unbalanced. Furthermore, if the user purchases a product based on the suggestions, points (e.g., points from an electronic payment system) may be provided. This allows the user to accumulate points while living a healthy lifestyle. The anti-aging support system collects and analyzes the user's life log, provides suggestions useful for anti-aging, and further provides points when purchasing products, thereby supporting the user's healthy lifestyle.

[0029] An anti-aging support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a point provision unit. The collection unit collects a user's life log. The collection unit collects the life log using, for example, smartphone sensors and apps. The smartphone sensors include an acceleration sensor, a GPS sensor, a heart rate sensor, and the like. The apps include a health management app, a fitness app, a food record app, and the like. The analysis unit analyzes the collected life log and evaluates the user's health condition and lifestyle habits. The analysis unit performs the analysis using, for example, statistical analysis of data or a machine learning algorithm. The health condition evaluation includes BMI, blood pressure, heart rate, and the like. The lifestyle evaluation includes exercise frequency, diet, sleep time, and the like. The suggestion unit provides suggestions useful for anti-aging, such as exercise plans and meal plans, based on the analysis results. For example, the suggestion unit suggests walking three times a week to a user who is not getting enough exercise. For example, the suggestion unit suggests a daily meal plan to a user with an unbalanced diet. The point provision unit provides points when the user purchases a product based on the suggestions. The point providing unit provides points for products such as health foods, fitness equipment, supplements, etc. As a result, the anti-aging support system according to the embodiment can support the user's healthy lifestyle by collecting and analyzing the user's life log, providing useful suggestions for anti-aging, and further providing points when purchasing products.

[0030] The collection unit can collect a life log using a smartphone sensor or an app. Smartphone sensors include an acceleration sensor, a GPS sensor, a heart rate sensor, and the like. For example, an acceleration sensor is used to measure a user's steps and exercise amount. A GPS sensor is used to obtain a user's location information. A heart rate sensor is used to measure a user's heart rate. Apps include a health management app, a fitness app, a food recording app, and the like. For example, a health management app is used to monitor a user's health status and collect data. A fitness app is used to record a user's exercise data and provide an exercise plan. A food recording app is used to record a user's diet and evaluate nutritional balance. As a result, a user's life log can be efficiently collected by using a smartphone sensor or app.

[0031] The analysis unit can analyze the collected life log and evaluate the user's health condition and lifestyle habits. The analysis unit performs the analysis using, for example, statistical analysis of data or machine learning algorithms. The health condition evaluation includes BMI, blood pressure, heart rate, etc. For example, BMI is an index calculated based on the user's weight and height and is used to evaluate obesity level. Blood pressure is evaluated based on the user's blood pressure measurement data. Heart rate is evaluated based on data acquired from the user's heart rate sensor. The lifestyle evaluation includes exercise frequency, meal content, sleep duration, etc. For example, exercise frequency is evaluated based on the user's exercise data. Meal content is evaluated based on the user's meal record data. Sleep duration is evaluated based on the user's sleep data. In this way, the user's health condition and lifestyle habits can be evaluated by analyzing the life log.

[0032] The suggestion unit can provide suggestions for anti-aging exercise plans or meal plans based on the analysis results. For example, the suggestion unit suggests walking three times a week to a user who is not getting enough exercise. For example, the suggestion unit suggests to the user that they walk for 30 minutes every Monday, Wednesday, and Friday. The suggestion unit can also instruct the user to keep their heart rate within a certain range while walking. For a user whose diet is unbalanced, the suggestion unit suggests a daily meal plan. For example, the suggestion unit suggests to the user that they eat a balanced meal for breakfast. Specifically, the suggestion unit suggests to the user that they eat a meal including vegetables, fruits, and protein. The suggestion unit also suggests to the user that they eat balanced meals for lunch and dinner. In this way, by providing appropriate suggestions based on the analysis results, it is possible to support the user's anti-aging efforts.

[0033] The point providing unit can provide points when a user purchases a product based on a suggestion. The point providing unit can provide points for products such as health foods, fitness equipment, and supplements. For example, when a user purchases health foods based on a suggestion, the point providing unit can provide points according to the purchase amount. Furthermore, when a user purchases fitness equipment based on a suggestion, the point providing unit can provide points according to the purchase amount. Furthermore, when a user purchases supplements based on a suggestion, the point providing unit can provide points according to the purchase amount. In this way, by providing points when purchasing products, it is possible to increase the user's purchasing motivation.

[0034] The suggestion unit can suggest to a user who is not getting enough exercise that they walk three times a week. For example, the suggestion unit can suggest to the user that they walk for 30 minutes every Monday, Wednesday, and Friday. The suggestion unit can also instruct the user to keep their heart rate within a certain range while walking. For example, the suggestion unit can suggest to the user that they keep their heart rate within a range of 120-140 while walking. The suggestion unit can also suggest to the user that they wear appropriate shoes while walking. For example, the suggestion unit can suggest to the user that they wear shoes with high cushioning. This can promote a healthy lifestyle by suggesting a specific exercise plan to a user who is not getting enough exercise.

[0035] The suggestion unit can suggest a daily meal plan to a user whose diet is unbalanced. For example, the suggestion unit suggests to the user that they eat a balanced meal for breakfast. Specifically, the suggestion unit suggests to the user that they eat a meal including vegetables, fruits, and protein. The suggestion unit also suggests to the user that they eat balanced meals for lunch and dinner. For example, the suggestion unit suggests to the user that they eat a meal including salad, chicken, and whole grains for lunch. Furthermore, the suggestion unit suggests to the user that they eat a meal including fish, vegetables, and beans for dinner. In this way, by suggesting a specific meal plan to a user whose diet is unbalanced, it is possible to promote a balanced diet.

[0036] The point providing unit can provide the user with information on how to use points. The point providing unit provides the user with information such as, for example, what items the points can be exchanged for, the expiration date, and usage restrictions. For example, the point providing unit notifies the user that the points can be exchanged for specific products or services. Specifically, the point providing unit notifies the user that the points can be exchanged for products such as health foods, fitness equipment, and supplements. The point providing unit also notifies the user of the expiration date of the points. For example, the point providing unit notifies the user that the expiration date of the points is one year. Furthermore, the point providing unit notifies the user of the usage restrictions on the points. For example, the point providing unit notifies the user that the upper limit of points that can be used in a single purchase is 1,000 points. In this way, by providing the user with information on how to use points, the user can use their points effectively.

[0037] The collection unit can analyze the user's past life log data and select a collection method. The collection unit, for example, identifies a time period in which the user provided a lot of data in the past and concentrates collection on that time period. For example, the collection unit can identify a time period in which the user provided a lot of data in the past and concentrate collection of life logs on that time period. The collection unit also selects an optimal collection method based on data from apps and sensors used by the user in the past. For example, the collection unit can analyze data from apps and sensors used by the user in the past and select an optimal collection method. Furthermore, the collection unit prioritizes collection of data related to specific activities from the user's past life log data. For example, the collection unit can analyze the user's past life log data and prioritize collection of data related to specific activities. This allows for efficient collection of life logs by selecting an optimal collection method based on past data.

[0038] When collecting a life log, the collection unit can perform filtering based on the user's current activity status and areas of interest. For example, when the user is exercising, the collection unit collects only data related to exercise. For example, when the user is exercising, the collection unit can collect only data related to exercise. Furthermore, when the user is eating, the collection unit collects only data related to meals. For example, when the user is eating, the collection unit can collect only data related to meals. Furthermore, when the user is sleeping, the collection unit collects only data related to sleep. For example, when the user is sleeping, the collection unit can collect only data related to sleep. In this way, by filtering data based on the user's activity status and areas of interest, highly relevant data can be collected.

[0039] When collecting a life log, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at a gym, the collection unit prioritizes collecting data related to exercise. For example, when the user is at a gym, the collection unit can prioritize collecting data related to exercise. Furthermore, when the user is at a restaurant, the collection unit prioritizes collecting data related to meals. For example, when the user is at a restaurant, the collection unit can prioritize collecting data related to meals. Furthermore, when the user is at home, the collection unit prioritizes collecting data related to daily life. For example, when the user is at home, the collection unit can prioritize collecting data related to daily life. In this way, highly relevant data can be efficiently collected by taking geographical location information into consideration.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting the life log. For example, if the user posts about exercise on social media, the collection unit collects data related to exercise. For example, if the user posts about exercise on social media, the collection unit can collect data related to exercise. Furthermore, if the user posts about meals on social media, the collection unit collects data related to meals. For example, if the user posts about meals on social media, the collection unit can collect data related to meals. Furthermore, if the user posts about sleep on social media, the collection unit collects data related to sleep. For example, if the user posts about sleep on social media, the collection unit can collect data related to sleep. In this way, data related to the user's interests can be collected by analyzing social media activities.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the life log. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit can perform analysis on data of high importance using detailed statistical analysis or machine learning algorithms. Furthermore, the analysis unit performs a simplified analysis on data of low importance. For example, the analysis unit can perform simplified aggregation or basic statistical analysis on data of low importance. Furthermore, the analysis unit performs an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit can perform analysis with a moderate level of detail on data of medium importance using statistical analysis or machine learning algorithms. In this way, by adjusting the level of detail of the analysis based on the importance of the life log, analysis can be performed efficiently.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the life log. For example, the analysis unit applies an exercise analysis algorithm to exercise data. For example, the analysis unit can apply an exercise analysis algorithm to the exercise data to evaluate the type, intensity, and frequency of exercise. The analysis unit also applies a diet analysis algorithm to diet data. For example, the analysis unit can apply a diet analysis algorithm to the diet data to evaluate nutritional balance and calorie intake. The analysis unit also applies a sleep analysis algorithm to sleep data. For example, the analysis unit can apply a sleep analysis algorithm to the sleep data to evaluate the quality and duration of sleep. In this way, by applying an analysis algorithm according to the category, more accurate analysis results can be provided.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the life log was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit can prioritize analysis of the most recent life log data to evaluate the user's current health condition and lifestyle habits. The analysis unit also analyzes the most recent data while referring to past data. For example, the analysis unit can analyze the most recent data while referring to past life log data to evaluate changes in the user's health condition and lifestyle habits. The analysis unit also prioritizes analysis of data collected during a specific period. For example, the analysis unit can prioritize analysis of life log data collected during a specific period to evaluate the user's health condition and lifestyle habits during that period. In this way, by determining the priority of analysis based on the time when the life log was collected, the most recent data can be prioritized for analysis.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the life log. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant life log data to evaluate the user's health condition and lifestyle habits. The analysis unit also postpones analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant life log data and prioritize analysis of important data. Furthermore, the analysis unit moderately analyzes data with a medium degree of relevance. For example, the analysis unit moderately analyzes life log data with a medium degree of relevance to evaluate the user's health condition and lifestyle habits. This allows for efficient analysis by adjusting the order of analysis based on relevance.

[0045] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the user's health condition. For example, the suggestion unit provides detailed proposals to a user in good health. For example, the suggestion unit can provide detailed exercise plans and meal plans to a user in good health. Furthermore, the suggestion unit provides simplified proposals to a user in poor health. For example, the suggestion unit can provide simplified exercise plans and meal plans to a user in poor health. Furthermore, the suggestion unit provides proposals with a moderate level of detail to a user in moderate health. For example, the suggestion unit can provide exercise plans and meal plans with a moderate level of detail to a user in moderate health. In this way, by adjusting the level of detail of the proposal based on the user's health condition, appropriate proposals can be provided.

[0046] When making a proposal, the proposal unit can apply different proposal algorithms depending on the user's lifestyle habits. For example, the proposal unit applies an algorithm that proposes an exercise plan to a user who has an exercise habit. For example, the proposal unit can apply an algorithm for proposing an exercise plan to a user who has an exercise habit. Furthermore, the proposal unit applies an algorithm that proposes a meal plan to a user who has eating habits. For example, the proposal unit can apply an algorithm for proposing a meal plan to a user who has eating habits. Furthermore, the proposal unit applies an algorithm that proposes a sleep plan to a user who has sleeping habits. For example, the proposal unit can apply an algorithm for proposing a sleep plan to a user who has sleeping habits. In this way, by applying a proposal algorithm according to the user's lifestyle habits, more effective proposals can be provided.

[0047] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the collection time of the user's life log. The suggestion unit makes the suggestion based on, for example, the latest life log. For example, the suggestion unit can make suggestions regarding the user's current health condition and lifestyle habits based on the latest life log data. Furthermore, the suggestion unit makes the suggestion based on the latest life log while referring to past life logs. For example, the suggestion unit can make suggestions based on the latest data while referring to past life log data, thereby responding to changes in the user's health condition and lifestyle habits. Furthermore, the suggestion unit makes the suggestion based on life logs collected during a specific period. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits during that period based on the life log data collected during that period. In this way, by determining the priority of the suggestion based on the collection time, it is possible to provide suggestions based on the latest data.

[0048] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the user's life log. The suggestion unit makes suggestions based on, for example, highly relevant life logs. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits based on highly relevant life log data. The suggestion unit also makes suggestions based on low-relevance life logs. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits based on low-relevance life log data. The suggestion unit also makes suggestions based on medium-relevance life logs. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits based on medium-relevance life log data. This allows suggestions to be made efficiently by adjusting the order of suggestions based on relevance.

[0049] When awarding points, the point providing unit can select a point awarding method by analyzing the user's past purchase history. The point providing unit selects the optimal point awarding method, for example, based on products for which the user has earned a large number of points in the past. For example, the point providing unit can select the optimal point awarding method based on products for which the user has earned a large number of points in the past. The point providing unit also awards points for specific products based on the user's past purchase history. For example, the point providing unit can analyze the user's past purchase history and award points for specific products. Furthermore, the point providing unit analyzes the user's past purchase history and selects the most effective point awarding method. For example, the point providing unit can analyze the user's past purchase history and select the most effective point awarding method. In this way, by selecting the optimal point awarding method based on the user's past purchase history, it is possible to increase the user's purchasing motivation.

[0050] When providing points, the point providing unit can customize the means of providing points based on the user's current purchasing behavior. For example, when the user makes an online purchase, the point providing unit provides electronic points. For example, the point providing unit can provide electronic points when the user makes an online purchase. Furthermore, when the user makes a purchase at a store, the point providing unit provides a paper point coupon. For example, the point providing unit can provide a paper point coupon when the user makes a purchase at a store. Furthermore, when the user makes a purchase through a specific app, the point providing unit provides points that can be used within the app. For example, the point providing unit can provide points that can be used within the app when the user makes a purchase through a specific app. In this way, by customizing the means of providing points based on the user's current purchasing behavior, user satisfaction can be increased.

[0051] When providing points, the point providing unit can select a point providing method based on the geographical location information of the user. For example, when the user is in a specific area, the point providing unit provides points that can be used in that area. For example, when the user is in a specific area, the point providing unit can provide points that can be used in that area. Furthermore, when the user is traveling, the point providing unit can provide points that can be used at the travel destination. For example, when the user is traveling, the point providing unit can provide points that can be used at the travel destination. Furthermore, when the user is at home, the point providing unit provides points that can be used at home. For example, when the user is at home, the point providing unit can provide points that can be used at home. In this way, by taking the geographical location information into consideration, it is possible to select the optimal point providing method for the user.

[0052] When providing points, the point providing unit can analyze the user's social media activity and suggest a means for providing points. For example, if a user shares a specific product on social media, the point providing unit can provide points for the product. For example, if a user shares a specific product on social media, the point providing unit can provide points for the product. Furthermore, if a user participates in a specific campaign on social media, the point providing unit can provide points for the campaign. For example, if a user participates in a specific campaign on social media, the point providing unit can provide points for the campaign. Furthermore, if a user follows a specific brand on social media, the point providing unit can provide points for the brand. For example, if a user follows a specific brand on social media, the point providing unit can provide points for the brand. In this way, by analyzing social media activity, it is possible to suggest the optimal means for providing points for the user.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] When collecting a user's life log, the collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing. For example, the collection unit can identify time periods in which the user provided a lot of data in the past and concentrate collection on those time periods. The collection unit can also select the optimal collection method based on data from apps and sensors used by the user in the past. Furthermore, the collection unit can prioritize the collection of data related to specific activities from the user's past life log data. This allows for efficient collection of life logs by selecting the optimal collection method based on past data.

[0055] When collecting a life log, the collection unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is exercising, only data related to exercise can be collected. Also, if the user is eating, only data related to eating can be collected. Furthermore, if the user is sleeping, only data related to sleep can be collected. In this way, by filtering data based on the user's activity status and areas of interest, highly relevant data can be collected.

[0056] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the life log. For example, a detailed analysis can be performed on data of high importance. A simplified analysis can be performed on data of low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the life log.

[0057] The suggestion unit can adjust the level of detail of the suggestion based on the user's health condition when making the suggestion. For example, detailed suggestions can be provided to a user in good health. A simplified suggestion can be provided to a user in poor health. Furthermore, a suggestion with an appropriate level of detail can be provided to a user in moderate health. In this way, by adjusting the level of detail of the suggestion based on the user's health condition, appropriate suggestions can be provided.

[0058] When awarding points, the point awarding unit can select a point awarding method by analyzing the user's past purchase history. For example, the optimal point awarding method can be selected based on the products for which the user has earned the most points in the past. Points can also be awarded for specific products based on the user's past purchase history. Furthermore, the most effective point awarding method can be selected by analyzing the user's past purchase history. This can increase the user's purchasing motivation by selecting the optimal point awarding method based on the user's past purchase history.

[0059] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the user's life log was collected. For example, the suggestion unit can make a proposal based on the latest life log. Also, the suggestion unit can make a proposal based on the latest life log while referring to past life logs. Furthermore, the suggestion unit can make a proposal based on life logs collected during a specific period. In this way, by determining the priority of the proposal based on the time when the life log was collected, it is possible to provide a proposal based on the latest data.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects the user's life log. The collection unit collects the life log using, for example, smartphone sensors and apps. Smartphone sensors include an acceleration sensor, a GPS sensor, a heart rate sensor, etc. Apps include health management apps, fitness apps, food recording apps, etc. Step 2: The analysis unit analyzes the collected life log and evaluates the user's health condition and lifestyle habits. The analysis unit performs the analysis using, for example, statistical analysis of the data or machine learning algorithms. The health condition evaluation includes BMI, blood pressure, heart rate, etc. The lifestyle evaluation includes exercise frequency, diet, sleep duration, etc. Step 3: The suggestion unit provides suggestions useful for anti-aging, such as exercise plans and meal plans, based on the analysis results. For example, the suggestion unit may suggest walking three times a week to a user who is not getting enough exercise. For a user whose diet is unbalanced, the suggestion unit may suggest a daily meal plan. Step 4: The point providing unit provides points when the user purchases a product based on the recommendation. The point providing unit provides points for products such as health foods, fitness equipment, and supplements, for example.

[0062] (Example 2) An anti-aging support system according to an embodiment of the present invention utilizes built-in functions of a smartphone to collect a user's life log and provide suggestions useful for anti-aging. The system collects the user's life log (e.g., number of steps, sleep time, dietary content, etc.) through sensors and apps on the smartphone. The system then analyzes the collected life log to evaluate the user's health condition and lifestyle. Based on the analysis results, the system provides the user with suggestions useful for anti-aging. For example, an appropriate exercise plan may be suggested for a user who is not getting enough exercise, and a balanced meal plan may be suggested for a user whose diet is unbalanced. Furthermore, if the user purchases a product based on the suggestions, points (e.g., points from an electronic payment system) may be provided. This allows the user to accumulate points while living a healthy lifestyle. The anti-aging support system collects and analyzes the user's life log, provides suggestions useful for anti-aging, and further provides points when purchasing products, thereby supporting the user's healthy lifestyle.

[0063] An anti-aging support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a point provision unit. The collection unit collects a user's life log. The collection unit collects the life log using, for example, smartphone sensors and apps. The smartphone sensors include an acceleration sensor, a GPS sensor, a heart rate sensor, and the like. The apps include a health management app, a fitness app, a food record app, and the like. The analysis unit analyzes the collected life log and evaluates the user's health condition and lifestyle habits. The analysis unit performs the analysis using, for example, statistical analysis of data or a machine learning algorithm. The health condition evaluation includes BMI, blood pressure, heart rate, and the like. The lifestyle evaluation includes exercise frequency, diet, sleep time, and the like. The suggestion unit provides suggestions useful for anti-aging, such as exercise plans and meal plans, based on the analysis results. For example, the suggestion unit suggests walking three times a week to a user who is not getting enough exercise. For example, the suggestion unit suggests a daily meal plan to a user with an unbalanced diet. The point provision unit provides points when the user purchases a product based on the suggestions. The point providing unit provides points for products such as health foods, fitness equipment, supplements, etc. As a result, the anti-aging support system according to the embodiment can support the user's healthy lifestyle by collecting and analyzing the user's life log, providing useful suggestions for anti-aging, and further providing points when purchasing products.

[0064] The collection unit can collect a life log using a smartphone sensor or an app. Smartphone sensors include an acceleration sensor, a GPS sensor, a heart rate sensor, and the like. For example, an acceleration sensor is used to measure a user's steps and exercise amount. A GPS sensor is used to obtain a user's location information. A heart rate sensor is used to measure a user's heart rate. Apps include a health management app, a fitness app, a food recording app, and the like. For example, a health management app is used to monitor a user's health status and collect data. A fitness app is used to record a user's exercise data and provide an exercise plan. A food recording app is used to record a user's diet and evaluate nutritional balance. As a result, a user's life log can be efficiently collected by using a smartphone sensor or app.

[0065] The analysis unit can analyze the collected life log and evaluate the user's health condition and lifestyle habits. The analysis unit performs the analysis using, for example, statistical analysis of data or machine learning algorithms. The health condition evaluation includes BMI, blood pressure, heart rate, etc. For example, BMI is an index calculated based on the user's weight and height and is used to evaluate obesity level. Blood pressure is evaluated based on the user's blood pressure measurement data. Heart rate is evaluated based on data acquired from the user's heart rate sensor. The lifestyle evaluation includes exercise frequency, meal content, sleep duration, etc. For example, exercise frequency is evaluated based on the user's exercise data. Meal content is evaluated based on the user's meal record data. Sleep duration is evaluated based on the user's sleep data. In this way, the user's health condition and lifestyle habits can be evaluated by analyzing the life log.

[0066] The suggestion unit can provide suggestions for anti-aging exercise plans or meal plans based on the analysis results. For example, the suggestion unit suggests walking three times a week to a user who is not getting enough exercise. For example, the suggestion unit suggests to the user that they walk for 30 minutes every Monday, Wednesday, and Friday. The suggestion unit can also instruct the user to keep their heart rate within a certain range while walking. For a user whose diet is unbalanced, the suggestion unit suggests a daily meal plan. For example, the suggestion unit suggests to the user that they eat a balanced meal for breakfast. Specifically, the suggestion unit suggests to the user that they eat a meal including vegetables, fruits, and protein. The suggestion unit also suggests to the user that they eat balanced meals for lunch and dinner. In this way, by providing appropriate suggestions based on the analysis results, it is possible to support the user's anti-aging efforts.

[0067] The point providing unit can provide points when a user purchases a product based on a suggestion. The point providing unit can provide points for products such as health foods, fitness equipment, and supplements. For example, when a user purchases health foods based on a suggestion, the point providing unit can provide points according to the purchase amount. Furthermore, when a user purchases fitness equipment based on a suggestion, the point providing unit can provide points according to the purchase amount. Furthermore, when a user purchases supplements based on a suggestion, the point providing unit can provide points according to the purchase amount. In this way, by providing points when purchasing products, it is possible to increase the user's purchasing motivation.

[0068] The suggestion unit can suggest to a user who is not getting enough exercise that they walk three times a week. For example, the suggestion unit can suggest to the user that they walk for 30 minutes every Monday, Wednesday, and Friday. The suggestion unit can also instruct the user to keep their heart rate within a certain range while walking. For example, the suggestion unit can suggest to the user that they keep their heart rate within a range of 120-140 while walking. The suggestion unit can also suggest to the user that they wear appropriate shoes while walking. For example, the suggestion unit can suggest to the user that they wear shoes with high cushioning. This can promote a healthy lifestyle by suggesting a specific exercise plan to a user who is not getting enough exercise.

[0069] The suggestion unit can suggest a daily meal plan to a user whose diet is unbalanced. For example, the suggestion unit suggests to the user that they eat a balanced meal for breakfast. Specifically, the suggestion unit suggests to the user that they eat a meal including vegetables, fruits, and protein. The suggestion unit also suggests to the user that they eat balanced meals for lunch and dinner. For example, the suggestion unit suggests to the user that they eat a meal including salad, chicken, and whole grains for lunch. Furthermore, the suggestion unit suggests to the user that they eat a meal including fish, vegetables, and beans for dinner. In this way, by suggesting a specific meal plan to a user whose diet is unbalanced, it is possible to promote a balanced diet.

[0070] The point providing unit can provide the user with information on how to use points. The point providing unit provides the user with information such as, for example, what items the points can be exchanged for, the expiration date, and usage restrictions. For example, the point providing unit notifies the user that the points can be exchanged for specific products or services. Specifically, the point providing unit notifies the user that the points can be exchanged for products such as health foods, fitness equipment, and supplements. The point providing unit also notifies the user of the expiration date of the points. For example, the point providing unit notifies the user that the expiration date of the points is one year. Furthermore, the point providing unit notifies the user of the usage restrictions on the points. For example, the point providing unit notifies the user that the upper limit of points that can be used in a single purchase is 1,000 points. In this way, by providing the user with information on how to use points, the user can use their points effectively.

[0071] The collection unit can estimate the user's emotions and adjust the timing of collecting the life log based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can reduce the collection timing to reduce the burden on the user. For example, when the user is feeling stressed, the collection unit can reduce the frequency of collecting the life log to once a day. Furthermore, when the user is relaxed, the collection unit can increase the collection timing to collect a more detailed life log. For example, when the user is relaxed, the collection unit can increase the frequency of collecting the life log to three times a day. Furthermore, when the user is exercising, the collection unit collects the life log after the user finishes exercising to obtain accurate data. For example, by collecting the life log immediately after the user finishes exercising, the collection unit can accurately grasp the effects of the exercise. In this way, by adjusting the collection timing according to the user's emotions, the burden on the user can be reduced and accurate data can be collected.

[0072] The collection unit can analyze the user's past life log data and select a collection method. The collection unit, for example, identifies a time period in which the user provided a lot of data in the past and concentrates collection on that time period. For example, the collection unit can identify a time period in which the user provided a lot of data in the past and concentrate collection of life logs on that time period. The collection unit also selects an optimal collection method based on data from apps and sensors used by the user in the past. For example, the collection unit can analyze data from apps and sensors used by the user in the past and select an optimal collection method. Furthermore, the collection unit prioritizes collection of data related to specific activities from the user's past life log data. For example, the collection unit can analyze the user's past life log data and prioritize collection of data related to specific activities. This allows for efficient collection of life logs by selecting an optimal collection method based on past data.

[0073] When collecting a life log, the collection unit can perform filtering based on the user's current activity status and areas of interest. For example, when the user is exercising, the collection unit collects only data related to exercise. For example, when the user is exercising, the collection unit can collect only data related to exercise. Furthermore, when the user is eating, the collection unit collects only data related to meals. For example, when the user is eating, the collection unit can collect only data related to meals. Furthermore, when the user is sleeping, the collection unit collects only data related to sleep. For example, when the user is sleeping, the collection unit can collect only data related to sleep. In this way, by filtering data based on the user's activity status and areas of interest, highly relevant data can be collected.

[0074] The collection unit can estimate the user's emotions and determine the priority of life logs to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to stress. For example, when the user is feeling stressed, the collection unit can prioritize collecting data related to stress. Furthermore, when the user is relaxed, the collection unit prioritizes collecting data related to relaxation. For example, when the user is relaxed, the collection unit can prioritize collecting data related to relaxation. Furthermore, when the user is exercising, the collection unit prioritizes collecting data related to exercise. For example, when the user is exercising, the collection unit can prioritize collecting data related to exercise. In this way, by determining the priority of data according to the user's emotions, important data can be collected preferentially.

[0075] When collecting a life log, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is at a gym, the collection unit prioritizes collecting data related to exercise. For example, when the user is at a gym, the collection unit can prioritize collecting data related to exercise. Furthermore, when the user is at a restaurant, the collection unit prioritizes collecting data related to meals. For example, when the user is at a restaurant, the collection unit can prioritize collecting data related to meals. Furthermore, when the user is at home, the collection unit prioritizes collecting data related to daily life. For example, when the user is at home, the collection unit can prioritize collecting data related to daily life. In this way, highly relevant data can be efficiently collected by taking geographical location information into consideration.

[0076] The collection unit can analyze the user's social media activities and collect related data when collecting the life log. For example, if the user posts about exercise on social media, the collection unit collects data related to exercise. For example, if the user posts about exercise on social media, the collection unit can collect data related to exercise. Furthermore, if the user posts about meals on social media, the collection unit collects data related to meals. For example, if the user posts about meals on social media, the collection unit can collect data related to meals. Furthermore, if the user posts about sleep on social media, the collection unit collects data related to sleep. For example, if the user posts about sleep on social media, the collection unit can collect data related to sleep. In this way, data related to the user's interests can be collected by analyzing social media activities.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit provides simple, visually easy-to-understand analysis results. For example, when the user is feeling stressed, the analysis unit can provide the analysis results using simple, visually easy-to-understand graphs or charts. Furthermore, when the user is relaxed, the analysis unit provides detailed analysis results. For example, when the user is relaxed, the analysis unit can provide the analysis results using detailed text reports or interactive dashboards. Furthermore, when the user is excited, the analysis unit provides visually stimulating analysis results. For example, when the user is excited, the analysis unit can provide the analysis results using visually stimulating animations or visual effects. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0078] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the life log. For example, the analysis unit performs a detailed analysis on data of high importance. For example, the analysis unit can perform analysis on data of high importance using detailed statistical analysis or machine learning algorithms. Furthermore, the analysis unit performs a simplified analysis on data of low importance. For example, the analysis unit can perform simplified aggregation or basic statistical analysis on data of low importance. Furthermore, the analysis unit performs an analysis with a moderate level of detail on data of medium importance. For example, the analysis unit can perform analysis with a moderate level of detail on data of medium importance using statistical analysis or machine learning algorithms. In this way, by adjusting the level of detail of the analysis based on the importance of the life log, analysis can be performed efficiently.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the life log. For example, the analysis unit applies an exercise analysis algorithm to exercise data. For example, the analysis unit can apply an exercise analysis algorithm to the exercise data to evaluate the type, intensity, and frequency of exercise. The analysis unit also applies a diet analysis algorithm to diet data. For example, the analysis unit can apply a diet analysis algorithm to the diet data to evaluate nutritional balance and calorie intake. The analysis unit also applies a sleep analysis algorithm to sleep data. For example, the analysis unit can apply a sleep analysis algorithm to the sleep data to evaluate the quality and duration of sleep. In this way, by applying an analysis algorithm according to the category, more accurate analysis results can be provided.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, when the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, when the user is in a hurry, the analysis unit can provide a short and to-the-point summary report. Furthermore, when the user is relaxed, the analysis unit provides a detailed analysis result. For example, when the user is relaxed, the analysis unit can provide a detailed analysis report or an interactive dashboard. Furthermore, when the user is excited, the analysis unit provides a visually stimulating analysis result. For example, when the user is excited, the analysis unit can provide an analysis result using visually stimulating animations or visual effects. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide the optimal analysis result for the user.

[0081] During analysis, the analysis unit can determine the priority of analysis based on the time when the life log was collected. The analysis unit, for example, prioritizes analysis of the most recent data. For example, the analysis unit can prioritize analysis of the most recent life log data to evaluate the user's current health condition and lifestyle habits. The analysis unit also analyzes the most recent data while referring to past data. For example, the analysis unit can analyze the most recent data while referring to past life log data to evaluate changes in the user's health condition and lifestyle habits. The analysis unit also prioritizes analysis of data collected during a specific period. For example, the analysis unit can prioritize analysis of life log data collected during a specific period to evaluate the user's health condition and lifestyle habits during that period. In this way, by determining the priority of analysis based on the time when the life log was collected, the most recent data can be prioritized for analysis.

[0082] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the life log. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit can prioritize analysis of highly relevant life log data to evaluate the user's health condition and lifestyle habits. The analysis unit also postpones analysis of less relevant data. For example, the analysis unit can postpone analysis of less relevant life log data and prioritize analysis of important data. Furthermore, the analysis unit moderately analyzes data with a medium degree of relevance. For example, the analysis unit moderately analyzes life log data with a medium degree of relevance to evaluate the user's health condition and lifestyle habits. This allows for efficient analysis by adjusting the order of analysis based on relevance.

[0083] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are presented based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit provides simple and visually easy-to-understand suggestions. For example, when the user is feeling stressed, the suggestion unit can provide suggestions using simple and visually easy-to-understand graphs or charts. Furthermore, when the user is relaxed, the suggestion unit provides detailed suggestions. For example, when the user is relaxed, the suggestion unit can provide suggestions using detailed text reports or interactive guides. Furthermore, when the user is excited, the suggestion unit provides visually stimulating suggestions. For example, when the user is excited, the suggestion unit can provide suggestions using visually stimulating animations or visual effects. In this way, by adjusting the way in which suggestions are presented according to the user's emotions, suggestions that are easy for the user to understand can be provided.

[0084] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the user's health condition. For example, the suggestion unit provides detailed proposals to a user in good health. For example, the suggestion unit can provide detailed exercise plans and meal plans to a user in good health. Furthermore, the suggestion unit provides simplified proposals to a user in poor health. For example, the suggestion unit can provide simplified exercise plans and meal plans to a user in poor health. Furthermore, the suggestion unit provides proposals with a moderate level of detail to a user in moderate health. For example, the suggestion unit can provide exercise plans and meal plans with a moderate level of detail to a user in moderate health. In this way, by adjusting the level of detail of the proposal based on the user's health condition, appropriate proposals can be provided.

[0085] When making a proposal, the proposal unit can apply different proposal algorithms depending on the user's lifestyle habits. For example, the proposal unit applies an algorithm that proposes an exercise plan to a user who has an exercise habit. For example, the proposal unit can apply an algorithm for proposing an exercise plan to a user who has an exercise habit. Furthermore, the proposal unit applies an algorithm that proposes a meal plan to a user who has eating habits. For example, the proposal unit can apply an algorithm for proposing a meal plan to a user who has eating habits. Furthermore, the proposal unit applies an algorithm that proposes a sleep plan to a user who has sleeping habits. For example, the proposal unit can apply an algorithm for proposing a sleep plan to a user who has sleeping habits. In this way, by applying a proposal algorithm according to the user's lifestyle habits, more effective proposals can be provided.

[0086] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, when the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. For example, when the user is in a hurry, the suggestion unit can provide a short and to-the-point summary report. Furthermore, when the user is relaxed, the suggestion unit can provide detailed suggestions. For example, when the user is relaxed, the suggestion unit can provide a detailed suggestion report or an interactive guide. Furthermore, when the user is excited, the suggestion unit can provide visually stimulating suggestions. For example, when the user is excited, the suggestion unit can provide suggestions using visually stimulating animations or visual effects. In this way, by adjusting the length of the suggestions according to the user's emotions, it is possible to provide optimal suggestions for the user.

[0087] When making a suggestion, the suggestion unit can determine the priority of the suggestion based on the collection time of the user's life log. The suggestion unit makes the suggestion based on, for example, the latest life log. For example, the suggestion unit can make suggestions regarding the user's current health condition and lifestyle habits based on the latest life log data. Furthermore, the suggestion unit makes the suggestion based on the latest life log while referring to past life logs. For example, the suggestion unit can make suggestions based on the latest data while referring to past life log data, thereby responding to changes in the user's health condition and lifestyle habits. Furthermore, the suggestion unit makes the suggestion based on life logs collected during a specific period. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits during that period based on the life log data collected during that period. In this way, by determining the priority of the suggestion based on the collection time, it is possible to provide suggestions based on the latest data.

[0088] When making a suggestion, the suggestion unit can adjust the order of suggestions based on the relevance of the user's life log. The suggestion unit makes suggestions based on, for example, highly relevant life logs. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits based on highly relevant life log data. The suggestion unit also makes suggestions based on low-relevance life logs. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits based on low-relevance life log data. The suggestion unit also makes suggestions based on medium-relevance life logs. For example, the suggestion unit can make suggestions regarding the user's health condition and lifestyle habits based on medium-relevance life log data. This allows suggestions to be made efficiently by adjusting the order of suggestions based on relevance.

[0089] The point providing unit can estimate the user's emotions and adjust the timing of providing points based on the estimated user's emotions. For example, the point providing unit delays the timing of providing points when the user is feeling stressed. For example, the point providing unit can delay the timing of providing points when the user is feeling stressed. Furthermore, the point providing unit advances the timing of providing points when the user is relaxed. For example, the point providing unit can advance the timing of providing points when the user is relaxed. Furthermore, the point providing unit adjusts the timing of providing points when the user is excited. For example, the point providing unit can adjust the timing of providing points when the user is excited. In this way, by adjusting the timing of providing points according to the user's emotions, user satisfaction can be increased.

[0090] When awarding points, the point providing unit can select a point awarding method by analyzing the user's past purchase history. The point providing unit selects the optimal point awarding method, for example, based on products for which the user has earned a large number of points in the past. For example, the point providing unit can select the optimal point awarding method based on products for which the user has earned a large number of points in the past. The point providing unit also awards points for specific products based on the user's past purchase history. For example, the point providing unit can analyze the user's past purchase history and award points for specific products. Furthermore, the point providing unit analyzes the user's past purchase history and selects the most effective point awarding method. For example, the point providing unit can analyze the user's past purchase history and select the most effective point awarding method. In this way, by selecting the optimal point awarding method based on the user's past purchase history, it is possible to increase the user's purchasing motivation.

[0091] When providing points, the point providing unit can customize the means of providing points based on the user's current purchasing behavior. For example, when the user makes an online purchase, the point providing unit provides electronic points. For example, the point providing unit can provide electronic points when the user makes an online purchase. Furthermore, when the user makes a purchase at a store, the point providing unit provides a paper point coupon. For example, the point providing unit can provide a paper point coupon when the user makes a purchase at a store. Furthermore, when the user makes a purchase through a specific app, the point providing unit provides points that can be used within the app. For example, the point providing unit can provide points that can be used within the app when the user makes a purchase through a specific app. In this way, by customizing the means of providing points based on the user's current purchasing behavior, user satisfaction can be increased.

[0092] The point providing unit can estimate the user's emotions and determine the priority of point provision based on the estimated user's emotions. For example, the point providing unit can lower the priority of point provision when the user is feeling stressed. For example, the point providing unit can lower the priority of point provision when the user is feeling stressed. Furthermore, the point providing unit can increase the priority of point provision when the user is relaxed. For example, the point providing unit can increase the priority of point provision when the user is relaxed. Furthermore, the point providing unit can adjust the priority of point provision when the user is excited. For example, the point providing unit can adjust the priority of point provision when the user is excited. In this way, by determining the priority of point provision according to the user's emotions, user satisfaction can be increased.

[0093] When providing points, the point providing unit can select a point providing method based on the geographical location information of the user. For example, when the user is in a specific area, the point providing unit provides points that can be used in that area. For example, when the user is in a specific area, the point providing unit can provide points that can be used in that area. Furthermore, when the user is traveling, the point providing unit can provide points that can be used at the travel destination. For example, when the user is traveling, the point providing unit can provide points that can be used at the travel destination. Furthermore, when the user is at home, the point providing unit provides points that can be used at home. For example, when the user is at home, the point providing unit can provide points that can be used at home. In this way, by taking the geographical location information into consideration, it is possible to select the optimal point providing method for the user.

[0094] When providing points, the point providing unit can analyze the user's social media activity and suggest a means for providing points. For example, if a user shares a specific product on social media, the point providing unit can provide points for the product. For example, if a user shares a specific product on social media, the point providing unit can provide points for the product. Furthermore, if a user participates in a specific campaign on social media, the point providing unit can provide points for the campaign. For example, if a user participates in a specific campaign on social media, the point providing unit can provide points for the campaign. Furthermore, if a user follows a specific brand on social media, the point providing unit can provide points for the brand. For example, if a user follows a specific brand on social media, the point providing unit can provide points for the brand. In this way, by analyzing social media activity, it is possible to suggest the optimal means for providing points for the user. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and point providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a life log using a sensor or an app of the smart device 14. The analysis unit analyzes the collected life log by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition and lifestyle habits. The suggestion unit provides suggestions useful for anti-aging based on the analysis results by, for example, the specific processing unit 290 of the data processing device 12. The point providing unit provides points when a product is purchased by, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and point 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 collects a life log using a sensor or an app of the smart glasses 214. For example, the analysis unit analyzes the collected life log by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition and lifestyle habits. For example, the suggestion unit provides suggestions useful for anti-aging based on the analysis results by, for example, the specific processing unit 290 of the data processing device 12. For example, the point provision unit provides points when purchasing a product by, for example, the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and point 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 collects a life log using a sensor or an app of the headset type terminal 314. For example, the analysis unit analyzes the collected life log by the specific processing unit 290 of the data processing device 12 and evaluates the user's health condition and lifestyle habits. For example, the suggestion unit provides suggestions useful for anti-aging based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the point provision unit provides points when a product is purchased by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, and point 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 collects a life log using sensors and apps of the robot 414. The analysis unit analyzes the life log collected, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the user's health condition and lifestyle habits. For example, the suggestion unit provides suggestions useful for anti-aging based on the analysis results by the specific processing unit 290 of the data processing device 12. For example, the point provision unit provides points when a product is purchased by the control unit 46A of the robot 414.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] When collecting a user's life log, the collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing. For example, the collection unit can identify time periods in which the user provided a lot of data in the past and concentrate collection on those time periods. The collection unit can also select the optimal collection method based on data from apps and sensors used by the user in the past. Furthermore, the collection unit can prioritize the collection of data related to specific activities from the user's past life log data. This allows for efficient collection of life logs by selecting the optimal collection method based on past data.

[0097] When analyzing the collected life log, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is feeling stressed, a simple and visually easy-to-understand analysis result can be provided. If the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. In this way, by adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0098] The suggestion unit can estimate the user's emotion and adjust the way suggestions are expressed based on the estimated user's emotion. For example, if the user is feeling stressed, a simple and visually easy-to-understand suggestion can be provided. If the user is relaxed, a detailed suggestion can be provided. If the user is excited, a visually stimulating suggestion can be provided. In this way, by adjusting the way suggestions are expressed according to the user's emotion, suggestions that are easy for the user to understand can be provided.

[0099] The point providing unit can estimate the user's emotions and adjust the timing of providing points based on the estimated user's emotions. For example, if the user is feeling stressed, the timing of providing points can be delayed. Also, if the user is relaxed, the timing of providing points can be advanced. Furthermore, if the user is excited, the timing of providing points can be adjusted. In this way, by adjusting the timing of providing points according to the user's emotions, it is possible to increase user satisfaction.

[0100] When collecting a life log, the collection unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is exercising, only data related to exercise can be collected. Also, if the user is eating, only data related to eating can be collected. Furthermore, if the user is sleeping, only data related to sleep can be collected. In this way, by filtering data based on the user's activity status and areas of interest, highly relevant data can be collected.

[0101] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the life log. For example, a detailed analysis can be performed on data of high importance. A simplified analysis can be performed on data of low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the life log.

[0102] The suggestion unit can adjust the level of detail of the suggestion based on the user's health condition when making the suggestion. For example, detailed suggestions can be provided to a user in good health. A simplified suggestion can be provided to a user in poor health. Furthermore, a suggestion with an appropriate level of detail can be provided to a user in moderate health. In this way, by adjusting the level of detail of the suggestion based on the user's health condition, appropriate suggestions can be provided.

[0103] When awarding points, the point awarding unit can select a point awarding method by analyzing the user's past purchase history. For example, the optimal point awarding method can be selected based on the products for which the user has earned the most points in the past. Points can also be awarded for specific products based on the user's past purchase history. Furthermore, the most effective point awarding method can be selected by analyzing the user's past purchase history. This can increase the user's purchasing motivation by selecting the optimal point awarding method based on the user's past purchase history.

[0104] The collection unit can estimate the user's emotions and determine the priority of life logs to be collected based on the estimated user emotions. For example, if the user is feeling stressed, data related to stress can be collected with priority. Also, if the user is relaxed, data related to relaxation can be collected with priority. Furthermore, if the user is exercising, data related to exercise can be collected with priority. In this way, by determining the priority of data according to the user's emotions, important data can be collected with priority.

[0105] When making a proposal, the suggestion unit can determine the priority of the proposal based on the time when the user's life log was collected. For example, the suggestion unit can make a proposal based on the latest life log. Also, the suggestion unit can make a proposal based on the latest life log while referring to past life logs. Furthermore, the suggestion unit can make a proposal based on life logs collected during a specific period. In this way, by determining the priority of the proposal based on the time when the life log was collected, it is possible to provide a proposal based on the latest data.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The collection unit collects the user's life log. The collection unit collects the life log using, for example, smartphone sensors and apps. Smartphone sensors include an acceleration sensor, a GPS sensor, a heart rate sensor, etc. Apps include health management apps, fitness apps, food recording apps, etc. Step 2: The analysis unit analyzes the collected life log and evaluates the user's health condition and lifestyle habits. The analysis unit performs the analysis using, for example, statistical analysis of the data or machine learning algorithms. The health condition evaluation includes BMI, blood pressure, heart rate, etc. The lifestyle evaluation includes exercise frequency, diet, sleep duration, etc. Step 3: The suggestion unit provides suggestions useful for anti-aging, such as exercise plans and meal plans, based on the analysis results. For example, the suggestion unit may suggest walking three times a week to a user who is not getting enough exercise. For a user whose diet is unbalanced, the suggestion unit may suggest a daily meal plan. Step 4: The point providing unit provides points when the user purchases a product based on the recommendation. The point providing unit provides points for products such as health foods, fitness equipment, and supplements, for example.

[0108] 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.

[0109] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 life logs; an analysis unit that analyzes the life log collected by the collection unit; a suggestion unit that provides suggestions useful for anti-aging based on the analysis results obtained by the analysis unit; a point providing unit that provides points when a product is purchased based on the proposal provided by the proposal unit; A system characterized by:

2. The collecting unit Collecting life logs using smartphone sensors or apps 2. The system of claim 1.

3. The analysis unit Analyze collected life logs to evaluate the user's health status and lifestyle habits 2. The system of claim 1.

4. The proposal unit Based on the analysis results, we provide anti-aging suggestions for exercise plans or diet plans.

2. The system of claim 1.

5. The point providing unit Offer points when users purchase products based on suggestions 2. The system of claim 1.

6. The proposal unit For users who are not very active, we recommend walking three times a week.

2. The system of claim 1.

7. The proposal unit For users with unbalanced diets, we suggest a daily meal plan.

2. The system of claim 1.

8. The point providing unit Provide users with ways to use their points 2. The system of claim 1.

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