System

Abstract

An object of a system according to an embodiment is to analyze behavior data of a user and support proposal of habituation and maintenance of motivation.SOLUTION: A system includes a collection part, an analysis part, a proposal part, a message part, and a charging part. The collection unit collects action data of a user. The analysis unit analyzes the action data collected by the collection unit. The proposal unit proposes habituation on the basis of the analysis result obtained by the analysis unit. The message unit transmits a message of advice or encouragement based on the content proposed by the proposal unit. The charging unit manages user charging.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately suggest habits or maintain motivation based on user behavior data, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze user behavior data and provide suggestions for forming habits and support for maintaining motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, a message unit, and a billing unit. The collection unit collects user behavioral data. The analysis unit analyzes the behavioral data collected by the collection unit. The suggestion unit makes habit formation suggestions based on the analysis results obtained by the analysis unit. The message unit sends advice or encouraging messages based on the content suggested by the suggestion unit. The billing unit manages user billing. [Effects of the Invention]

[0007] The system according to the embodiment can analyze user behavior data and provide suggestions for forming habits and support for maintaining motivation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A habit formation recommendation system according to an embodiment of the present invention collects user behavioral data, analyzes it with a generation AI, makes habit formation suggestions, sends advice and encouragement messages, and manages billing. The habit formation recommendation system collects user behavioral data, analyzes it with a generation AI, and generates habit formation suggestions that are beneficial to the user. Furthermore, the generation AI sends advice and encouragement messages to the user to maintain motivation toward their goals. For example, the habit formation recommendation system automatically records user behavioral data using a smartphone or wearable device. For example, data such as the number of steps taken, exercise time, and sleep time is collected. Next, the generation AI analyzes the collected behavioral data. The generation AI analyzes the user's behavioral patterns and generates habit formation suggestions that are beneficial to the user. For example, if the system determines that exercising at the same time every day is healthy, it suggests exercising at that time. Also, if the system determines that reading at a certain time helps relieve stress, it suggests reading at that time. Next, the generation AI sends advice and encouragement messages to the user. For example, messages such as "Try your best to exercise today!" or "Enjoy your reading time!" are sent. This makes it easier for users to maintain motivation toward their goals. Furthermore, habit formation recommendation systems can also be monetized by charging users. For example, fees could be set based on a monthly fee or the number of uses. This allows the habit formation recommendation system to efficiently collect, analyze, make suggestions, send messages, and manage billing for users' behavioral data. This allows the habit formation recommendation system to efficiently collect, analyze, make suggestions, send messages, and manage billing for users' behavioral data. For example, it makes it easier for users to maintain habits for maintaining their health or relieving stress. It also allows service providers to earn stable revenue.

[0029] A habit formation recommendation system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a message unit, and a billing unit. The collection unit collects user behavioral data. The behavioral data includes, but is not limited to, location information, activity logs, and app usage history. The collection unit automatically records the user behavioral data using, for example, a smartphone or a wearable device. The collection unit can also collect the user behavioral data in real time. For example, the collection unit records the number of steps and exercise time using a sensor on the smartphone. The analysis unit uses a generation AI to analyze the behavioral data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to these examples. For example, the generation AI analyzes the user's behavioral patterns and generates habit formation suggestions that are beneficial to the user. The suggestion unit makes habit formation suggestions based on the analysis results obtained by the analysis unit. The habit formation suggestions include, for example, exercise habits, eating habits, and study habits, but are not limited to these examples. For example, the suggestion unit suggests that the user exercise at the same time every day. The suggestion unit can also suggest that the user read at a certain time. The message unit sends advice or encouraging messages based on the content suggested by the suggestion unit. Examples of advice or encouraging messages include, but are not limited to, text messages, voice messages, and notifications. For example, the message unit sends messages such as "Let's exercise hard today!" or "Enjoy your time reading!" The billing unit manages user billing. Billing management includes, but is not limited to, the type of billing plan, billing timing, and payment method. For example, the billing unit manages fee settings based on monthly fees and number of uses. This enables the habit formation recommendation system according to the embodiment to efficiently collect, analyze, suggest, send messages, and manage billing for users' behavioral data.

[0030] The suggestion unit can generate specific habit formation suggestions based on the user's behavioral patterns. The suggestion unit, for example, analyzes the user's behavioral patterns and generates habit formation suggestions that are beneficial to the user. For example, the suggestion unit can suggest that the user exercise at the same time every day. The suggestion unit can also suggest that the user read at a fixed time. The suggestion unit can also make suggestions about eating habits and study habits based on the user's behavioral patterns. For example, the suggestion unit can suggest that the user eat meals at the same time every day. The suggestion unit can also suggest that the user study at a fixed time. This makes it possible to make specific habit formation suggestions based on the user's behavioral patterns.

[0031] The message unit can send personalized advice or encouraging messages based on the user's behavioral history. The message unit, for example, analyzes the user's behavioral history and generates personalized advice or encouraging messages. For example, the message unit can send encouraging messages based on actions that the user has previously succeeded in. The message unit can also send messages containing advice for improvement based on actions that the user has previously failed in. The message unit can also analyze the user's behavioral history and send messages at optimal times. For example, the message unit can send messages based on the time period in which the user previously performed an action. This makes it possible to send personalized messages based on the user's behavioral history.

[0032] The billing unit can manage the differences between the free version and the paid version, and the additional functions provided in the paid version. For example, the billing unit can manage the differences between the free version and the paid version, and the additional functions provided in the paid version. For example, the billing unit can provide only basic functions in the free version, and additional functions in the paid version. The billing unit can also manage the fee settings and payment methods for the paid version. For example, the billing unit can manage fee settings based on monthly fees and number of uses. The billing unit can also manage the procedures for users to upgrade to the paid version. This makes it possible to manage the differences between the free version and the paid version, and the additional functions.

[0033] The collection unit can automatically record the user's behavioral data using a smartphone or a wearable device. The collection unit automatically records the user's behavioral data using, for example, a smartphone or a wearable device. For example, the collection unit records the number of steps and exercise time using a sensor in the smartphone. The collection unit can also record the heart rate and sleep time using a wearable device. The collection unit can also record location information using the smartphone's GPS function. For example, the collection unit records the route the user traveled. This makes it possible to automatically record behavioral data using a smartphone or a wearable device.

[0034] The analysis unit can analyze the user's behavioral data and generate suggestions for forming beneficial habits for the user. The analysis unit, for example, uses a generation AI to analyze the behavioral data collected by the collection unit. For example, the analysis unit analyzes the behavioral data using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze the user's behavioral patterns and generate suggestions for forming beneficial habits for the user. For example, the analysis unit can suggest that the user exercise at the same time every day. The analysis unit can also suggest that the user read at a certain time. The analysis unit can also analyze the user's behavioral data and make suggestions for eating habits and study habits. For example, the analysis unit can suggest that the user eat at the same time every day. The analysis unit can also suggest that the user study at a certain time. In this way, it is possible to analyze the user's behavioral data and make suggestions for forming beneficial habits.

[0035] The collection unit can analyze the user's past behavioral history and select an appropriate collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the user has frequently used in the past. The collection unit can also select the optimal collection time based on the user's past behavioral patterns. The collection unit can also analyze the user's past data collection history and select the most efficient collection method. This makes it possible to select the optimal collection method based on the user's past behavioral history.

[0036] The collection unit can filter the behavioral data based on the user's current living situation or areas of interest when collecting the behavioral data. For example, the collection unit filters the behavioral data based on the user's current living situation or areas of interest when collecting the behavioral data. For example, if the user is interested in health, the collection unit can prioritize collecting data related to exercise and diet. Furthermore, if the user is concentrating on work, the collection unit can prioritize collecting work-related data. Furthermore, if the user is relaxing, the collection unit can prioritize collecting data related to hobbies and entertainment. This makes it possible to filter the behavioral data based on the user's living situation and areas of interest.

[0037] The collection unit can select an appropriate collection means according to the user's input method when collecting behavioral data. For example, when collecting behavioral data, the collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Also, if the user prefers text input, the collection unit can also prioritize collecting text data. Also, if the user prefers image input, the collection unit can prioritize collecting image data. This makes it possible to select the optimal collection means according to the user's input method.

[0038] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information. For example, when collecting behavioral data, the collection unit prioritizes collecting highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data related to the user's home. This makes it possible to prioritize collecting highly relevant data based on the user's geographical location information.

[0039] The collection unit can analyze the content of the user's social media posts and collect related data when collecting behavioral data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting behavioral data. For example, the collection unit collects data related to places where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data based on the user's social media activities.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize the collection method that the user previously preferred. The collection unit can also analyze the user's past feedback and suggest the optimal collection method. This makes it possible to customize the collection method based on the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during analysis. The analysis unit, for example, uses a generation AI to adjust the level of detail of the analysis based on the importance of the behavioral data during analysis. For example, the analysis unit performs a detailed analysis on important behavioral data. The analysis unit can also perform a concise analysis on behavioral data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the behavioral data. This makes it possible to adjust the level of detail of the analysis based on the importance of the behavioral data.

[0042] The analysis unit can apply different analysis algorithms depending on the type of behavioral data during analysis. For example, the analysis unit uses a generation AI to apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit applies a health analysis algorithm to health-related data. The analysis unit can also apply a work analysis algorithm to work-related data. The analysis unit can also apply a hobby analysis algorithm to hobby-related data. This makes it possible to apply different analysis algorithms depending on the category of behavioral data.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis, for example, using a generative AI. For example, the analysis unit can adjust the current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0044] The analysis unit can determine the priority of analysis based on the time of submission of behavioral data during analysis. The analysis unit, for example, uses a generation AI to determine the priority of analysis based on the time of submission of behavioral data during analysis. For example, the analysis unit prioritizes analysis of the most recent behavioral data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of behavioral data.

[0045] The analysis unit can adjust the order of analysis based on the correlation of behavioral data during analysis. The analysis unit can adjust the order of analysis based on the relevance of behavioral data during analysis, for example, using a generative AI. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of data. This makes it possible to adjust the order of analysis based on the relevance of behavioral data.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis, for example, by using a generative AI. For example, if the user has technical knowledge, the analysis unit uses a lot of technical terms. Also, if the user does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. Also, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the habit at the time of suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the habit at the time of suggestion, for example, by using a generation AI. For example, the suggestion unit makes detailed suggestions for important habits. The suggestion unit can also make brief suggestions for less important habits. The suggestion unit can also determine the priority of the suggestions according to the importance of the habit. This makes it possible to adjust the level of detail of the suggestion based on the importance of the habit.

[0048] The suggestion unit can apply different suggestion algorithms depending on the type of habit at the time of suggestion. The suggestion unit can apply different suggestion algorithms depending on the category of habit at the time of suggestion, for example, by using a generation AI. For example, the suggestion unit can apply a health suggestion algorithm to health-related habit. The suggestion unit can also apply a work suggestion algorithm to work-related habit. The suggestion unit can also apply a hobby suggestion algorithm to hobby-related habit. This makes it possible to apply different suggestion algorithms depending on the category of habit.

[0049] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions, for example, using a generation AI. For example, the suggestion unit can adjust the current suggestion based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and improve the suggestion algorithm. The suggestion unit can also select the optimal suggestion method by referring to the user's past suggestion results. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results.

[0050] The suggestion unit can determine the priority of the proposals based on the time of submission of the habit formation at the time of proposal. The suggestion unit can, for example, use a generation AI to determine the priority of the proposals based on the time of submission of the habit formation at the time of proposal. For example, the suggestion unit can provide the most recent habit formation proposals with priority. The suggestion unit can also postpone proposals that were submitted earlier. The suggestion unit can also adjust the schedule of the proposals based on the time of submission. This makes it possible to determine the priority of the proposals based on the time of submission of the habit formation.

[0051] The suggestion unit can adjust the order of suggestions based on the correlation of habit formation when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of habit formation when making suggestions, for example, by using a generation AI. For example, the suggestion unit provides highly relevant habit formation suggestions with priority. The suggestion unit can also postpone less relevant suggestions. The suggestion unit can also adjust the schedule of suggestions based on the relevance of the suggestions. This makes it possible to adjust the order of suggestions based on the relevance of habit formation.

[0052] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can, for example, use a generation AI to adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit uses a lot of technical terminology when the user has technical knowledge. Furthermore, the suggestion unit can also explain the proposed content in simple terms when the user does not have technical knowledge. Furthermore, the suggestion unit can adjust the way the proposed content is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise.

[0053] The message unit can select appropriate message content based on the user's behavioral history when sending a message. The message unit, for example, uses a generation AI to select optimal message content based on the user's behavioral history when sending a message. For example, the message unit can send an encouraging message based on the user's past successful actions. The message unit can also send a message containing advice for improvement based on the user's past unsuccessful actions. The message unit can also analyze the user's behavioral history and send a message at the optimal timing. This makes it possible to select optimal message content based on the user's behavioral history.

[0054] The message unit can customize the message content based on the user's current living environment when sending a message. The message unit, for example, uses a generation AI to customize the message content based on the user's current living situation when sending a message. For example, if the user is busy, the message unit can send a short and to-the-point message. Alternatively, if the user is relaxed, the message unit can send a message containing detailed advice. The message unit can also adjust the message content according to the user's living situation. This makes it possible to customize the message content based on the user's current living situation.

[0055] The message unit can improve the message content by reflecting user feedback when the message is sent. The message unit can, for example, use a generation AI to improve the message content by reflecting user feedback when the message is sent. For example, the message unit can adjust the message content based on feedback previously provided by the user. The message unit can also analyze the user feedback and improve the way the message is expressed. The message unit can also select the optimal message content by referring to the user feedback. This makes it possible to improve the message content based on user feedback.

[0056] The message unit can select appropriate message content taking into account the user's geographical location information when sending a message. The message unit, for example, uses a generation AI to select optimal message content taking into account the user's geographical location information when sending a message. For example, if the user is in a specific area, the message unit can send a message related to that area. Also, if the user is traveling, the message unit can send a message related to the user's travel destination. Also, if the user is at home, the message unit can send a message related to the user's home. This makes it possible to select optimal message content based on the user's geographical location information.

[0057] The message unit can analyze the content of the user's social media posts and send relevant messages when sending a message. The message unit can, for example, use a generation AI to analyze the user's social media activity when sending a message and send relevant messages. For example, the message unit can send messages related to places the user has checked in to on social media. The message unit can also analyze the content of the user's social media posts and send relevant messages. The message unit can also send relevant messages based on the activities of the user's friends on social media. This makes it possible to send relevant messages based on the user's social media activity.

[0058] The message unit can customize the message content by reflecting the user's past feedback when sending a message. The message unit, for example, uses a generation AI to customize the message content by reflecting the user's past feedback when sending a message. For example, the message unit adjusts the message content based on feedback provided by the user in the past. The message unit can also analyze the user's feedback and improve the way the message is expressed. The message unit can also select the optimal message content by referring to the user's feedback. This makes it possible to customize the message content based on the user's past feedback.

[0059] The billing unit can analyze the user's usage history and propose an appropriate billing plan during billing management. The billing unit can, for example, use a generation AI to analyze the user's usage history and propose an optimal billing plan during billing management. For example, the billing unit can propose an optimal billing plan based on services the user has used in the past. The billing unit can also analyze the user's usage history and propose a billing plan based on usage frequency. The billing unit can also refer to the user's usage history to propose a billing plan that includes benefits. This makes it possible to propose an optimal billing plan based on the user's usage history.

[0060] The billing unit can customize the billing plan based on the user's current living environment during billing management. The billing unit, for example, uses a generation AI to customize the billing plan based on the user's current living situation during billing management. For example, the billing unit can suggest a short-term billing plan if the user is busy. The billing unit can also suggest a long-term billing plan if the user is relaxed. The billing unit can also adjust the content of the billing plan depending on the user's living situation. This makes it possible to customize the billing plan based on the user's current living situation.

[0061] The billing unit can improve the billing plan by reflecting user feedback during billing management. The billing unit can improve the billing plan by reflecting user feedback during billing management, for example, using a generation AI. For example, the billing unit can adjust the billing plan based on feedback previously provided by the user. The billing unit can also analyze user feedback and improve the content of the billing plan. The billing unit can also propose an optimal billing plan by referring to user feedback. This makes it possible to improve the billing plan based on user feedback.

[0062] The billing unit can propose an appropriate billing plan taking into account the user's geographical location information during billing management. The billing unit, for example, uses a generation AI to propose an optimal billing plan taking into account the user's geographical location information during billing management. For example, if the user is in a specific area, the billing unit can propose a billing plan related to that area. Also, if the user is traveling, the billing unit can propose a billing plan related to the user's travel destination. Also, if the user is at home, the billing unit can propose a billing plan related to the user's home. This makes it possible to propose an optimal billing plan based on the user's geographical location information.

[0063] The billing unit can analyze the content of a user's social media posts and suggest a relevant billing plan during billing management. The billing unit can, for example, use a generation AI to analyze the user's social media activities and suggest a relevant billing plan during billing management. For example, the billing unit can suggest a billing plan related to a location where the user checked in on social media. The billing unit can also analyze the content of a user's social media posts and suggest a relevant billing plan. The billing unit can also suggest a relevant billing plan based on the activities of the user's friends on social media. This makes it possible to suggest relevant billing plans based on the user's social media activities.

[0064] The billing unit can customize the billing plan by reflecting the user's past feedback during billing management. The billing unit, for example, uses a generation AI to customize the billing plan by reflecting the user's past feedback during billing management. For example, the billing unit adjusts the billing plan based on feedback provided by the user in the past. The billing unit can also analyze the user's feedback and improve the content of the billing plan. The billing unit can also suggest an optimal billing plan by referring to the user's feedback. This makes it possible to customize the billing plan based on the user's past feedback.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The analysis unit can also estimate the user's health condition based on the user's behavioral data and suggest habits based on the user's health condition. For example, the analysis unit analyzes data such as the user's number of steps, exercise time, and heart rate to estimate the user's health condition. Next, the analysis unit can suggest appropriate exercise and diet based on the estimated health condition. Furthermore, the analysis unit can also suggest that the user visit a medical institution if the user's health condition is deteriorating. This makes it possible to suggest specific habits based on the user's health condition.

[0067] The suggestion unit can also suggest habits related to the user's hobbies and interests based on the user's behavioral patterns. For example, the suggestion unit can analyze data on the user's past hobby activities and suggest new hobbies that the user might be interested in. If the user is interested in a particular field, the suggestion unit can also suggest learning or activities related to that field. Furthermore, the suggestion unit can make suggestions that are useful for relaxation or stress relief based on activities the user has enjoyed in the past. This makes it possible to suggest specific habits based on the user's hobbies and interests.

[0068] The message unit can also send messages reporting the user's progress toward achieving a goal based on the user's behavior history. For example, the message unit periodically reports the user's progress toward a goal set by the user. The message unit can also send an encouraging message if the user is approaching the goal. Furthermore, the message unit can also send a message including advice for improvement if the user is moving away from the goal. This makes it possible to report the user's progress toward achieving the goal.

[0069] The billing unit can provide benefits and discounts according to the user's usage. For example, the billing unit can provide a benefit if the user achieves a specific goal within a certain period of time. The billing unit can also provide a discount if the user uses the service for a long period of time. Furthermore, the billing unit can also provide a referral benefit if the user refers a friend. This makes it possible to provide benefits and discounts according to the user's usage.

[0070] The collection unit may perform filtering to protect the user's privacy when collecting the user's behavioral data. For example, the collection unit may anonymize the user's personal information before collecting it. The collection unit may also allow the user to select the type and scope of the collected data. Furthermore, the collection unit may collect data with the user's consent. This makes it possible to collect behavioral data while protecting the user's privacy.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects user behavioral data. The behavioral data includes location information, activity logs, and app usage history. The collection unit can also automatically record and collect user behavioral data in real time using a smartphone or wearable device. For example, the number of steps and exercise time can be recorded using a smartphone sensor. Step 2: The analysis unit uses the generation AI to analyze the behavioral data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI analyzes the user's behavioral patterns and generates habit-forming suggestions that are beneficial to the user. Step 3: The suggestion unit makes habit formation suggestions based on the analysis results obtained by the analysis unit. Habit formation suggestions include exercise habits, eating habits, study habits, etc. For example, the suggestion unit suggests that the user exercise at the same time every day or read at a set time. Step 4: The message module sends advice and encouragement messages based on the suggestions made by the suggestion module. Advice and encouragement messages can include text messages, voice messages, notifications, etc. For example, messages such as "Make sure you exercise hard today!" or "Enjoy your reading time!" are sent. Step 5: The billing unit manages user billing. Billing management includes the type of billing plan, timing of billing, payment method, etc. For example, it manages monthly fees and fee settings according to the number of times of use.

[0073] (Example 2) A habit formation recommendation system according to an embodiment of the present invention collects user behavioral data, analyzes it with a generation AI, makes habit formation suggestions, sends advice and encouragement messages, and manages billing. The habit formation recommendation system collects user behavioral data, analyzes it with a generation AI, and generates habit formation suggestions that are beneficial to the user. Furthermore, the generation AI sends advice and encouragement messages to the user to maintain motivation toward their goals. For example, the habit formation recommendation system automatically records user behavioral data using a smartphone or wearable device. For example, data such as the number of steps taken, exercise time, and sleep time is collected. Next, the generation AI analyzes the collected behavioral data. The generation AI analyzes the user's behavioral patterns and generates habit formation suggestions that are beneficial to the user. For example, if the system determines that exercising at the same time every day is healthy, it suggests exercising at that time. Also, if the system determines that reading at a certain time helps relieve stress, it suggests reading at that time. Next, the generation AI sends advice and encouragement messages to the user. For example, messages such as "Try your best to exercise today!" or "Enjoy your reading time!" are sent. This makes it easier for users to maintain motivation toward their goals. Furthermore, habit formation recommendation systems can also be monetized by charging users. For example, fees could be set based on a monthly fee or the number of uses. This allows the habit formation recommendation system to efficiently collect, analyze, make suggestions, send messages, and manage billing for users' behavioral data. This allows the habit formation recommendation system to efficiently collect, analyze, make suggestions, send messages, and manage billing for users' behavioral data. For example, it makes it easier for users to maintain habits for maintaining their health or relieving stress. It also allows service providers to earn stable revenue.

[0074] A habit formation recommendation system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, a message unit, and a billing unit. The collection unit collects user behavioral data. The behavioral data includes, but is not limited to, location information, activity logs, and app usage history. The collection unit automatically records the user behavioral data using, for example, a smartphone or a wearable device. The collection unit can also collect the user behavioral data in real time. For example, the collection unit records the number of steps and exercise time using a sensor on the smartphone. The analysis unit uses a generation AI to analyze the behavioral data collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or the like, but is not limited to these examples. For example, the generation AI analyzes the user's behavioral patterns and generates habit formation suggestions that are beneficial to the user. The suggestion unit makes habit formation suggestions based on the analysis results obtained by the analysis unit. The habit formation suggestions include, for example, exercise habits, eating habits, and study habits, but are not limited to these examples. For example, the suggestion unit suggests that the user exercise at the same time every day. The suggestion unit can also suggest that the user read at a certain time. The message unit sends advice or encouraging messages based on the content suggested by the suggestion unit. Examples of advice or encouraging messages include, but are not limited to, text messages, voice messages, and notifications. For example, the message unit sends messages such as "Let's exercise hard today!" or "Enjoy your time reading!" The billing unit manages user billing. Billing management includes, but is not limited to, the type of billing plan, billing timing, and payment method. For example, the billing unit manages fee settings based on monthly fees and number of uses. This enables the habit formation recommendation system according to the embodiment to efficiently collect, analyze, suggest, send messages, and manage billing for users' behavioral data.

[0075] The suggestion unit can generate specific habit formation suggestions based on the user's behavioral patterns. The suggestion unit, for example, analyzes the user's behavioral patterns and generates habit formation suggestions that are beneficial to the user. For example, the suggestion unit can suggest that the user exercise at the same time every day. The suggestion unit can also suggest that the user read at a fixed time. The suggestion unit can also make suggestions about eating habits and study habits based on the user's behavioral patterns. For example, the suggestion unit can suggest that the user eat meals at the same time every day. The suggestion unit can also suggest that the user study at a fixed time. This makes it possible to make specific habit formation suggestions based on the user's behavioral patterns.

[0076] The message unit can send personalized advice or encouraging messages based on the user's behavioral history. The message unit, for example, analyzes the user's behavioral history and generates personalized advice or encouraging messages. For example, the message unit can send encouraging messages based on actions that the user has previously succeeded in. The message unit can also send messages containing advice for improvement based on actions that the user has previously failed in. The message unit can also analyze the user's behavioral history and send messages at optimal times. For example, the message unit can send messages based on the time period in which the user previously performed an action. This makes it possible to send personalized messages based on the user's behavioral history.

[0077] The billing unit can manage the differences between the free version and the paid version, and the additional functions provided in the paid version. For example, the billing unit can manage the differences between the free version and the paid version, and the additional functions provided in the paid version. For example, the billing unit can provide only basic functions in the free version, and additional functions in the paid version. The billing unit can also manage the fee settings and payment methods for the paid version. For example, the billing unit can manage fee settings based on monthly fees and number of uses. The billing unit can also manage the procedures for users to upgrade to the paid version. This makes it possible to manage the differences between the free version and the paid version, and the additional functions.

[0078] The collection unit can automatically record the user's behavioral data using a smartphone or a wearable device. The collection unit automatically records the user's behavioral data using, for example, a smartphone or a wearable device. For example, the collection unit records the number of steps and exercise time using a sensor in the smartphone. The collection unit can also record the heart rate and sleep time using a wearable device. The collection unit can also record location information using the smartphone's GPS function. For example, the collection unit records the route the user traveled. This makes it possible to automatically record behavioral data using a smartphone or a wearable device.

[0079] The analysis unit can analyze the user's behavioral data and generate suggestions for forming beneficial habits for the user. The analysis unit, for example, uses a generation AI to analyze the behavioral data collected by the collection unit. For example, the analysis unit analyzes the behavioral data using methods such as data mining, statistical analysis, and machine learning algorithms. The analysis unit can also analyze the user's behavioral patterns and generate suggestions for forming beneficial habits for the user. For example, the analysis unit can suggest that the user exercise at the same time every day. The analysis unit can also suggest that the user read at a certain time. The analysis unit can also analyze the user's behavioral data and make suggestions for eating habits and study habits. For example, the analysis unit can suggest that the user eat at the same time every day. The analysis unit can also suggest that the user study at a certain time. In this way, it is possible to analyze the user's behavioral data and make suggestions for forming beneficial habits.

[0080] The collection unit can estimate the user's emotions and adjust the timing of collecting behavioral data based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting behavioral data based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects behavioral data during a relaxing time. If the user is excited, the collection unit can also collect behavioral data after the user has calmed down. If the user is tired, the collection unit can also collect behavioral data after the user has rested. This makes it possible to adjust the timing of collecting behavioral data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The collection unit can analyze the user's past behavioral history and select an appropriate collection method. The collection unit, for example, analyzes the user's past behavioral history and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the user has frequently used in the past. The collection unit can also select the optimal collection time based on the user's past behavioral patterns. The collection unit can also analyze the user's past data collection history and select the most efficient collection method. This makes it possible to select the optimal collection method based on the user's past behavioral history.

[0082] The collection unit can filter the behavioral data based on the user's current living situation or areas of interest when collecting the behavioral data. For example, the collection unit filters the behavioral data based on the user's current living situation or areas of interest when collecting the behavioral data. For example, if the user is interested in health, the collection unit can prioritize collecting data related to exercise and diet. Furthermore, if the user is concentrating on work, the collection unit can prioritize collecting work-related data. Furthermore, if the user is relaxing, the collection unit can prioritize collecting data related to hobbies and entertainment. This makes it possible to filter the behavioral data based on the user's living situation and areas of interest.

[0083] The collection unit can select an appropriate collection means according to the user's input method when collecting behavioral data. For example, when collecting behavioral data, the collection unit selects the optimal collection means according to the user's input method (voice, text, image, etc.). For example, if the user prefers voice input, the collection unit can prioritize collecting voice data. Also, if the user prefers text input, the collection unit can also prioritize collecting text data. Also, if the user prefers image input, the collection unit can prioritize collecting image data. This makes it possible to select the optimal collection means according to the user's input method.

[0084] The collection unit can estimate the user's emotions and determine the priority of behavioral data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of behavioral data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress relief. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data related to relaxation. Furthermore, if the user is excited, the collection unit can also prioritize collecting data related to excitement. This makes it possible to determine the priority of behavioral data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data in consideration of the user's geographical location information. For example, when collecting behavioral data, the collection unit prioritizes collecting highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data related to the user's home. This makes it possible to prioritize collecting highly relevant data based on the user's geographical location information.

[0086] The collection unit can analyze the content of the user's social media posts and collect related data when collecting behavioral data. For example, the collection unit analyzes the user's social media activities and collects related data when collecting behavioral data. For example, the collection unit collects data related to places where the user checked in on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect related data based on the user's social media activities.

[0087] The collection unit can customize the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting behavioral data. For example, the collection unit adjusts the collection method based on feedback provided by the user in the past. The collection unit can also prioritize the collection method that the user previously preferred. The collection unit can also analyze the user's past feedback and suggest the optimal collection method. This makes it possible to customize the collection method based on the user's past feedback.

[0088] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results that focus on the main points when the user is in a hurry. The analysis unit can also provide visually stimulating analysis results when the user is excited. This makes it possible to adjust the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavioral data during analysis. The analysis unit, for example, uses a generation AI to adjust the level of detail of the analysis based on the importance of the behavioral data during analysis. For example, the analysis unit performs a detailed analysis on important behavioral data. The analysis unit can also perform a concise analysis on behavioral data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the behavioral data. This makes it possible to adjust the level of detail of the analysis based on the importance of the behavioral data.

[0090] The analysis unit can apply different analysis algorithms depending on the type of behavioral data during analysis. For example, the analysis unit uses a generation AI to apply different analysis algorithms depending on the category of behavioral data during analysis. For example, the analysis unit applies a health analysis algorithm to health-related data. The analysis unit can also apply a work analysis algorithm to work-related data. The analysis unit can also apply a hobby analysis algorithm to hobby-related data. This makes it possible to apply different analysis algorithms depending on the category of behavioral data.

[0091] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis, for example, using a generative AI. For example, the analysis unit can adjust the current analysis based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and improve the analysis algorithm. The analysis unit can also select the optimal analysis method by referring to the user's past analysis results. This makes it possible to improve the accuracy of the analysis based on the user's past analysis results.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions using, for example, a generation AI and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a short analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. This makes it possible to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The analysis unit can determine the priority of analysis based on the time of submission of behavioral data during analysis. The analysis unit, for example, uses a generation AI to determine the priority of analysis based on the time of submission of behavioral data during analysis. For example, the analysis unit prioritizes analysis of the most recent behavioral data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. This makes it possible to determine the priority of analysis based on the time of submission of behavioral data.

[0094] The analysis unit can adjust the order of analysis based on the correlation of behavioral data during analysis. The analysis unit can adjust the order of analysis based on the relevance of behavioral data during analysis, for example, using a generative AI. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of data. This makes it possible to adjust the order of analysis based on the relevance of behavioral data.

[0095] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis, for example, by using a generative AI. For example, if the user has technical knowledge, the analysis unit uses a lot of technical terms. Also, if the user does not have technical knowledge, the analysis unit can explain the analysis results in simple terms. Also, the analysis unit can adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0096] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, a generation AI and adjust the way the suggestion is expressed based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Furthermore, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually stimulating suggestions when the user is excited. This makes it possible to adjust the way the suggestion is expressed based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the habit at the time of suggestion. The suggestion unit can adjust the level of detail of the suggestion based on the importance of the habit at the time of suggestion, for example, by using a generation AI. For example, the suggestion unit makes detailed suggestions for important habits. The suggestion unit can also make brief suggestions for less important habits. The suggestion unit can also determine the priority of the suggestions according to the importance of the habit. This makes it possible to adjust the level of detail of the suggestion based on the importance of the habit.

[0098] The suggestion unit can apply different suggestion algorithms depending on the type of habit at the time of suggestion. The suggestion unit can apply different suggestion algorithms depending on the category of habit at the time of suggestion, for example, by using a generation AI. For example, the suggestion unit can apply a health suggestion algorithm to health-related habit. The suggestion unit can also apply a work suggestion algorithm to work-related habit. The suggestion unit can also apply a hobby suggestion algorithm to hobby-related habit. This makes it possible to apply different suggestion algorithms depending on the category of habit.

[0099] The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions. The suggestion unit can improve the accuracy of suggestions by referring to the user's past suggestion results when making suggestions, for example, using a generation AI. For example, the suggestion unit can adjust the current suggestion based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and improve the suggestion algorithm. The suggestion unit can also select the optimal suggestion method by referring to the user's past suggestion results. This makes it possible to improve the accuracy of suggestions based on the user's past suggestion results.

[0100] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, a generation AI and adjust the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide a short suggestion if the user is in a hurry. The suggestion unit can also provide a detailed suggestion if the user is relaxed. The suggestion unit can also provide a visually stimulating suggestion if the user is excited. This makes it possible to adjust the length of the suggestion based on the user's emotion. The emotion estimation is realized using, for example, an emotion estimation function using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0101] The suggestion unit can determine the priority of the proposals based on the time of submission of the habit formation at the time of proposal. The suggestion unit can, for example, use a generation AI to determine the priority of the proposals based on the time of submission of the habit formation at the time of proposal. For example, the suggestion unit can provide the most recent habit formation proposals with priority. The suggestion unit can also postpone proposals that were submitted earlier. The suggestion unit can also adjust the schedule of the proposals based on the time of submission. This makes it possible to determine the priority of the proposals based on the time of submission of the habit formation.

[0102] The suggestion unit can adjust the order of suggestions based on the correlation of habit formation when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of habit formation when making suggestions, for example, by using a generation AI. For example, the suggestion unit provides highly relevant habit formation suggestions with priority. The suggestion unit can also postpone less relevant suggestions. The suggestion unit can also adjust the schedule of suggestions based on the relevance of the suggestions. This makes it possible to adjust the order of suggestions based on the relevance of habit formation.

[0103] The suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. The suggestion unit can, for example, use a generation AI to adjust the use of technical terminology in the proposal according to the user's level of expertise when making a proposal. For example, the suggestion unit uses a lot of technical terminology when the user has technical knowledge. Furthermore, the suggestion unit can also explain the proposed content in simple terms when the user does not have technical knowledge. Furthermore, the suggestion unit can adjust the way the proposed content is expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the proposal according to the user's level of expertise.

[0104] The message unit can estimate the user's emotions and adjust the way the message is expressed based on the estimated user's emotions. The message unit can estimate the user's emotions using, for example, a generation AI and adjust the way the message is expressed based on the estimated user's emotions. For example, if the user is relaxed, the message unit can send a message using gentle words. If the user is in a hurry, the message unit can also send a concise and to-the-point message. If the user is excited, the message unit can also send a message containing many encouraging words. This makes it possible to adjust the way the message is expressed based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0105] The message unit can select appropriate message content based on the user's behavioral history when sending a message. The message unit, for example, uses a generation AI to select optimal message content based on the user's behavioral history when sending a message. For example, the message unit can send an encouraging message based on the user's past successful actions. The message unit can also send a message containing advice for improvement based on the user's past unsuccessful actions. The message unit can also analyze the user's behavioral history and send a message at the optimal timing. This makes it possible to select optimal message content based on the user's behavioral history.

[0106] The message unit can customize the message content based on the user's current living environment when sending a message. The message unit, for example, uses a generation AI to customize the message content based on the user's current living situation when sending a message. For example, if the user is busy, the message unit can send a short and to-the-point message. Alternatively, if the user is relaxed, the message unit can send a message containing detailed advice. The message unit can also adjust the message content according to the user's living situation. This makes it possible to customize the message content based on the user's current living situation.

[0107] The message unit can improve the message content by reflecting user feedback when the message is sent. The message unit can, for example, use a generation AI to improve the message content by reflecting user feedback when the message is sent. For example, the message unit can adjust the message content based on feedback previously provided by the user. The message unit can also analyze the user feedback and improve the way the message is expressed. The message unit can also select the optimal message content by referring to the user feedback. This makes it possible to improve the message content based on user feedback.

[0108] The message unit can estimate the user's emotions and adjust the timing of message transmission based on the estimated user emotions. The message unit can estimate the user's emotions using, for example, a generation AI and adjust the timing of message transmission based on the estimated user emotions. For example, if the user is relaxed, the message unit can send a message during a time period when the user is relaxed. Also, if the user is in a hurry, the message unit can send a message avoiding a time period when the user is in a hurry. Also, if the user is excited, the message unit can send a message after the user has calmed down. This makes it possible to adjust the timing of message transmission based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0109] The message unit can select appropriate message content taking into account the user's geographical location information when sending a message. The message unit, for example, uses a generation AI to select optimal message content taking into account the user's geographical location information when sending a message. For example, if the user is in a specific area, the message unit can send a message related to that area. Also, if the user is traveling, the message unit can send a message related to the user's travel destination. Also, if the user is at home, the message unit can send a message related to the user's home. This makes it possible to select optimal message content based on the user's geographical location information.

[0110] The message unit can analyze the content of the user's social media posts and send relevant messages when sending a message. The message unit can, for example, use a generation AI to analyze the user's social media activity when sending a message and send relevant messages. For example, the message unit can send messages related to places the user has checked in to on social media. The message unit can also analyze the content of the user's social media posts and send relevant messages. The message unit can also send relevant messages based on the activities of the user's friends on social media. This makes it possible to send relevant messages based on the user's social media activity.

[0111] The message unit can customize the message content by reflecting the user's past feedback when sending a message. The message unit, for example, uses a generation AI to customize the message content by reflecting the user's past feedback when sending a message. For example, the message unit adjusts the message content based on feedback provided by the user in the past. The message unit can also analyze the user's feedback and improve the way the message is expressed. The message unit can also select the optimal message content by referring to the user's feedback. This makes it possible to customize the message content based on the user's past feedback.

[0112] The billing unit can estimate the user's emotions and adjust the proposed billing plan based on the estimated user emotions. The billing unit can estimate the user's emotions using, for example, a generation AI and adjust the proposed billing plan based on the estimated user emotions. For example, the billing unit can propose a detailed billing plan when the user is relaxed. The billing unit can also propose a concise billing plan when the user is in a hurry. The billing unit can also propose a billing plan with many perks when the user is excited. This makes it possible to adjust the proposed billing plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The billing unit can analyze the user's usage history and propose an appropriate billing plan during billing management. The billing unit can, for example, use a generation AI to analyze the user's usage history and propose an optimal billing plan during billing management. For example, the billing unit can propose an optimal billing plan based on services the user has used in the past. The billing unit can also analyze the user's usage history and propose a billing plan based on usage frequency. The billing unit can also refer to the user's usage history to propose a billing plan that includes benefits. This makes it possible to propose an optimal billing plan based on the user's usage history.

[0114] The billing unit can customize the billing plan based on the user's current living environment during billing management. The billing unit, for example, uses a generation AI to customize the billing plan based on the user's current living situation during billing management. For example, the billing unit can suggest a short-term billing plan if the user is busy. The billing unit can also suggest a long-term billing plan if the user is relaxed. The billing unit can also adjust the content of the billing plan depending on the user's living situation. This makes it possible to customize the billing plan based on the user's current living situation.

[0115] The billing unit can improve the billing plan by reflecting user feedback during billing management. The billing unit can improve the billing plan by reflecting user feedback during billing management, for example, using a generation AI. For example, the billing unit can adjust the billing plan based on feedback previously provided by the user. The billing unit can also analyze user feedback and improve the content of the billing plan. The billing unit can also propose an optimal billing plan by referring to user feedback. This makes it possible to improve the billing plan based on user feedback.

[0116] The billing unit can estimate the user's emotions and determine the priority of billing plans based on the estimated user emotions. The billing unit can estimate the user's emotions using, for example, a generation AI and determine the priority of billing plans based on the estimated user emotions. For example, the billing unit can prioritize and suggest detailed billing plans when the user is relaxed. The billing unit can also prioritize and suggest simple billing plans when the user is in a hurry. The billing unit can also prioritize and suggest billing plans with many perks when the user is excited. This makes it possible to determine the priority of billing plans based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0117] The billing unit can propose an appropriate billing plan taking into account the user's geographical location information during billing management. The billing unit, for example, uses a generation AI to propose an optimal billing plan taking into account the user's geographical location information during billing management. For example, if the user is in a specific area, the billing unit can propose a billing plan related to that area. Also, if the user is traveling, the billing unit can propose a billing plan related to the user's travel destination. Also, if the user is at home, the billing unit can propose a billing plan related to the user's home. This makes it possible to propose an optimal billing plan based on the user's geographical location information.

[0118] The billing unit can analyze the content of a user's social media posts and suggest a relevant billing plan during billing management. The billing unit can, for example, use a generation AI to analyze the user's social media activities and suggest a relevant billing plan during billing management. For example, the billing unit can suggest a billing plan related to a location where the user checked in on social media. The billing unit can also analyze the content of a user's social media posts and suggest a relevant billing plan. The billing unit can also suggest a relevant billing plan based on the activities of the user's friends on social media. This makes it possible to suggest relevant billing plans based on the user's social media activities.

[0119] The billing unit can customize the billing plan by reflecting the user's past feedback during billing management. The billing unit, for example, uses a generation AI to customize the billing plan by reflecting the user's past feedback during billing management. For example, the billing unit adjusts the billing plan based on feedback provided by the user in the past. The billing unit can also analyze the user's feedback and improve the content of the billing plan. The billing unit can also suggest an optimal billing plan by referring to the user's feedback. This makes it possible to customize the billing plan based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, message unit, and billing unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavior data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes habit formation suggestions based on the analysis results. The message unit is realized by the control unit 46A of the smart device 14 and sends advice or encouraging messages based on the suggestions. The billing unit is realized by the specific processing unit 290 of the data processing device 12 and manages user billing. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, message unit, and billing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavior data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes habit formation suggestions based on the analysis results. The message unit is realized by the control unit 46A of the smart glasses 214 and sends advice or encouraging messages based on the suggested content. The billing unit is realized by the specific processing unit 290 of the data processing device 12 and manages user billing. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, message unit, and billing unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavior data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes habit formation suggestions based on the analysis results. The message unit is realized by the control unit 46A of the headset-type terminal 314 and sends advice or encouraging messages based on the suggestions. The billing unit is realized by the specific processing unit 290 of the data processing device 12 and manages user billing. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, suggestion unit, message unit, and billing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect user behavior data using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavior data. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and makes habit formation suggestions based on the analysis results. The message unit is realized by the control unit 46A of the robot 414 and sends advice or encouraging messages based on the suggestions. The billing unit is realized by the specific processing unit 290 of the data processing device 12 and manages user billing.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The analysis unit can also estimate the user's health condition based on the user's behavioral data and suggest habits based on the user's health condition. For example, the analysis unit analyzes data such as the user's number of steps, exercise time, and heart rate to estimate the user's health condition. Next, the analysis unit can suggest appropriate exercise and diet based on the estimated health condition. Furthermore, the analysis unit can also suggest that the user visit a medical institution if the user's health condition is deteriorating. This makes it possible to suggest specific habits based on the user's health condition.

[0122] The suggestion unit can also suggest habits related to the user's hobbies and interests based on the user's behavioral patterns. For example, the suggestion unit can analyze data on the user's past hobby activities and suggest new hobbies that the user might be interested in. If the user is interested in a particular field, the suggestion unit can also suggest learning or activities related to that field. Furthermore, the suggestion unit can make suggestions that are useful for relaxation or stress relief based on activities the user has enjoyed in the past. This makes it possible to suggest specific habits based on the user's hobbies and interests.

[0123] The message unit can also send messages reporting the user's progress toward achieving a goal based on the user's behavior history. For example, the message unit periodically reports the user's progress toward a goal set by the user. The message unit can also send an encouraging message if the user is approaching the goal. Furthermore, the message unit can also send a message including advice for improvement if the user is moving away from the goal. This makes it possible to report the user's progress toward achieving the goal.

[0124] The billing unit can provide benefits and discounts according to the user's usage. For example, the billing unit can provide a benefit if the user achieves a specific goal within a certain period of time. The billing unit can also provide a discount if the user uses the service for a long period of time. Furthermore, the billing unit can also provide a referral benefit if the user refers a friend. This makes it possible to provide benefits and discounts according to the user's usage.

[0125] The collection unit may perform filtering to protect the user's privacy when collecting the user's behavioral data. For example, the collection unit may anonymize the user's personal information before collecting it. The collection unit may also allow the user to select the type and scope of the collected data. Furthermore, the collection unit may collect data with the user's consent. This makes it possible to collect behavioral data while protecting the user's privacy.

[0126] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. The analysis unit can also display concise analysis results when the user is in a hurry. Furthermore, the analysis unit can display visually stimulating analysis results when the user is excited. This makes it possible to adjust the display method of the analysis results based on the user's emotions.

[0127] The collection unit can estimate the user's emotions and adjust the timing of collecting behavioral data based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can collect behavioral data during a relaxing time. If the user is excited, the collection unit can also collect behavioral data after the user has calmed down. Furthermore, if the user is tired, the collection unit can also collect behavioral data after the user has rested. This makes it possible to adjust the timing of collecting behavioral data based on the user's emotions.

[0128] The suggestion unit can estimate the user's emotion and adjust the way suggestions are presented based on the estimated user's emotion. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. Alternatively, the suggestion unit can provide concise suggestions that focus on the main points when the user is in a hurry. Furthermore, the suggestion unit can provide visually stimulating suggestions when the user is excited. This makes it possible to adjust the way suggestions are presented based on the user's emotion.

[0129] The message unit can estimate the user's emotions and adjust the timing of message transmission based on the estimated user's emotions. For example, if the user is relaxed, the message unit can send a message during a time period when the user is relaxed. Also, if the user is in a hurry, the message unit can send a message that avoids a time period when the user is in a hurry. Furthermore, if the user is excited, the message unit can send a message after the user has calmed down. This makes it possible to adjust the timing of message transmission based on the user's emotions.

[0130] The billing unit can estimate the user's emotions and adjust the proposed billing plan based on the estimated user's emotions. For example, the billing unit can propose a detailed billing plan when the user is relaxed. The billing unit can also propose a simple billing plan when the user is in a hurry. Furthermore, the billing unit can propose a billing plan with many benefits when the user is excited. This makes it possible to adjust the proposed billing plan based on the user's emotions.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects user behavioral data. The behavioral data includes location information, activity logs, and app usage history. The collection unit can also automatically record and collect user behavioral data in real time using a smartphone or wearable device. For example, the number of steps and exercise time can be recorded using a smartphone sensor. Step 2: The analysis unit uses the generation AI to analyze the behavioral data collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, the generation AI analyzes the user's behavioral patterns and generates habit-forming suggestions that are beneficial to the user. Step 3: The suggestion unit makes habit formation suggestions based on the analysis results obtained by the analysis unit. Habit formation suggestions include exercise habits, eating habits, study habits, etc. For example, the suggestion unit suggests that the user exercise at the same time every day or read at a set time. Step 4: The message module sends advice and encouragement messages based on the suggestions made by the suggestion module. Advice and encouragement messages can include text messages, voice messages, notifications, etc. For example, messages such as "Make sure you exercise hard today!" or "Enjoy your reading time!" are sent. Step 5: The billing unit manages user billing. Billing management includes the type of billing plan, timing of billing, payment method, etc. For example, it manages monthly fees and fee settings according to the number of times of use.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 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.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the 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.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] 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.

[0185] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0191] 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."

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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, in order to avoid confusion and to 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.

[0203] 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.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user behavior data; an analysis unit that analyzes the behavioral data collected by the collection unit; a suggestion unit that suggests habit formation based on the analysis result obtained by the analysis unit; a message unit that transmits a message of advice or encouragement based on the content suggested by the suggestion unit; a billing unit that manages user billing; A system characterized by:

2. The proposal unit Generate specific habit-forming suggestions based on user behavior patterns 2. The system of claim 1.

3. The message section Sending personalized advice or encouraging messages based on user behavior 2. The system of claim 1.

4. The charging unit Manage the differences between the free and paid versions and the additional features offered in the paid version 2. The system of claim 1.

5. The collecting unit Automatically record user behavior data using a smartphone or wearable device 2. The system of claim 1.

6. The analysis unit Analyze user behavior data and generate useful habit suggestions for users 2. The system of claim 1.

7. The collecting unit The system estimates user emotions and adjusts the timing of behavioral data collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past behavior history and select the appropriate collection method 2. The system of claim 1.

9. The collecting unit As behavioral data is collected, it is filtered based on the user's current life situation or areas of interest.

2. The system of claim 1.

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