system

Abstract

The system according to the embodiment aims to provide personalized support by utilizing user behavioral data. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a provision unit, a synchronization unit, and an adaptation unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides support based on the analysis results obtained by the analysis unit. The synchronization unit synchronizes data between devices. The adaptation unit switches functions based on the user's growth.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not yet effectively utilized user behavioral data to provide personalized support, and there is room for improvement.

[0005] The system according to the embodiment aims to provide personalized support by utilizing user behavioral data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a synchronization unit, and an adaptation unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The provision unit provides support based on the analysis results obtained by the analysis unit. The synchronization unit synchronizes data between devices. The adaptation unit switches functions based on the user's growth. [Effects of the Invention]

[0007] The system according to the embodiment can utilize user behavioral data to provide personalized support. [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) The Buddy AI system according to an embodiment of the present invention is a dedicated support system for users. This Buddy AI system supports and grows alongside users across networked devices, such as smartphones, PCs, smartwatches, and IoT appliances. Specifically, the Buddy AI collects and analyzes user behavioral data. Based on the analysis results, it provides appropriate support tailored to the user's needs. For example, as a child, the Buddy AI acts as a teacher, reading textbooks and manuals. As an adult, the Buddy AI gathers necessary materials for work and points out overlooked points in idea development. The Buddy AI can also synchronize data across multiple devices and switch functions as the user grows. This allows the Buddy AI to support users in all aspects of their lives and function as the coveted personal partner. For example, the Buddy AI collects data from devices such as smartphones, PCs, smartwatches, and IoT appliances to collect user behavioral data. The Buddy AI then analyzes the collected data to understand the user's behavioral patterns and needs. For example, the Buddy AI analyzes the user's activities and the time of day, and provides appropriate support based on the results. Furthermore, the Buddy AI can switch functions as the user grows. For example, Buddy AI can provide educational support functions for children and work support functions for adults. Buddy AI can also synchronize data across multiple devices depending on the user's needs. This allows users to receive consistent support regardless of which device they are using. For example, Buddy AI can synchronize data collected on a smartphone with a PC or smartwatch, allowing users to receive the same support regardless of which device they are using. This allows Buddy AI to support users in all aspects of their lives and function as the personal partner everyone longs for. This allows the Buddy AI system to collect and analyze user behavioral data, provide support, synchronize data, and switch functions.

[0029] The buddy AI system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a synchronization unit, and an adaptation unit. The collection unit collects user behavioral data. Examples of the user behavioral data include, but are not limited to, website browsing history, app usage history, and location information. The collection unit acquires data from devices such as smartphones, PCs, smartwatches, and IoT home appliances. For example, the collection unit can acquire location information using a smartphone sensor and collect app usage history by analyzing PC log files. The collection unit can also collect user activity data using a smartwatch sensor. For example, the collection unit can measure the user's steps and exercise volume using the smartwatch's acceleration sensor and collect this data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data using AI to understand the user's behavioral patterns and needs. For example, the analysis unit can cluster the user's behavioral data using a machine learning algorithm to identify the user's behavioral patterns. The analysis unit can also analyze the user's text data using natural language processing technology to determine the user's needs. For example, the analysis unit can analyze a user's social media posts to understand what interests the user. The provision unit provides support tailored to the user's needs based on the analysis results obtained by the analysis unit. The provision unit can provide support in the form of notifications, advice, reminders, etc. For example, the provision unit can automatically collect and notify the user of materials needed for work. The provision unit can also remind the user of tasks that they tend to forget. For example, the provision unit can analyze the user's calendar and remind the user of important appointments. The synchronization unit synchronizes data between multiple devices. For example, the synchronization unit can synchronize data collected on a smartphone with a PC or smartwatch, allowing the user to receive consistent support regardless of which device they are using. For example, the synchronization unit can synchronize data using cloud storage, allowing the user to access the data from any device. The adaptation unit can switch functions, such as educational support or work support, according to the user's growth.For example, the adaptation unit can provide educational support functions when the user is a child and work support functions when the user becomes an adult. For example, the adaptation unit can automatically switch between appropriate functions based on the user's age and behavioral data. This enables the buddy AI system according to the embodiment to collect and analyze user behavioral data, provide support, synchronize data, and switch functions.

[0030] The collection unit can analyze the user's past behavioral data and select a collection method. The collection unit can optimize the collection method based on, for example, behaviors the user frequently performed in the past. The collection unit can also analyze the user's past behavioral patterns and select an efficient collection method. Furthermore, the collection unit can suggest an optimal collection method for a specific time period based on the user's past behavioral data. This enables efficient data collection by selecting an optimal collection method based on the past behavioral data. Past behavioral data includes, for example, data from the past year or data related to specific activities. The selection of a collection method includes, for example, selecting an optimal collection means depending on the type of data. For example, the collection unit can optimize the collection method for location information based on data of places the user frequently visited in the past. The collection unit can also optimize the collection method for app usage history based on data of apps the user frequently used in the past. This allows the collection unit to analyze the user's past behavioral data and select an optimal collection method.

[0031] The collection unit can filter the behavioral data based on the user's activity status and areas of interest when collecting the behavioral data. For example, the collection unit can collect only data related to the user's current activity. Furthermore, the collection unit can preferentially collect highly relevant data based on the user's areas of interest. Furthermore, the collection unit can filter unnecessary data taking the user's current activity status into consideration. As a result, highly relevant data can be collected by filtering data based on the user's activity status and areas of interest. The activity status includes, for example, current activities and applications currently in use. The areas of interest include, for example, past search history and social media following. Filtering methods include, for example, filtering based on specific keywords. For example, the collection unit can filter data based on keywords related to the user's current activity to collect only highly relevant data. Furthermore, the collection unit can filter data based on keywords related to the user's areas of interest to preferentially collect highly relevant data. As a result, the collection unit can filter the behavioral data based on the user's activity status and areas of interest when collecting the behavioral data.

[0032] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting behavioral data. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to the location. Furthermore, the collection unit can collect highly relevant data taking into account the distance from the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data related to the destination. This allows highly relevant data to be collected efficiently by taking the user's geographical location information into account. Geographical location information includes, for example, GPS data and Wi-Fi location information. Highly relevant data includes, for example, event information related to the current location. For example, when the user is in a specific location, the collection unit can prioritize collecting event information related to the location. Furthermore, the collection unit can collect highly relevant data taking into account the distance from the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data related to the destination. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information when collecting behavioral data.

[0033] The collection unit can analyze the user's social media activities and collect relevant data when collecting behavioral data. The collection unit can, for example, collect relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect optimal data. The collection unit can also preferentially collect relevant data based on the user's areas of interest on social media. This makes it possible to collect highly relevant data by analyzing social media activities. Social media activities include, for example, post content, follow relationships, and like history. The relevant data includes, for example, data related to topics mentioned on social media. The collection unit can, for example, collect relevant data based on the information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect optimal data. The collection unit can also preferentially collect relevant data based on the user's areas of interest on social media. This makes it possible to collect highly relevant data when collecting behavioral data.

[0034] The analysis unit can adjust the level of analysis detail based on the importance of the data during analysis. For example, the analysis unit can analyze data with high importance in detail and analyze data with low importance simply. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to data with high importance. As a result, efficient data analysis is possible by adjusting the level of analysis detail based on the importance of the data. The importance of the data includes, for example, the frequency of the data and the impact of the data. Adjusting the level of analysis detail includes, for example, the depth of the analysis according to the importance. For example, the analysis unit can analyze data with high importance in detail and analyze data with low importance simply. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to data with high importance. As a result, the analysis unit can adjust the level of analysis detail based on the importance of the data during analysis.

[0035] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to audio data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Data categories include, for example, text data, image data, and audio data. Analysis algorithms include, for example, clustering algorithms and regression analysis. For example, the analysis unit can apply a natural language processing algorithm to text data and an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to audio data. This allows the analysis unit to apply different analysis algorithms depending on the category of data during analysis.

[0036] The analysis unit can determine the priority of the analysis based on the time when the data was collected during analysis. For example, the analysis unit can prioritize analyzing the most recent data and postpone analyzing older data. The analysis unit can also adjust the level of detail of the analysis depending on the time when the data was collected. Furthermore, the analysis unit can analyze data that were collected recently together to improve efficiency. This enables efficient data analysis by determining the priority of the analysis based on the time when the data was collected. The time when the data was collected includes, for example, the date and time of collection or a specific event period. Determining the priority of the analysis includes, for example, setting the priority based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the most recent data and postpone analyzing older data. The analysis unit can also adjust the level of detail of the analysis depending on the time when the data was collected. Furthermore, the analysis unit can analyze data that were collected recently together to improve efficiency. This allows the analysis unit to determine the priority of the analysis based on the time when the data was collected during analysis.

[0037] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data and postpone analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. Furthermore, the analysis unit can analyze highly relevant data collectively to improve efficiency. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, for example, co-occurrence frequency and correlation. Adjusting the order of analysis includes, for example, setting the priority of analysis based on the relevance. For example, the analysis unit can analyze highly relevant data preferentially and postpone analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. Furthermore, the analysis unit can analyze highly relevant data collectively to improve efficiency. This allows the analysis unit to adjust the order of analysis based on the relevance of the data during analysis.

[0038] The providing unit can adjust the level of detail of the support based on the importance of the user's needs when providing support. For example, the providing unit can provide detailed support for needs of high importance and provide simplified support for needs of low importance. The providing unit can also determine the priority of the support based on the importance of the user's needs. Furthermore, the providing unit can apply multiple support techniques to needs of high importance. As a result, efficient support can be provided by adjusting the level of detail of the support based on the importance of the user's needs. The importance of needs includes, for example, user feedback and frequency of use. Adjusting the level of detail of the support includes, for example, changing the support content based on the importance. For example, the providing unit can provide detailed support for needs of high importance and provide simplified support for needs of low importance. The providing unit can also determine the priority of the support based on the importance of the user's needs. Furthermore, the providing unit can apply multiple support techniques to needs of high importance. As a result, the providing unit can adjust the level of detail of the support based on the importance of the user's needs when providing support.

[0039] The providing unit can apply different support algorithms depending on the user category when providing support. For example, the providing unit can apply an education support algorithm to students. The providing unit can also apply a work support algorithm to business people. The providing unit can also apply a lifestyle support algorithm to elderly people. This allows effective support to be provided by applying an appropriate support algorithm depending on the user category. User categories include, for example, age group, occupation, and interests. Support algorithms include, for example, recommendation algorithms and personalized algorithms. For example, the providing unit can apply an education support algorithm to students and a work support algorithm to business people. The providing unit can also apply a lifestyle support algorithm to elderly people. This allows the providing unit to apply different support algorithms depending on the user category when providing support.

[0040] The providing unit, when providing support, can determine the priority of support based on the user's activity period. For example, when the user is at work, the providing unit can prioritize support related to work. Furthermore, when the user is on vacation, the providing unit can prioritize support related to relaxation. Furthermore, when the user is studying, the providing unit can prioritize support related to educational support. In this way, by determining the priority of support based on the user's activity period, support can be provided at an appropriate time. The activity period includes, for example, a specific time period or a specific event period. Determining the priority includes, for example, setting a priority based on the activity period. For example, when the user is at work, the providing unit can prioritize support related to work. Furthermore, when the user is on vacation, the providing unit can prioritize support related to relaxation. Furthermore, when the user is studying, the providing unit can prioritize support related to educational support. In this way, the providing unit, when providing support, can determine the priority of support based on the user's activity period.

[0041] The providing unit can adjust the order of support based on the user's related data when providing support. The providing unit can, for example, determine an optimal support order based on the user's past support history. The providing unit can also adjust the order of support taking into account the user's current activity status. Furthermore, the providing unit can optimize the order of support according to the user's needs. As a result, efficient support can be provided by adjusting the order of support based on the user's related data. The related data includes, for example, the user's past behavioral data and current activity. Adjusting the order includes, for example, setting an order based on relevance. For example, the providing unit can determine the optimal support order based on the user's past support history. The providing unit can also adjust the order of support taking into account the user's current activity status. Furthermore, the providing unit can optimize the order of support according to the user's needs. As a result, the providing unit can adjust the order of support based on the user's related data when providing support.

[0042] The synchronization unit can adjust the synchronization detail level based on the importance of data between devices during data synchronization. For example, the synchronization unit can synchronize highly important data in detail and synchronize less important data simply. The synchronization unit can also determine the synchronization priority level based on the importance of the data. Furthermore, the synchronization unit can apply multiple synchronization methods to highly important data. As a result, efficient data synchronization is possible by adjusting the synchronization detail level based on the importance of data between devices. The importance of data includes, for example, the frequency of data and the impact of data. Adjustment of the synchronization detail level includes, for example, the synchronization depth based on the importance. For example, the synchronization unit can synchronize highly important data in detail and synchronize less important data simply. The synchronization unit can also determine the synchronization priority level based on the importance of data. Furthermore, the synchronization unit can apply multiple synchronization methods to highly important data. As a result, the synchronization unit can adjust the synchronization detail level based on the importance of data between devices during data synchronization.

[0043] The synchronization unit can apply different synchronization algorithms depending on the device category when synchronizing data. For example, the synchronization unit can apply a high-speed synchronization algorithm to a smartphone. The synchronization unit can also apply a detailed synchronization algorithm to a PC. The synchronization unit can also apply a power-saving synchronization algorithm to a smartwatch. This enables efficient data synchronization by applying an appropriate synchronization algorithm depending on the device category. Device categories include, for example, smartphones, tablets, and PCs. The synchronization algorithms include, for example, real-time synchronization and batch synchronization. For example, the synchronization unit can apply a high-speed synchronization algorithm to a smartphone and a detailed synchronization algorithm to a PC. The synchronization unit can also apply a power-saving synchronization algorithm to a smartwatch. This allows the synchronization unit to apply different synchronization algorithms depending on the device category when synchronizing data.

[0044] The synchronization unit can prioritize synchronizing highly relevant data by taking into account the geographical location information of the device when synchronizing data. For example, when a user is in a specific location, the synchronization unit can prioritize synchronizing data related to the location. Furthermore, the synchronization unit can prioritize synchronizing highly relevant data by taking into account the distance from the user's current location. Furthermore, when a user is moving, the synchronization unit can prioritize synchronizing data related to a destination. This allows highly relevant data to be efficiently synchronized by taking into account the geographical location information of the device. Geographical location information includes, for example, GPS data and Wi-Fi location information. Highly relevant data includes, for example, event information related to the current location. For example, when a user is in a specific location, the synchronization unit can prioritize synchronizing data related to the location. Furthermore, the synchronization unit can prioritize synchronizing highly relevant data by taking into account the distance from the user's current location. Furthermore, when a user is moving, the synchronization unit can prioritize synchronizing data related to a destination. This allows the synchronization unit to prioritize highly relevant data by taking into account the geographical location information of the device when synchronizing data.

[0045] The synchronization unit can analyze device usage status and synchronize related data when synchronizing data. For example, the synchronization unit can prioritize synchronization of data related to devices frequently used by the user. The synchronization unit can also determine synchronization priorities according to the device usage status. Furthermore, the synchronization unit can select an optimal synchronization method based on the frequency of device use. This allows highly relevant data to be efficiently synchronized by analyzing device usage status. Device usage status includes, for example, active applications and usage frequency. Related data includes, for example, data related to applications currently in use. For example, the synchronization unit can prioritize synchronization of data related to devices frequently used by the user. The synchronization unit can also determine synchronization priorities according to the device usage status. Furthermore, the synchronization unit can select an optimal synchronization method based on the frequency of device use. This allows the synchronization unit to analyze device usage status and synchronize related data when synchronizing data.

[0046] The adaptation unit can select a function based on the user's growth stage when switching functions. The adaptation unit can, for example, provide an educational support function to children. The adaptation unit can also provide a work support function to adults. The adaptation unit can also provide a lifestyle support function to elderly people. This allows effective support to be provided by selecting an optimal function based on the user's growth stage. Growth stages include, for example, learning progress and skill improvement. Selection of an optimal function includes, for example, selection of a function according to the growth stage. For example, the adaptation unit can provide an educational support function to children and a work support function to adults. The adaptation unit can also provide a lifestyle support function to elderly people. This allows the adaptation unit to select a function based on the user's growth stage when switching functions.

[0047] The adaptation unit can customize the function based on the user's current activity status when switching functions. For example, when the user is studying, the adaptation unit can provide an education support function. Furthermore, when the user is working, the adaptation unit can provide a work support function. Furthermore, when the user is on vacation, the adaptation unit can provide a relaxation support function. This allows more appropriate support to be provided by customizing the function based on the user's current activity status. The activity status includes, for example, a current activity and an application being used. Customizing the function includes, for example, changing the function according to the activity status. For example, when the user is studying, the adaptation unit can provide an education support function. Furthermore, when the user is working, the adaptation unit can provide a work support function. Furthermore, when the user is on vacation, the adaptation unit can provide a relaxation support function. This allows the adaptation unit to customize the function based on the user's current activity status when switching functions.

[0048] The adaptation unit can select a function based on the user's geographical location information when switching functions. For example, when the user is in a specific location, the adaptation unit can provide a function related to the location. Furthermore, the adaptation unit can provide an optimal function by taking into account the distance from the user's current location. Furthermore, when the user is moving, the adaptation unit can provide a function related to the destination. This allows highly relevant functions to be provided by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and Wi-Fi location information. Selection of the optimal function includes, for example, selection of a function based on the geographical location information. For example, when the user is in a specific location, the adaptation unit can provide a function related to the location. Furthermore, the adaptation unit can provide an optimal function by taking into account the distance from the user's current location. Furthermore, when the user is moving, the adaptation unit can provide a function related to the destination. This allows the adaptation unit to select a function based on the user's geographical location information when switching functions.

[0049] The adaptation unit can analyze the user's social media activity and provide a relevant function when switching functions. For example, the adaptation unit can provide a relevant function based on information shared by the user on social media. The adaptation unit can also analyze the user's social media activity patterns and provide an optimal function. Furthermore, the adaptation unit can prioritize providing a relevant function based on the user's areas of interest on social media. This makes it possible to provide highly relevant functions by analyzing social media activity. Social media activity includes, for example, post content, follow relationships, and like history. Related functions include, for example, functions related to topics mentioned on social media. For example, the adaptation unit can provide a relevant function based on the information shared by the user on social media. The adaptation unit can also analyze the user's social media activity patterns and provide an optimal function. Furthermore, the adaptation unit can prioritize providing a relevant function based on the user's areas of interest on social media. This makes it possible to analyze the user's social media activity and provide a highly relevant function when switching functions.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The Buddy AI system can further collect the user's health data and adjust the support content based on the user's health condition. For example, the collection unit can collect heart rate and sleep data using the smartwatch's sensors, and the analysis unit can analyze this data to understand the user's health condition. The provision unit can send a notification encouraging the user to rest if the user is tired, or recommend exercise if the user is energetic. Furthermore, the adaptation unit can provide functions to promote stress reduction and relaxation depending on the user's health condition. This allows the Buddy AI system to provide more appropriate support based on the user's health condition.

[0052] The collection unit can analyze the user's past behavioral data and adjust the accuracy of the collected data. For example, it can collect high-accuracy data for behaviors that the user frequently performed in the past, and collect low-accuracy data for behaviors that the user infrequently performed. The collection unit can also analyze the user's past behavioral patterns and adjust the accuracy of data collection for specific time periods. Furthermore, the collection unit can optimize the accuracy of data collection for specific activities from the user's past behavioral data. This allows the collection unit to adjust the accuracy of the data to be collected based on the past behavioral data, enabling efficient data collection.

[0053] When collecting behavioral data, the collection unit can adjust the frequency of data collection based on the user's activity status and areas of interest. For example, data related to the user's current activity can be collected frequently, and less relevant data can be collected less frequently. The collection unit can also preferentially collect highly relevant data at a higher frequency based on the user's areas of interest. Furthermore, the collection unit can reduce the frequency of collection of unnecessary data, taking into account the user's current activity status. This allows the collection unit to adjust the data collection frequency based on the user's activity status and areas of interest, thereby enabling efficient data collection.

[0054] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, data related to that location can be prioritized. The collection unit can also collect highly relevant data taking into account the distance from the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data related to the user's destination. This allows the collection unit to efficiently collect highly relevant data by taking into account the user's geographical location information.

[0055] When collecting behavioral data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect optimal data. Furthermore, the collection unit can preferentially collect related data based on the user's areas of interest on social media. This allows the collection unit to collect highly relevant data by analyzing social media activities.

[0056] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, data with high importance can be analyzed in detail, and data with low importance can be analyzed simply. The analysis unit can also determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to data with high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, enabling efficient data analysis.

[0057] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to text data. The analysis unit can also apply an image recognition algorithm to image data. Furthermore, the analysis unit can apply a voice recognition algorithm to voice data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category, enabling highly accurate analysis.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects user behavior data. User behavior data includes website browsing history, app usage history, location information, etc. The collection unit acquires data from devices such as smartphones, PCs, smartwatches, and IoT home appliances. For example, it can acquire location information using a smartphone sensor and collect app usage history by analyzing PC log files. It can also collect user activity data using a smartwatch sensor. For example, it can measure the user's number of steps and amount of exercise using the smartwatch's acceleration sensor and collect this as data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and understand the user's behavioral patterns and needs. For example, it uses machine learning algorithms to cluster user behavioral data and identify user behavioral patterns. It also uses natural language processing technology to analyze the user's text data and determine the user's needs. For example, it can analyze the user's social media posts to understand what interests the user. Step 3: The provision unit provides support tailored to the user's needs based on the analysis results obtained by the analysis unit. The provision unit provides support in the form of notifications, advice, reminders, etc. For example, it can automatically collect and notify the user of materials needed for work. It can also remind the user of tasks that they tend to forget. For example, it can analyze the user's calendar and remind them of important appointments. Step 4: The synchronization unit synchronizes data across multiple devices. For example, data collected on a smartphone can be synchronized with a PC or smartwatch, allowing users to receive consistent support regardless of which device they use. Data can also be synchronized using cloud storage, allowing users to access the data from any device. Step 5: The adaptation unit switches between educational support, work support, and other functions depending on the user's development. For example, it can provide educational support functions when the user is a child and work support functions when the user becomes an adult. It can automatically switch between appropriate functions based on the user's age and behavioral data.

[0060] (Example 2) The Buddy AI system according to an embodiment of the present invention is a dedicated support system for users. This Buddy AI system supports and grows alongside users across networked devices, such as smartphones, PCs, smartwatches, and IoT appliances. Specifically, the Buddy AI collects and analyzes user behavioral data. Based on the analysis results, it provides appropriate support tailored to the user's needs. For example, as a child, the Buddy AI acts as a teacher, reading textbooks and manuals. As an adult, the Buddy AI gathers necessary materials for work and points out overlooked points in idea development. The Buddy AI can also synchronize data across multiple devices and switch functions as the user grows. This allows the Buddy AI to support users in all aspects of their lives and function as the coveted personal partner. For example, the Buddy AI collects data from devices such as smartphones, PCs, smartwatches, and IoT appliances to collect user behavioral data. The Buddy AI then analyzes the collected data to understand the user's behavioral patterns and needs. For example, the Buddy AI analyzes the user's activities and the time of day, and provides appropriate support based on the results. Furthermore, the Buddy AI can switch functions as the user grows. For example, Buddy AI can provide educational support functions for children and work support functions for adults. Buddy AI can also synchronize data across multiple devices depending on the user's needs. This allows users to receive consistent support regardless of which device they are using. For example, Buddy AI can synchronize data collected on a smartphone with a PC or smartwatch, allowing users to receive the same support regardless of which device they are using. This allows Buddy AI to support users in all aspects of their lives and function as the personal partner everyone longs for. This allows the Buddy AI system to collect and analyze user behavioral data, provide support, synchronize data, and switch functions.

[0061] The buddy AI system according to the embodiment includes a collection unit, an analysis unit, a provision unit, a synchronization unit, and an adaptation unit. The collection unit collects user behavioral data. Examples of the user behavioral data include, but are not limited to, website browsing history, app usage history, and location information. The collection unit acquires data from devices such as smartphones, PCs, smartwatches, and IoT home appliances. For example, the collection unit can acquire location information using a smartphone sensor and collect app usage history by analyzing PC log files. The collection unit can also collect user activity data using a smartwatch sensor. For example, the collection unit can measure the user's steps and exercise volume using the smartwatch's acceleration sensor and collect this data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data using AI to understand the user's behavioral patterns and needs. For example, the analysis unit can cluster the user's behavioral data using a machine learning algorithm to identify the user's behavioral patterns. The analysis unit can also analyze the user's text data using natural language processing technology to determine the user's needs. For example, the analysis unit can analyze a user's social media posts to understand what interests the user. The provision unit provides support tailored to the user's needs based on the analysis results obtained by the analysis unit. The provision unit can provide support in the form of notifications, advice, reminders, etc. For example, the provision unit can automatically collect and notify the user of materials needed for work. The provision unit can also remind the user of tasks that they tend to forget. For example, the provision unit can analyze the user's calendar and remind the user of important appointments. The synchronization unit synchronizes data between multiple devices. For example, the synchronization unit can synchronize data collected on a smartphone with a PC or smartwatch, allowing the user to receive consistent support regardless of which device they are using. For example, the synchronization unit can synchronize data using cloud storage, allowing the user to access the data from any device. The adaptation unit can switch functions, such as educational support or work support, according to the user's growth.For example, the adaptation unit can provide educational support functions when the user is a child and work support functions when the user becomes an adult. For example, the adaptation unit can automatically switch between appropriate functions based on the user's age and behavioral data. This enables the buddy AI system according to the embodiment to collect and analyze user behavioral data, provide support, synchronize data, and switch functions.

[0062] The collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can increase the collection timing to acquire detailed behavioral data. Furthermore, if the user is concentrating, the collection unit can adjust the collection timing to avoid interrupting the user's work. This enables more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit can analyze the user's facial expression data captured with a camera to estimate the user's emotions. Furthermore, the collection unit can analyze the user's voice data recorded with a microphone to estimate the user's emotions. Furthermore, the collection unit can analyze the user's text data to estimate the user's emotions. For example, the collection unit can analyze the user's social media posts to understand the user's emotions. This allows the collection unit to adjust the timing of behavioral data collection based on the user's emotions.

[0063] The collection unit can analyze the user's past behavioral data and select a collection method. The collection unit can optimize the collection method based on, for example, behaviors the user frequently performed in the past. The collection unit can also analyze the user's past behavioral patterns and select an efficient collection method. Furthermore, the collection unit can suggest an optimal collection method for a specific time period based on the user's past behavioral data. This enables efficient data collection by selecting an optimal collection method based on the past behavioral data. Past behavioral data includes, for example, data from the past year or data related to specific activities. The selection of a collection method includes, for example, selecting an optimal collection means depending on the type of data. For example, the collection unit can optimize the collection method for location information based on data of places the user frequently visited in the past. The collection unit can also optimize the collection method for app usage history based on data of apps the user frequently used in the past. This allows the collection unit to analyze the user's past behavioral data and select an optimal collection method.

[0064] The collection unit can filter the behavioral data based on the user's activity status and areas of interest when collecting the behavioral data. For example, the collection unit can collect only data related to the user's current activity. Furthermore, the collection unit can preferentially collect highly relevant data based on the user's areas of interest. Furthermore, the collection unit can filter unnecessary data taking the user's current activity status into consideration. As a result, highly relevant data can be collected by filtering data based on the user's activity status and areas of interest. The activity status includes, for example, current activities and applications currently in use. The areas of interest include, for example, past search history and social media following. Filtering methods include, for example, filtering based on specific keywords. For example, the collection unit can filter data based on keywords related to the user's current activity to collect only highly relevant data. Furthermore, the collection unit can filter data based on keywords related to the user's areas of interest to preferentially collect highly relevant data. As a result, the collection unit can filter the behavioral data based on the user's activity status and areas of interest when collecting the behavioral data.

[0065] The collection unit can estimate the user's emotions and determine the priority of behavioral data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data related to relaxation. Furthermore, if the user is concentrating, the collection unit can prioritize collecting data related to improving concentration. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit can analyze the user's facial expression data captured with a camera to estimate the user's emotions. Furthermore, the collection unit can analyze the user's voice data recorded with a microphone to estimate the user's emotions. Furthermore, the collection unit can analyze the user's text data to estimate the user's emotions. For example, the collection unit can analyze the user's social media posts to understand the user's emotions. This allows the collection unit to determine the priority of behavioral data to be collected based on the user's emotions.

[0066] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting behavioral data. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to the location. Furthermore, the collection unit can collect highly relevant data taking into account the distance from the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data related to the destination. This allows highly relevant data to be collected efficiently by taking the user's geographical location information into account. Geographical location information includes, for example, GPS data and Wi-Fi location information. Highly relevant data includes, for example, event information related to the current location. For example, when the user is in a specific location, the collection unit can prioritize collecting event information related to the location. Furthermore, the collection unit can collect highly relevant data taking into account the distance from the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data related to the destination. This allows the collection unit to prioritize collecting highly relevant data based on the user's geographical location information when collecting behavioral data.

[0067] The collection unit can analyze the user's social media activities and collect relevant data when collecting behavioral data. The collection unit can, for example, collect relevant data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect optimal data. The collection unit can also preferentially collect relevant data based on the user's areas of interest on social media. This makes it possible to collect highly relevant data by analyzing social media activities. Social media activities include, for example, post content, follow relationships, and like history. The relevant data includes, for example, data related to topics mentioned on social media. The collection unit can, for example, collect relevant data based on the information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect optimal data. The collection unit can also preferentially collect relevant data based on the user's areas of interest on social media. This makes it possible to collect highly relevant data when collecting behavioral data.

[0068] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analysis of data related to stress reduction. Furthermore, if the user is relaxed, the analysis unit can analyze data related to relaxation in detail. Furthermore, if the user is concentrating, the analysis unit can focus on analyzing data related to improving concentration. This allows for more appropriate analysis by adjusting the data analysis method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the analysis unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the analysis unit can analyze text data of the user to estimate the user's emotions. For example, the analysis unit can analyze the user's social media posts to understand the user's emotions. This allows the analysis unit to adjust the data analysis method based on the user's emotions.

[0069] The analysis unit can adjust the level of analysis detail based on the importance of the data during analysis. For example, the analysis unit can analyze data with high importance in detail and analyze data with low importance simply. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to data with high importance. As a result, efficient data analysis is possible by adjusting the level of analysis detail based on the importance of the data. The importance of the data includes, for example, the frequency of the data and the impact of the data. Adjusting the level of analysis detail includes, for example, the depth of the analysis according to the importance. For example, the analysis unit can analyze data with high importance in detail and analyze data with low importance simply. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to data with high importance. As a result, the analysis unit can adjust the level of analysis detail based on the importance of the data during analysis.

[0070] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to audio data. This enables highly accurate analysis by applying an appropriate analysis algorithm depending on the category of data. Data categories include, for example, text data, image data, and audio data. Analysis algorithms include, for example, clustering algorithms and regression analysis. For example, the analysis unit can apply a natural language processing algorithm to text data and an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to audio data. This allows the analysis unit to apply different analysis algorithms depending on the category of data during analysis.

[0071] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is concentrating, the analysis unit can provide a display method that focuses on the main points. This allows for highly visible display by adjusting the display method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the analysis unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the analysis unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the analysis unit can analyze text data of the user to estimate the user's emotions. For example, the analysis unit can analyze the user's social media posts to understand the user's emotions. This allows the analysis unit to adjust the display method of the analysis results based on the user's emotions.

[0072] The analysis unit can determine the priority of the analysis based on the time when the data was collected during analysis. For example, the analysis unit can prioritize analyzing the most recent data and postpone analyzing older data. The analysis unit can also adjust the level of detail of the analysis depending on the time when the data was collected. Furthermore, the analysis unit can analyze data that were collected recently together to improve efficiency. This enables efficient data analysis by determining the priority of the analysis based on the time when the data was collected. The time when the data was collected includes, for example, the date and time of collection or a specific event period. Determining the priority of the analysis includes, for example, setting the priority based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the most recent data and postpone analyzing older data. The analysis unit can also adjust the level of detail of the analysis depending on the time when the data was collected. Furthermore, the analysis unit can analyze data that were collected recently together to improve efficiency. This allows the analysis unit to determine the priority of the analysis based on the time when the data was collected during analysis.

[0073] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data and postpone analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. Furthermore, the analysis unit can analyze highly relevant data collectively to improve efficiency. This enables efficient data analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, for example, co-occurrence frequency and correlation. Adjusting the order of analysis includes, for example, setting the priority of analysis based on the relevance. For example, the analysis unit can analyze highly relevant data preferentially and postpone analysis of less relevant data. The analysis unit can also adjust the level of detail of the analysis according to the relevance of the data. Furthermore, the analysis unit can analyze highly relevant data collectively to improve efficiency. This allows the analysis unit to adjust the order of analysis based on the relevance of the data during analysis.

[0074] The providing unit can estimate the user's emotions and adjust the way support is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide support using gentle language. Furthermore, if the user is relaxed, the providing unit can provide support that includes detailed explanations. Furthermore, if the user is concentrating, the providing unit can provide concise, to-the-point support. This allows for more appropriate support to be provided by adjusting the way support is expressed based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the providing unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the providing unit can analyze text data of the user to estimate the user's emotions. For example, the providing unit can analyze the user's social media posts to understand the user's emotions. This allows the providing unit to adjust the way support is expressed based on the user's emotions.

[0075] The providing unit can adjust the level of detail of the support based on the importance of the user's needs when providing support. For example, the providing unit can provide detailed support for needs of high importance and provide simplified support for needs of low importance. The providing unit can also determine the priority of the support based on the importance of the user's needs. Furthermore, the providing unit can apply multiple support techniques to needs of high importance. As a result, efficient support can be provided by adjusting the level of detail of the support based on the importance of the user's needs. The importance of needs includes, for example, user feedback and frequency of use. Adjusting the level of detail of the support includes, for example, changing the support content based on the importance. For example, the providing unit can provide detailed support for needs of high importance and provide simplified support for needs of low importance. The providing unit can also determine the priority of the support based on the importance of the user's needs. Furthermore, the providing unit can apply multiple support techniques to needs of high importance. As a result, the providing unit can adjust the level of detail of the support based on the importance of the user's needs when providing support.

[0076] The providing unit can apply different support algorithms depending on the user category when providing support. For example, the providing unit can apply an education support algorithm to students. The providing unit can also apply a work support algorithm to business people. The providing unit can also apply a lifestyle support algorithm to elderly people. This allows effective support to be provided by applying an appropriate support algorithm depending on the user category. User categories include, for example, age group, occupation, and interests. Support algorithms include, for example, recommendation algorithms and personalized algorithms. For example, the providing unit can apply an education support algorithm to students and a work support algorithm to business people. The providing unit can also apply a lifestyle support algorithm to elderly people. This allows the providing unit to apply different support algorithms depending on the user category when providing support.

[0077] The providing unit can estimate the user's emotions and determine the priority of support based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize support related to stress reduction. Furthermore, if the user is relaxed, the providing unit can prioritize support related to relaxation. Furthermore, if the user is concentrating, the providing unit can prioritize support related to improving concentration. This allows important support to be provided preferentially by determining the priority of support according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the providing unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the providing unit can analyze text data of the user to estimate the user's emotions. For example, the providing unit can analyze the user's social media posts to understand the user's emotions. This allows the providing unit to determine the priority of support based on the user's emotions.

[0078] The providing unit, when providing support, can determine the priority of support based on the user's activity period. For example, when the user is at work, the providing unit can prioritize support related to work. Furthermore, when the user is on vacation, the providing unit can prioritize support related to relaxation. Furthermore, when the user is studying, the providing unit can prioritize support related to educational support. In this way, by determining the priority of support based on the user's activity period, support can be provided at an appropriate time. The activity period includes, for example, a specific time period or a specific event period. Determining the priority includes, for example, setting a priority based on the activity period. For example, when the user is at work, the providing unit can prioritize support related to work. Furthermore, when the user is on vacation, the providing unit can prioritize support related to relaxation. Furthermore, when the user is studying, the providing unit can prioritize support related to educational support. In this way, the providing unit, when providing support, can determine the priority of support based on the user's activity period.

[0079] The providing unit can adjust the order of support based on the user's related data when providing support. The providing unit can, for example, determine an optimal support order based on the user's past support history. The providing unit can also adjust the order of support taking into account the user's current activity status. Furthermore, the providing unit can optimize the order of support according to the user's needs. As a result, efficient support can be provided by adjusting the order of support based on the user's related data. The related data includes, for example, the user's past behavioral data and current activity. Adjusting the order includes, for example, setting an order based on relevance. For example, the providing unit can determine the optimal support order based on the user's past support history. The providing unit can also adjust the order of support taking into account the user's current activity status. Furthermore, the providing unit can optimize the order of support according to the user's needs. As a result, the providing unit can adjust the order of support based on the user's related data when providing support.

[0080] The synchronization unit can estimate a user's emotions and adjust the timing of data synchronization based on the estimated user emotions. For example, if the user is feeling stressed, the synchronization unit can reduce the frequency of data synchronization to reduce the user's burden. Furthermore, if the user is relaxed, the synchronization unit can increase the frequency of data synchronization to acquire more detailed data. Furthermore, if the user is concentrating, the synchronization unit can adjust the timing of data synchronization so as not to interfere with the user's work. This enables efficient data synchronization by adjusting the timing of data synchronization according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the synchronization unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the synchronization unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the synchronization unit can analyze the user's text data to estimate the user's emotions. For example, the synchronization unit can analyze the user's social media posts to understand the user's emotions. This allows the synchronization unit to adjust the timing of data synchronization based on the user's emotions.

[0081] The synchronization unit can adjust the synchronization detail level based on the importance of data between devices during data synchronization. For example, the synchronization unit can synchronize highly important data in detail and synchronize less important data simply. The synchronization unit can also determine the synchronization priority level based on the importance of the data. Furthermore, the synchronization unit can apply multiple synchronization methods to highly important data. As a result, efficient data synchronization is possible by adjusting the synchronization detail level based on the importance of data between devices. The importance of data includes, for example, the frequency of data and the impact of data. Adjustment of the synchronization detail level includes, for example, the synchronization depth based on the importance. For example, the synchronization unit can synchronize highly important data in detail and synchronize less important data simply. The synchronization unit can also determine the synchronization priority level based on the importance of data. Furthermore, the synchronization unit can apply multiple synchronization methods to highly important data. As a result, the synchronization unit can adjust the synchronization detail level based on the importance of data between devices during data synchronization.

[0082] The synchronization unit can apply different synchronization algorithms depending on the device category when synchronizing data. For example, the synchronization unit can apply a high-speed synchronization algorithm to a smartphone. The synchronization unit can also apply a detailed synchronization algorithm to a PC. The synchronization unit can also apply a power-saving synchronization algorithm to a smartwatch. This enables efficient data synchronization by applying an appropriate synchronization algorithm depending on the device category. Device categories include, for example, smartphones, tablets, and PCs. The synchronization algorithms include, for example, real-time synchronization and batch synchronization. For example, the synchronization unit can apply a high-speed synchronization algorithm to a smartphone and a detailed synchronization algorithm to a PC. The synchronization unit can also apply a power-saving synchronization algorithm to a smartwatch. This allows the synchronization unit to apply different synchronization algorithms depending on the device category when synchronizing data.

[0083] The synchronization unit can estimate a user's emotions and determine the priority of data to be synchronized based on the estimated user's emotions. For example, if the user is feeling stressed, the synchronization unit can prioritize synchronization of data related to stress reduction. Furthermore, if the user is relaxed, the synchronization unit can prioritize synchronization of data related to relaxation. Furthermore, if the user is concentrating, the synchronization unit can prioritize synchronization of data related to improving concentration. In this way, by determining the priority of data according to the user's emotions, important data can be synchronized preferentially. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the synchronization unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the synchronization unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the synchronization unit can analyze the user's text data to estimate the user's emotions. For example, the synchronization unit can analyze the user's social media posts to understand the user's emotions. In this way, the synchronization unit can determine the priority of data to be synchronized based on the user's emotions.

[0084] The synchronization unit can prioritize synchronizing highly relevant data by taking into account the geographical location information of the device when synchronizing data. For example, when a user is in a specific location, the synchronization unit can prioritize synchronizing data related to the location. Furthermore, the synchronization unit can prioritize synchronizing highly relevant data by taking into account the distance from the user's current location. Furthermore, when a user is moving, the synchronization unit can prioritize synchronizing data related to a destination. This allows highly relevant data to be efficiently synchronized by taking into account the geographical location information of the device. Geographical location information includes, for example, GPS data and Wi-Fi location information. Highly relevant data includes, for example, event information related to the current location. For example, when a user is in a specific location, the synchronization unit can prioritize synchronizing data related to the location. Furthermore, the synchronization unit can prioritize synchronizing highly relevant data by taking into account the distance from the user's current location. Furthermore, when a user is moving, the synchronization unit can prioritize synchronizing data related to a destination. This allows the synchronization unit to prioritize highly relevant data by taking into account the geographical location information of the device when synchronizing data.

[0085] The synchronization unit can analyze device usage status and synchronize related data when synchronizing data. For example, the synchronization unit can prioritize synchronization of data related to devices frequently used by the user. The synchronization unit can also determine synchronization priorities according to the device usage status. Furthermore, the synchronization unit can select an optimal synchronization method based on the frequency of device use. This allows highly relevant data to be efficiently synchronized by analyzing device usage status. Device usage status includes, for example, active applications and usage frequency. Related data includes, for example, data related to applications currently in use. For example, the synchronization unit can prioritize synchronization of data related to devices frequently used by the user. The synchronization unit can also determine synchronization priorities according to the device usage status. Furthermore, the synchronization unit can select an optimal synchronization method based on the frequency of device use. This allows the synchronization unit to analyze device usage status and synchronize related data when synchronizing data.

[0086] The adaptation unit can estimate the user's emotions and adjust the function switching method based on the estimated user's emotions. For example, if the user is feeling stressed, the adaptation unit can prioritize providing functions related to stress reduction. Furthermore, if the user is relaxed, the adaptation unit can prioritize providing functions related to relaxation. Furthermore, if the user is concentrating, the adaptation unit can prioritize providing functions related to improving concentration. This allows for more appropriate function provision by adjusting the function switching method according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the adaptation unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the adaptation unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the adaptation unit can analyze text data of the user to estimate the user's emotions. For example, the adaptation unit can analyze the user's social media posts to understand the user's emotions. This allows the adaptation unit to adjust the function switching method based on the user's emotions.

[0087] The adaptation unit can select a function based on the user's growth stage when switching functions. The adaptation unit can, for example, provide an educational support function to children. The adaptation unit can also provide a work support function to adults. The adaptation unit can also provide a lifestyle support function to elderly people. This allows effective support to be provided by selecting an optimal function based on the user's growth stage. Growth stages include, for example, learning progress and skill improvement. Selection of an optimal function includes, for example, selection of a function according to the growth stage. For example, the adaptation unit can provide an educational support function to children and a work support function to adults. The adaptation unit can also provide a lifestyle support function to elderly people. This allows the adaptation unit to select a function based on the user's growth stage when switching functions.

[0088] The adaptation unit can customize the function based on the user's current activity status when switching functions. For example, when the user is studying, the adaptation unit can provide an education support function. Furthermore, when the user is working, the adaptation unit can provide a work support function. Furthermore, when the user is on vacation, the adaptation unit can provide a relaxation support function. This allows more appropriate support to be provided by customizing the function based on the user's current activity status. The activity status includes, for example, a current activity and an application being used. Customizing the function includes, for example, changing the function according to the activity status. For example, when the user is studying, the adaptation unit can provide an education support function. Furthermore, when the user is working, the adaptation unit can provide a work support function. Furthermore, when the user is on vacation, the adaptation unit can provide a relaxation support function. This allows the adaptation unit to customize the function based on the user's current activity status when switching functions.

[0089] The adaptation unit can estimate the user's emotions and determine the priority of functions based on the estimated user's emotions. For example, if the user is feeling stressed, the adaptation unit can prioritize providing functions related to stress reduction. Furthermore, if the user is relaxed, the adaptation unit can prioritize providing functions related to relaxation. Furthermore, if the user is concentrating, the adaptation unit can prioritize providing functions related to improving concentration. In this way, by determining the priority of functions according to the user's emotions, important functions can be provided preferentially. Emotion estimation is performed using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the adaptation unit can analyze facial expression data of the user captured with a camera to estimate the user's emotions. Furthermore, the adaptation unit can analyze audio data of the user recorded with a microphone to estimate the user's emotions. Furthermore, the adaptation unit can analyze text data of the user to estimate the user's emotions. For example, the adaptation unit can analyze the user's social media posts to understand the user's emotions. In this way, the adaptation unit can determine the priority of functions based on the user's emotions.

[0090] The adaptation unit can select a function based on the user's geographical location information when switching functions. For example, when the user is in a specific location, the adaptation unit can provide a function related to the location. Furthermore, the adaptation unit can provide an optimal function by taking into account the distance from the user's current location. Furthermore, when the user is moving, the adaptation unit can provide a function related to the destination. This allows highly relevant functions to be provided by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and Wi-Fi location information. Selection of the optimal function includes, for example, selection of a function based on the geographical location information. For example, when the user is in a specific location, the adaptation unit can provide a function related to the location. Furthermore, the adaptation unit can provide an optimal function by taking into account the distance from the user's current location. Furthermore, when the user is moving, the adaptation unit can provide a function related to the destination. This allows the adaptation unit to select a function based on the user's geographical location information when switching functions.

[0091] The adaptation unit can analyze the user's social media activity and provide a relevant function when switching functions. For example, the adaptation unit can provide a relevant function based on information shared by the user on social media. The adaptation unit can also analyze the user's social media activity patterns and provide an optimal function. Furthermore, the adaptation unit can prioritize providing a relevant function based on the user's areas of interest on social media. This makes it possible to provide highly relevant functions by analyzing social media activity. Social media activity includes, for example, post content, follow relationships, and like history. Related functions include, for example, functions related to topics mentioned on social media. For example, the adaptation unit can provide a relevant function based on the information shared by the user on social media. The adaptation unit can also analyze the user's social media activity patterns and provide an optimal function. Furthermore, the adaptation unit can prioritize providing a relevant function based on the user's areas of interest on social media. This makes it possible to analyze the user's social media activity and provide a highly relevant function when switching functions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, synchronization unit, and adaptation unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit acquires user behavior data using a sensor of the smart device 14 and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The provision unit provides appropriate support based on the analysis results by, for example, the specific processing unit 290 of the data processing device 12, and the synchronization unit synchronizes data between the smart device 14 and the data processing device 12. The adaptation unit switches functions according to the user's growth by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, synchronization unit, and adaptation unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit acquires user behavior data using a sensor of the smart glasses 214 and analyzes the collected data by a specific processing unit 290 of the data processing device 12. The provision unit provides appropriate support based on the analysis results by, for example, the specific processing unit 290 of the data processing device 12, and the synchronization unit synchronizes data between the smart glasses 214 and the data processing device 12. The adaptation unit switches functions according to the user's growth by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, synchronization unit, and adaptation unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit acquires behavioral data of the user using a sensor of the headset type terminal 314, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The provision unit provides appropriate support based on the analysis results by the specific processing unit 290 of the data processing device 12, for example, and the synchronization unit synchronizes data between the headset type terminal 314 and the data processing device 12. The adaptation unit switches functions in accordance with the user's growth by the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, provision unit, synchronization unit, and adaptation unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the collection unit acquires behavioral data of the user using a sensor of the robot 414, and analyzes the collected data by the specific processing unit 290 of the data processing device 12. The provision unit provides appropriate support based on the analysis results by the specific processing unit 290 of the data processing device 12, for example, and the synchronization unit synchronizes data between the robot 414 and the data processing device 12. The adaptation unit switches functions according to the user's growth by the specific processing unit 290 of the data processing device 12, for example.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The Buddy AI system can further collect the user's health data and adjust the support content based on the user's health condition. For example, the collection unit can collect heart rate and sleep data using the smartwatch's sensors, and the analysis unit can analyze this data to understand the user's health condition. The provision unit can send a notification encouraging the user to rest if the user is tired, or recommend exercise if the user is energetic. Furthermore, the adaptation unit can provide functions to promote stress reduction and relaxation depending on the user's health condition. This allows the Buddy AI system to provide more appropriate support based on the user's health condition.

[0094] The collection unit can estimate the user's emotions and select the type of data to collect based on the estimated user emotions. For example, if the user is feeling stressed, data for identifying the cause of the stress can be collected preferentially. Also, if the user is relaxed, data for identifying the factors that cause relaxation can be collected. Furthermore, if the user is concentrating, data for maintaining concentration can be collected. This allows the collection unit to select the type of data to collect based on the user's emotions, thereby enabling more appropriate data collection.

[0095] The collection unit can analyze the user's past behavioral data and adjust the accuracy of the collected data. For example, it can collect high-accuracy data for behaviors that the user frequently performed in the past, and collect low-accuracy data for behaviors that the user infrequently performed. The collection unit can also analyze the user's past behavioral patterns and adjust the accuracy of data collection for specific time periods. Furthermore, the collection unit can optimize the accuracy of data collection for specific activities from the user's past behavioral data. This allows the collection unit to adjust the accuracy of the data to be collected based on the past behavioral data, enabling efficient data collection.

[0096] When collecting behavioral data, the collection unit can adjust the frequency of data collection based on the user's activity status and areas of interest. For example, data related to the user's current activity can be collected frequently, and less relevant data can be collected less frequently. The collection unit can also preferentially collect highly relevant data at a higher frequency based on the user's areas of interest. Furthermore, the collection unit can reduce the frequency of collection of unnecessary data, taking into account the user's current activity status. This allows the collection unit to adjust the data collection frequency based on the user's activity status and areas of interest, thereby enabling efficient data collection.

[0097] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be collected with priority. If the user is relaxed, data related to relaxation can be collected with priority. If the user is concentrating, data related to improving concentration can be collected with priority. In this way, the collection unit can prioritize collecting important data by determining the priority of data according to the user's emotions.

[0098] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, data related to that location can be prioritized. The collection unit can also collect highly relevant data taking into account the distance from the user's current location. Furthermore, when the user is moving, the collection unit can prioritize collecting data related to the user's destination. This allows the collection unit to efficiently collect highly relevant data by taking into account the user's geographical location information.

[0099] When collecting behavioral data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and collect optimal data. Furthermore, the collection unit can preferentially collect related data based on the user's areas of interest on social media. This allows the collection unit to collect highly relevant data by analyzing social media activities.

[0100] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be analyzed preferentially. If the user is relaxed, data related to relaxation can be analyzed in detail. Furthermore, if the user is concentrating, data related to improving concentration can be analyzed with a focus. This allows the analysis unit to adjust the data analysis method according to the user's emotions, enabling more appropriate analysis.

[0101] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, data with high importance can be analyzed in detail, and data with low importance can be analyzed simply. The analysis unit can also determine the priority of the analysis based on the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to data with high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, enabling efficient data analysis.

[0102] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a natural language processing algorithm can be applied to text data. The analysis unit can also apply an image recognition algorithm to image data. Furthermore, the analysis unit can apply a voice recognition algorithm to voice data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category, enabling highly accurate analysis.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection unit collects user behavior data. User behavior data includes website browsing history, app usage history, location information, etc. The collection unit acquires data from devices such as smartphones, PCs, smartwatches, and IoT home appliances. For example, it can acquire location information using a smartphone sensor and collect app usage history by analyzing PC log files. It can also collect user activity data using a smartwatch sensor. For example, it can measure the user's number of steps and amount of exercise using the smartwatch's acceleration sensor and collect this as data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses AI to analyze the data and understand the user's behavioral patterns and needs. For example, it uses machine learning algorithms to cluster user behavioral data and identify user behavioral patterns. It also uses natural language processing technology to analyze the user's text data and determine the user's needs. For example, it can analyze the user's social media posts to understand what interests the user. Step 3: The provision unit provides support tailored to the user's needs based on the analysis results obtained by the analysis unit. The provision unit provides support in the form of notifications, advice, reminders, etc. For example, it can automatically collect and notify the user of materials needed for work. It can also remind the user of tasks that they tend to forget. For example, it can analyze the user's calendar and remind them of important appointments. Step 4: The synchronization unit synchronizes data across multiple devices. For example, data collected on a smartphone can be synchronized with a PC or smartwatch, allowing users to receive consistent support regardless of which device they use. Data can also be synchronized using cloud storage, allowing users to access the data from any device. Step 5: The adaptation unit switches between educational support, work support, and other functions depending on the user's development. For example, it can provide educational support functions when the user is a child and work support functions when the user becomes an adult. It can automatically switch between appropriate functions based on the user's age and behavioral data.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 data collected by the collection unit; a providing unit that provides support based on the analysis results obtained by the analysis unit; a synchronization unit that synchronizes data between devices; an adaptation unit that switches functions based on the user's growth; A system characterized by:

2. 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.

3. The collecting unit Analyze users' past behavioral data and select collection methods 2. The system of claim 1.

4. The collecting unit When collecting behavioral data, filter it based on user activity and interests.

2. The system of claim 1.

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

2. The system of claim 1.

6. The collecting unit When collecting behavioral data, prioritize collection of relevant data based on the user's geographic location.

2. The system of claim 1.

7. The collecting unit When collecting behavioral data, analyze your social media activity and collect related data.

2. The system of claim 1.

8. The analysis unit Inferring user sentiment and adjusting data analysis methods based on the estimated user sentiment 2. The system of claim 1.

9. The analysis unit During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1.

10. The analysis unit During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1.

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