System

Abstract

An object of a system according to an embodiment is to provide appropriate support and services based on the needs and preferences of a user.SOLUTION: A system includes a collection unit, an analysis unit, and a provision unit. The collection unit collects needs and preferences of users. The analysis unit analyzes the data collected by the collection unit. The providing unit provides an appropriate support or service to the user based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide appropriate support and services based on users' needs and preferences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide appropriate support and services based on the needs and preferences of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user needs and preferences. The analysis unit analyzes the data collected by the collection unit. The provision unit provides appropriate support and services to the user based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate support and services based on the needs and preferences of the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A personalized AI system according to an embodiment of the present invention provides support and services tailored to a user's needs and preferences. The personalized AI system collects the user's needs and preferences, analyzes them using AI, and provides optimal support and services to the user based on the analysis results. For example, the personalized AI system manages the user's schedule, health, and hobby support. For example, the personalized AI system collects data from the user's daily devices and applications. This data includes smartphone usage history, social media posts, and email content. The personalized AI system then analyzes the collected data. The AI ​​identifies the user's needs and preferences based on the collected data. For example, the AI ​​analyzes the user's frequently visited places, frequently purchased products, and topics of interest. The personalized AI system then provides optimal support and services to the user based on the analysis results. For example, to support the user's schedule management, the AI ​​automatically adjusts schedules and sets reminders. To support health management, the AI ​​manages the user's diet and exercise records and provides advice for living a healthy lifestyle. Furthermore, to support hobbies, the AI ​​provides information on topics of interest to the user and introduces related events. This allows the personal AI system to support the user's daily life and work, improve user satisfaction, and realize a more comfortable life. This allows the personal AI system to provide support and services tailored to the user's needs and preferences. For example, it can efficiently manage the user's schedule, health management, and hobby support. This allows the user's satisfaction to improve and realize a more comfortable life.

[0029] A personal AI system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user needs and preferences. The collection unit collects data from, for example, devices and applications used daily by the user. For example, the data includes smartphone usage history, social media posts, and email content. The collection unit collects smartphone usage history to understand the user's behavioral patterns. The collection unit can also collect social media posts to identify the user's interests. The collection unit can also collect email content to understand the user's interests. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, identify the user's needs and preferences based on the collected data. For example, the analysis unit can analyze places frequently visited by the user to identify the user's behavioral patterns. The analysis unit can also analyze products frequently purchased by the user to identify the user's purchasing tendencies. The analysis unit can also analyze topics of interest to the user to identify the user's interests. The provision unit provides optimal support and services to the user based on the analysis results obtained by the analysis unit. The providing unit, for example, allows the AI ​​to automatically adjust schedules to support the user's schedule management. For example, the providing unit automatically adjusts the user's schedule and sets reminders. Furthermore, to support the user's health management, the providing unit can also allow the AI ​​to manage diet and exercise records and provide advice for living a healthy lifestyle. Furthermore, the providing unit can provide information on topics of interest to the user and introduce related events to support the user's hobbies. For example, the providing unit provides information on topics of interest to the user and introduces related events. This allows the personal AI system according to the embodiment to provide support and services tailored to the user's needs and preferences.

[0030] The collection unit can collect data from devices and applications that the user normally uses. The collection unit collects data from, for example, devices and applications that the user normally uses. For example, the collection unit collects smartphone usage history and understands the user's behavioral patterns. The collection unit can also collect content posted on social media and identify the user's interests. The collection unit can also collect content of emails and understand the user's interests. This makes it possible to understand the user's daily behavioral patterns and interests. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input smartphone usage history into AI, which can analyze the behavioral patterns.

[0031] The analysis unit can identify the user's requests and preferences based on the collected data. The analysis unit can, for example, identify the user's requests and preferences based on the collected data. For example, the analysis unit can analyze places frequently visited by the user to identify the user's behavioral patterns. The analysis unit can also analyze products frequently purchased by the user to identify the user's purchasing tendencies. The analysis unit can also analyze topics in which the user is interested to identify the user's interests. This makes it possible to accurately identify the user's needs and preferences. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI, which can then identify the user's requests and preferences.

[0032] The providing unit can automatically adjust the schedule using an AI to support the user's schedule management. The providing unit can automatically adjust the schedule using an AI to support the user's schedule management, for example. For example, the providing unit automatically adjusts the user's schedule and sets reminders. The providing unit can also optimize the user's schedule and support efficient time management. This can efficiently support the user's schedule management. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or can be performed without using an AI. For example, the providing unit can input the user's schedule data into an AI, which can then suggest an optimal schedule.

[0033] The providing unit can use AI to manage dietary and exercise records and provide advice for maintaining health to support the user's health management. For example, the providing unit can use AI to manage dietary and exercise records and provide advice for living a healthy lifestyle to support the user's health management. For example, the providing unit can record the user's dietary content and evaluate nutritional balance. The providing unit can also manage the user's exercise records and propose an appropriate exercise plan. The providing unit can also provide advice for maintaining health based on the user's health checkup results. This can efficiently support the user's health management. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's dietary data into AI, which can evaluate nutritional balance.

[0034] The providing unit can provide information on topics of interest and introduce related events to support the user's hobbies. For example, the providing unit can provide information on topics of interest and introduce related events to support the user's hobbies. For example, the providing unit provides the latest information on topics in which the user is interested. The providing unit can also introduce events related to the user's hobbies and encourage participation. The providing unit can also introduce communities related to the user's hobbies and provide opportunities for interaction. This makes it possible to efficiently provide information on the user's hobbies and introduce events. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's interest data into AI, which can then suggest related information and events.

[0035] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the user has used favorably in the past. The collection unit can also collect data from applications that the user has frequently accessed in the past. The collection unit can also prioritize collection methods that the user has given high ratings in the past. This makes it possible to select the optimal collection method based on the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which then selects the optimal collection method.

[0036] The collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, if the user is at work, the collection unit can collect only work-related data. Also, if the user is immersed in a hobby, the collection unit can preferentially collect data related to that hobby. Also, if the user is on vacation, the collection unit can collect data related to relaxation and travel. This makes it possible to filter data based on the user's current activity status and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's current activity status data into AI, which can then filter the data.

[0037] The collection unit can select an appropriate collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Also, if the user prefers text input, the collection unit can preferentially collect text data. Also, if the user prefers image input, the collection unit can preferentially collect image data. This makes it possible to select the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, which then selects the optimal collection means.

[0038] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration when collecting data. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can select highly relevant data.

[0039] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect data related to content frequently posted by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also collect data related to topics in which the user has shown interest on social media. In this way, the user's social media activities can be analyzed and related data can be collected. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can collect related data.

[0040] The collection unit can customize the collection method based on the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit preferentially adopts collection methods that the user has previously rated highly. The collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. The collection unit can also optimize the collection method based on the user's past feedback. This makes it possible to customize the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI, which can then customize the collection method.

[0041] The analysis unit can adjust the level of detail of the analysis according to the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, and the AI ​​can adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms based on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an analysis algorithm specialized for health management to health data. The analysis unit can also apply an analysis algorithm specialized for schedule management to schedule data. The analysis unit can also apply an analysis algorithm specialized for hobbies to hobby data. This makes it possible to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI, which can select an appropriate analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority according to the time when the data was collected during analysis. For example, the analysis unit determines the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. The analysis unit can also prioritize analyzing data collected during a specific period. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI, and the AI ​​can adjust the use of technical terms in the analysis.

[0047] The providing unit can adjust the level of detail of the information provided depending on the importance of the support or service when providing the information. For example, the providing unit adjusts the level of detail of the information provided based on the importance of the support or service when providing the information. For example, the providing unit provides detailed information for support or service with a high level of importance. The providing unit can also provide concise information for support or service with a low level of importance. The providing unit can also provide information with an appropriate level of detail for support or service with a medium level of importance. This makes it possible to adjust the level of detail of the information provided based on the importance of the support or service. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the importance of the support or service into AI, and the AI ​​can adjust the level of detail of the information provided.

[0048] The providing unit can apply different provision algorithms based on the category of support or service when providing the support or service. For example, the providing unit applies different provision algorithms based on the category of support or service when providing the support or service. For example, the providing unit applies a provision algorithm specialized for health management to support or services related to health management. Furthermore, the providing unit can apply a provision algorithm specialized for schedule management to support or services related to schedule management. Furthermore, the providing unit can apply a provision algorithm specialized for hobbies to support or services related to hobbies. This makes it possible to apply different provision algorithms based on the category of support or service. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the category of support or service into AI, which can select an appropriate provision algorithm.

[0049] The providing unit can improve the accuracy of the provision based on the user's past provision results at the time of provision. For example, the providing unit can improve the accuracy of the provision by referring to the user's past provision results at the time of provision. For example, the providing unit corrects the current provision content based on the user's past provision results. The providing unit can also optimize the provision algorithm based on the user's past provision results. The providing unit can also adjust the level of detail of the provision based on the user's past provision results. This can improve the accuracy of the provision by referring to the user's past provision results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past provision result data into AI, which can improve the accuracy of the provision.

[0050] The providing unit can prioritize providing highly relevant support and services based on the user's geographical location information at the time of providing. For example, the providing unit can prioritize providing highly relevant support and services by taking the user's geographical location information into consideration at the time of providing. For example, when the user is in a specific area, the providing unit can prioritize providing support and services related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing support and services related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing support and services in the vicinity of the user's home. In this way, highly relevant support and services can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can select highly relevant support and services.

[0051] The providing unit can analyze the user's social media activity at the time of providing the data and provide related support and services. For example, the providing unit can analyze the user's social media activity at the time of providing the data and provide related support and services. For example, the providing unit can provide support and services related to content that the user frequently posts on social media. The providing unit can also provide related support and services by referring to the activities of the user's friends on social media. The providing unit can also provide support and services related to topics in which the user has shown interest on social media. In this way, the user's social media activity can be analyzed and related support and services can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media data into AI, which can then provide related support and services.

[0052] The providing unit can customize the delivery method based on the user's past feedback when providing the content. The providing unit, for example, customizes the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit preferentially adopts delivery methods that the user has previously given high ratings to. The providing unit can also avoid delivery methods that the user has previously expressed dissatisfaction with. The providing unit can also optimize the delivery method based on the user's past feedback. This makes it possible to customize the delivery method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI, which can then customize the delivery method.

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

[0054] The personal AI system may further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit evaluates the user's sleep quality by collecting and analyzing the user's sleep data. For example, the sleep analysis unit may monitor the user's heart rate and breathing patterns to identify the depth and cycle of sleep. The sleep analysis unit may also suggest an optimal sleeping environment by taking into account the user's sleep environment (temperature, humidity, noise level, etc.). Furthermore, the sleep analysis unit may provide advice on improving the quality of sleep based on the user's sleep data. This allows the user to get better sleep and improve the quality of their daily life.

[0055] The personal AI system can further include a driving analysis unit that analyzes the user's driving habits. The driving analysis unit collects and analyzes the user's driving data to evaluate the safety and efficiency of driving. For example, the driving analysis unit can monitor the user's brake and accelerator operation patterns and provide advice on safe driving. The driving analysis unit can also suggest eco-driving methods to improve fuel efficiency. Furthermore, the driving analysis unit can suggest optimal routes and provide advice on avoiding traffic jams based on the user's driving data. This allows the user to drive safely and efficiently.

[0056] The personalized AI system can further include a dietary analysis unit that analyzes the user's dietary preferences. The dietary analysis unit evaluates the user's dietary preferences and nutritional balance by collecting and analyzing the user's dietary data. For example, the dietary analysis unit can identify the user's favorite ingredients and dishes and suggest recipes based on them. The dietary analysis unit can also provide a healthy meal plan that takes the user's nutritional balance into consideration. Furthermore, the dietary analysis unit can point out areas for improvement in the user's diet based on the user's dietary data and provide advice to support a healthy diet. This allows the user to enjoy a balanced diet and maintain their health.

[0057] The personalized AI system can further include a learning analysis unit that analyzes the user's learning style. The learning analysis unit collects and analyzes the user's learning data to identify the user's learning style and effective learning methods. For example, the learning analysis unit can identify the user's preferred learning method (visual, auditory, tactile, etc.) and provide a learning plan based on that. The learning analysis unit can also monitor the user's learning progress and adjust the learning plan as needed. Furthermore, the learning analysis unit can provide advice to maximize the effectiveness of learning based on the user's learning data. This allows the user to study more effectively and improve their learning outcomes.

[0058] The personalized AI system can further include a travel support unit that supports the user's travel planning. The travel support unit provides optimal travel plans by collecting and analyzing the user's travel data. For example, the travel support unit may suggest travel destinations based on the user's preferences and budget. The travel support unit may also create optimal travel itineraries that match the user's schedule. Furthermore, the travel support unit may suggest activities and tourist spots during the trip based on the user's travel data. This allows the user to plan an efficient and enjoyable trip, improving their satisfaction with the trip.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects the user's needs and preferences. For example, the collection unit collects data from the devices and applications the user uses on a daily basis. Specifically, this includes smartphone usage history, social media posts, and email content. This allows the system to understand the user's behavioral patterns, interests, and concerns. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies the user's needs and preferences based on the collected data. Specifically, the analysis unit analyzes the places the user frequently visits, the products they frequently purchase, and the topics they are interested in, and identifies the user's behavioral patterns, purchasing tendencies, and interests. Step 3: The provision unit provides optimal support and services to the user based on the analysis results obtained by the analysis unit. For example, the provision unit supports the user's schedule management by having the AI ​​automatically adjust appointments and set reminders. To support the user's health management, the AI ​​manages diet and exercise records and provides advice on living a healthy lifestyle. Furthermore, to support the user's hobbies, the AI ​​provides information on topics of interest and introduces related events.

[0061] (Example 2) A personalized AI system according to an embodiment of the present invention provides support and services tailored to a user's needs and preferences. The personalized AI system collects the user's needs and preferences, analyzes them using AI, and provides optimal support and services to the user based on the analysis results. For example, the personalized AI system manages the user's schedule, health, and hobby support. For example, the personalized AI system collects data from the user's daily devices and applications. This data includes smartphone usage history, social media posts, and email content. The personalized AI system then analyzes the collected data. The AI ​​identifies the user's needs and preferences based on the collected data. For example, the AI ​​analyzes the user's frequently visited places, frequently purchased products, and topics of interest. The personalized AI system then provides optimal support and services to the user based on the analysis results. For example, to support the user's schedule management, the AI ​​automatically adjusts schedules and sets reminders. To support health management, the AI ​​manages the user's diet and exercise records and provides advice for living a healthy lifestyle. Furthermore, to support hobbies, the AI ​​provides information on topics of interest to the user and introduces related events. This allows the personal AI system to support the user's daily life and work, improve user satisfaction, and realize a more comfortable life. This allows the personal AI system to provide support and services tailored to the user's needs and preferences. For example, it can efficiently manage the user's schedule, health management, and hobby support. This allows the user's satisfaction to improve and realize a more comfortable life.

[0062] A personal AI system according to an embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects user needs and preferences. The collection unit collects data from, for example, devices and applications used daily by the user. For example, the data includes smartphone usage history, social media posts, and email content. The collection unit collects smartphone usage history to understand the user's behavioral patterns. The collection unit can also collect social media posts to identify the user's interests. The collection unit can also collect email content to understand the user's interests. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, identify the user's needs and preferences based on the collected data. For example, the analysis unit can analyze places frequently visited by the user to identify the user's behavioral patterns. The analysis unit can also analyze products frequently purchased by the user to identify the user's purchasing tendencies. The analysis unit can also analyze topics of interest to the user to identify the user's interests. The provision unit provides optimal support and services to the user based on the analysis results obtained by the analysis unit. The providing unit, for example, allows the AI ​​to automatically adjust schedules to support the user's schedule management. For example, the providing unit automatically adjusts the user's schedule and sets reminders. Furthermore, to support the user's health management, the providing unit can also allow the AI ​​to manage diet and exercise records and provide advice for living a healthy lifestyle. Furthermore, the providing unit can provide information on topics of interest to the user and introduce related events to support the user's hobbies. For example, the providing unit provides information on topics of interest to the user and introduces related events. This allows the personal AI system according to the embodiment to provide support and services tailored to the user's needs and preferences.

[0063] The collection unit can collect data from devices and applications that the user normally uses. The collection unit collects data from, for example, devices and applications that the user normally uses. For example, the collection unit collects smartphone usage history and understands the user's behavioral patterns. The collection unit can also collect content posted on social media and identify the user's interests. The collection unit can also collect content of emails and understand the user's interests. This makes it possible to understand the user's daily behavioral patterns and interests. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input smartphone usage history into AI, which can analyze the behavioral patterns.

[0064] The analysis unit can identify the user's requests and preferences based on the collected data. The analysis unit can, for example, identify the user's requests and preferences based on the collected data. For example, the analysis unit can analyze places frequently visited by the user to identify the user's behavioral patterns. The analysis unit can also analyze products frequently purchased by the user to identify the user's purchasing tendencies. The analysis unit can also analyze topics in which the user is interested to identify the user's interests. This makes it possible to accurately identify the user's needs and preferences. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the collected data into AI, which can then identify the user's requests and preferences.

[0065] The providing unit can automatically adjust the schedule using an AI to support the user's schedule management. The providing unit can automatically adjust the schedule using an AI to support the user's schedule management, for example. For example, the providing unit automatically adjusts the user's schedule and sets reminders. The providing unit can also optimize the user's schedule and support efficient time management. This can efficiently support the user's schedule management. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or can be performed without using an AI. For example, the providing unit can input the user's schedule data into an AI, which can then suggest an optimal schedule.

[0066] The providing unit can use AI to manage dietary and exercise records and provide advice for maintaining health to support the user's health management. For example, the providing unit can use AI to manage dietary and exercise records and provide advice for living a healthy lifestyle to support the user's health management. For example, the providing unit can record the user's dietary content and evaluate nutritional balance. The providing unit can also manage the user's exercise records and propose an appropriate exercise plan. The providing unit can also provide advice for maintaining health based on the user's health checkup results. This can efficiently support the user's health management. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input the user's dietary data into AI, which can evaluate nutritional balance.

[0067] The providing unit can provide information on topics of interest and introduce related events to support the user's hobbies. For example, the providing unit can provide information on topics of interest and introduce related events to support the user's hobbies. For example, the providing unit provides the latest information on topics in which the user is interested. The providing unit can also introduce events related to the user's hobbies and encourage participation. The providing unit can also introduce communities related to the user's hobbies and provide opportunities for interaction. This makes it possible to efficiently provide information on the user's hobbies and introduce events. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's interest data into AI, which can then suggest related information and events.

[0068] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces data collection and collects data when the user is relaxed. Furthermore, if the user is excited, the collection unit can collect data in real time and immediately analyze it. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. This allows the timing of data collection to be optimized according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into an AI, which can then adjust the timing of data collection.

[0069] The collection unit can analyze the user's past data collection history and select an appropriate collection method. The collection unit, for example, analyzes the user's past data collection history and selects the optimal collection method. For example, the collection unit prioritizes collecting data from devices that the user has used favorably in the past. The collection unit can also collect data from applications that the user has frequently accessed in the past. The collection unit can also prioritize collection methods that the user has given high ratings in the past. This makes it possible to select the optimal collection method based on the user's past data collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past data collection history into AI, which then selects the optimal collection method.

[0070] The collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current activity status and areas of interest when collecting data. For example, if the user is at work, the collection unit can collect only work-related data. Also, if the user is immersed in a hobby, the collection unit can preferentially collect data related to that hobby. Also, if the user is on vacation, the collection unit can collect data related to relaxation and travel. This makes it possible to filter data based on the user's current activity status and areas of interest. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's current activity status data into AI, which can then filter the data.

[0071] The collection unit can select an appropriate collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. For example, if the user prefers voice input, the collection unit can preferentially collect voice data. Also, if the user prefers text input, the collection unit can preferentially collect text data. Also, if the user prefers image input, the collection unit can preferentially collect image data. This makes it possible to select the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into AI, which then selects the optimal collection means.

[0072] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data related to relaxation. Also, if the user is excited, the collection unit can prioritize collecting data for reducing excitement. Also, if the user is tired, the collection unit can prioritize collecting data related to rest. This makes it possible to determine the priority of data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into an AI and determine the priority of data to be collected by the AI.

[0073] The collection unit can prioritize collecting highly relevant data based on the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking the user's geographical location information into consideration when collecting data. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. In this way, highly relevant data can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information into AI, which can select highly relevant data.

[0074] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect data related to content frequently posted by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also collect data related to topics in which the user has shown interest on social media. In this way, the user's social media activities can be analyzed and related data can be collected. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into AI, which can collect related data.

[0075] The collection unit can customize the collection method based on the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit preferentially adopts collection methods that the user has previously rated highly. The collection unit can also avoid collection methods that the user has previously expressed dissatisfaction with. The collection unit can also optimize the collection method based on the user's past feedback. This makes it possible to customize the collection method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into AI, which can then customize the collection method.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. This allows the presentation method of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the presentation method of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis according to the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This makes it possible to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI, and the AI ​​can adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms based on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an analysis algorithm specialized for health management to health data. The analysis unit can also apply an analysis algorithm specialized for schedule management to schedule data. The analysis unit can also apply an analysis algorithm specialized for hobbies to hobby data. This makes it possible to apply different analysis algorithms depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data into AI, which can select an appropriate analysis algorithm.

[0079] The analysis unit can improve the accuracy of the analysis based on the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit corrects the current analysis result based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis based on the user's past analysis results. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's past analysis result data into AI, which can improve the accuracy of the analysis.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Also, if the user is relaxed, the analysis unit can provide a detailed analysis result. Also, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the length of the analysis to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into an AI, which can then adjust the length of the analysis.

[0081] The analysis unit can determine the analysis priority according to the time when the data was collected during analysis. For example, the analysis unit determines the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. The analysis unit can also prioritize analyzing data collected during a specific period. This makes it possible to determine the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into AI, and the AI ​​can determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data into AI, which can adjust the order of analysis.

[0083] The analysis unit can adjust the use of technical terms in the analysis based on the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI, and the AI ​​can adjust the use of technical terms in the analysis.

[0084] The providing unit can estimate the user's emotions and adjust the way in which the support and services are presented based on the estimated user's emotions. For example, the providing unit can estimate the user's emotions and adjust the way in which the support and services are presented based on the estimated user's emotions. For example, the providing unit can provide detailed support and services when the user is relaxed. Furthermore, the providing unit can provide concise support and services that focus on the main points when the user is in a hurry. Furthermore, the providing unit can provide visually stimulating support and services when the user is excited. This allows the way in which the support and services are presented to be adjusted based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into an AI, which can then adjust the way in which the support and services are presented.

[0085] The providing unit can adjust the level of detail of the information provided depending on the importance of the support or service when providing the information. For example, the providing unit adjusts the level of detail of the information provided based on the importance of the support or service when providing the information. For example, the providing unit provides detailed information for support or service with a high level of importance. The providing unit can also provide concise information for support or service with a low level of importance. The providing unit can also provide information with an appropriate level of detail for support or service with a medium level of importance. This makes it possible to adjust the level of detail of the information provided based on the importance of the support or service. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the importance of the support or service into AI, and the AI ​​can adjust the level of detail of the information provided.

[0086] The providing unit can apply different provision algorithms based on the category of support or service when providing the support or service. For example, the providing unit applies different provision algorithms based on the category of support or service when providing the support or service. For example, the providing unit applies a provision algorithm specialized for health management to support or services related to health management. Furthermore, the providing unit can apply a provision algorithm specialized for schedule management to support or services related to schedule management. Furthermore, the providing unit can apply a provision algorithm specialized for hobbies to support or services related to hobbies. This makes it possible to apply different provision algorithms based on the category of support or service. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the category of support or service into AI, which can select an appropriate provision algorithm.

[0087] The providing unit can improve the accuracy of the provision based on the user's past provision results at the time of provision. For example, the providing unit can improve the accuracy of the provision by referring to the user's past provision results at the time of provision. For example, the providing unit corrects the current provision content based on the user's past provision results. The providing unit can also optimize the provision algorithm based on the user's past provision results. The providing unit can also adjust the level of detail of the provision based on the user's past provision results. This can improve the accuracy of the provision by referring to the user's past provision results. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's past provision result data into AI, which can improve the accuracy of the provision.

[0088] The providing unit can estimate the user's emotions and determine the priority of support and services to be provided based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions and determines the priority of support and services to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing support and services related to relaxation. Furthermore, if the user is excited, the providing unit can prioritize providing support and services to alleviate the excitement. Furthermore, if the user is tired, the providing unit can prioritize providing support and services related to rest. This makes it possible to determine the priority of support and services to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into an AI, which can then determine the priority of support and services.

[0089] The providing unit can prioritize providing highly relevant support and services based on the user's geographical location information at the time of providing. For example, the providing unit can prioritize providing highly relevant support and services by taking the user's geographical location information into consideration at the time of providing. For example, when the user is in a specific area, the providing unit can prioritize providing support and services related to that area. Furthermore, when the user is traveling, the providing unit can prioritize providing support and services related to the travel destination. Furthermore, when the user is at home, the providing unit can prioritize providing support and services in the vicinity of the user's home. In this way, highly relevant support and services can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information into AI, which can select highly relevant support and services.

[0090] The providing unit can analyze the user's social media activity at the time of providing the data and provide related support and services. For example, the providing unit can analyze the user's social media activity at the time of providing the data and provide related support and services. For example, the providing unit can provide support and services related to content that the user frequently posts on social media. The providing unit can also provide related support and services by referring to the activities of the user's friends on social media. The providing unit can also provide support and services related to topics in which the user has shown interest on social media. In this way, the user's social media activity can be analyzed and related support and services can be provided. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input the user's social media data into AI, which can then provide related support and services.

[0091] The providing unit can customize the delivery method based on the user's past feedback when providing the content. The providing unit, for example, customizes the delivery method by reflecting the user's past feedback when providing the content. For example, the providing unit preferentially adopts delivery methods that the user has previously given high ratings to. The providing unit can also avoid delivery methods that the user has previously expressed dissatisfaction with. The providing unit can also optimize the delivery method based on the user's past feedback. This makes it possible to customize the delivery method by reflecting the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past feedback data into AI, which can then customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A grasps the user's behavioral patterns and interests. The analysis unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's needs and preferences based on the collected data. The provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12, and provides optimal support and services to the user based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A grasps the user's behavioral patterns and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's needs and preferences based on the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the user with optimal support and services based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the headset type terminal 314, and grasps the user's behavioral patterns and interests using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's needs and preferences based on the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the user with optimal support and services based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and microphone 238 of the robot 414, and the control unit 46A grasps the user's behavioral patterns and interests. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and identifies the user's needs and preferences based on the collected data. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the user with optimal support and services based on the analysis results. Some or all of the collection unit, analysis unit, and provision unit may be realized, for example, by the control unit 46A of the robot 414.

[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 personal AI system may further include a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit evaluates the user's sleep quality by collecting and analyzing the user's sleep data. For example, the sleep analysis unit may monitor the user's heart rate and breathing patterns to identify the depth and cycle of sleep. The sleep analysis unit may also suggest an optimal sleeping environment by taking into account the user's sleep environment (temperature, humidity, noise level, etc.). Furthermore, the sleep analysis unit may provide advice on improving the quality of sleep based on the user's sleep data. This allows the user to get better sleep and improve the quality of their daily life.

[0094] The personalized AI system can further include a music recommendation unit that estimates the user's emotions and recommends music based on the estimated emotions. The music recommendation unit analyzes the user's emotional data and selects music that matches the user's mood. For example, if the user wants to relax, it can recommend calming music. On the other hand, if the user is feeling energetic, it can recommend upbeat music. Furthermore, the music recommendation unit can take into account the user's past music playback history and prioritize recommending music from the user's favorite genres and artists. This allows the user to enjoy music that matches their mood at the time, which can be useful for regulating their emotions.

[0095] The personal AI system can further include a driving analysis unit that analyzes the user's driving habits. The driving analysis unit collects and analyzes the user's driving data to evaluate the safety and efficiency of driving. For example, the driving analysis unit can monitor the user's brake and accelerator operation patterns and provide advice on safe driving. The driving analysis unit can also suggest eco-driving methods to improve fuel efficiency. Furthermore, the driving analysis unit can suggest optimal routes and provide advice on avoiding traffic jams based on the user's driving data. This allows the user to drive safely and efficiently.

[0096] The personal AI system can further include a stress management unit that estimates the user's emotions and manages stress based on the estimated emotions. The stress management unit analyzes the user's emotional data and evaluates their stress level. For example, if the user is feeling high stress, it can provide relaxation advice. The stress management unit can also provide breathing exercises and meditation guides to reduce the user's stress. Furthermore, the stress management unit can monitor the user's stress level and suggest professional support as needed. This allows the user to effectively manage stress and maintain their physical and mental health.

[0097] The personalized AI system can further include a dietary analysis unit that analyzes the user's dietary preferences. The dietary analysis unit evaluates the user's dietary preferences and nutritional balance by collecting and analyzing the user's dietary data. For example, the dietary analysis unit can identify the user's favorite ingredients and dishes and suggest recipes based on them. The dietary analysis unit can also provide a healthy meal plan that takes the user's nutritional balance into consideration. Furthermore, the dietary analysis unit can point out areas for improvement in the user's diet based on the user's dietary data and provide advice to support a healthy diet. This allows the user to enjoy a balanced diet and maintain their health.

[0098] The personal AI system may further include a fitness adjustment unit that estimates the user's emotions and adjusts the fitness plan based on the estimated emotions. The fitness adjustment unit analyzes the user's emotional data and provides a fitness plan that matches the user's mood. For example, if the user is tired, it may suggest light exercise. Alternatively, if the user is feeling energetic, it may suggest hard training. Furthermore, the fitness adjustment unit may provide an effective training plan by taking into account the user's past exercise history. This allows the user to exercise according to their mood at that time, maximizing the benefits of their fitness.

[0099] The personalized AI system can further include a learning analysis unit that analyzes the user's learning style. The learning analysis unit collects and analyzes the user's learning data to identify the user's learning style and effective learning methods. For example, the learning analysis unit can identify the user's preferred learning method (visual, auditory, tactile, etc.) and provide a learning plan based on that. The learning analysis unit can also monitor the user's learning progress and adjust the learning plan as needed. Furthermore, the learning analysis unit can provide advice to maximize the effectiveness of learning based on the user's learning data. This allows the user to study more effectively and improve their learning outcomes.

[0100] The personal AI system can further include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on the estimated emotions. The communication adjustment unit analyzes the user's emotional data and provides a communication method that matches the user's mood. For example, if the user is relaxed, detailed information can be provided. Alternatively, if the user is in a hurry, concise information that focuses on the main points can be provided. Furthermore, the communication adjustment unit can provide an effective communication method by taking into account the user's past communication history. This allows the user to communicate in a way that matches their mood at the time, improving the efficiency of information transmission.

[0101] The personalized AI system can further include a travel support unit that supports the user's travel planning. The travel support unit provides optimal travel plans by collecting and analyzing the user's travel data. For example, the travel support unit may suggest travel destinations based on the user's preferences and budget. The travel support unit may also create optimal travel itineraries that match the user's schedule. Furthermore, the travel support unit may suggest activities and tourist spots during the trip based on the user's travel data. This allows the user to plan an efficient and enjoyable trip, improving their satisfaction with the trip.

[0102] The personalized AI system can further include an entertainment provider that estimates the user's emotions and provides entertainment based on the estimated emotions. The entertainment provider analyzes the user's emotional data and selects entertainment that matches the user's mood. For example, if the user wants to relax, it can recommend calming movies or dramas. On the other hand, if the user is feeling energetic, it can recommend action movies or sporting events. Furthermore, the entertainment provider can take the user's past viewing history into consideration and prioritize the recommendation of genres and works that the user prefers. This allows the user to enjoy entertainment that matches their mood at any given time, refreshing them.

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

[0104] Step 1: The collection unit collects the user's needs and preferences. For example, the collection unit collects data from the devices and applications the user uses on a daily basis. Specifically, this includes smartphone usage history, social media posts, and email content. This allows the system to understand the user's behavioral patterns, interests, and concerns. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit identifies the user's needs and preferences based on the collected data. Specifically, the analysis unit analyzes the places the user frequently visits, the products they frequently purchase, and the topics they are interested in, and identifies the user's behavioral patterns, purchasing tendencies, and interests. Step 3: The provision unit provides optimal support and services to the user based on the analysis results obtained by the analysis unit. For example, the provision unit supports the user's schedule management by having the AI ​​automatically adjust appointments and set reminders. To support the user's health management, the AI ​​manages diet and exercise records and provides advice on living a healthy lifestyle. Furthermore, to support the user's hobbies, the AI ​​provides information on topics of interest and introduces related events.

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

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

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

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

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

[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 for collecting user needs and preferences; an analysis unit that analyzes the data collected by the collection unit; a providing unit that provides appropriate support and services to the user based on the analysis results obtained by the analyzing unit. A system characterized by:

2. The collecting unit Collect data from the devices and applications you use every day 2. The system of claim 1.

3. The analysis unit Identifying user needs and preferences based on collected data 2. The system of claim 1.

4. The providing unit AI automatically adjusts schedules to support users' schedule management 2. The system of claim 1.

5. The providing unit To help users manage their health, AI will manage their diet and exercise records and provide advice on how to stay healthy.

2. The system of claim 1.

6. The providing unit Supporting users' hobbies by providing information on topics of interest and introducing related events 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of data collection according to the estimated user emotions.

2. The system of claim 1.

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

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