system

Abstract

The system according to the embodiment aims to provide content based on the user's hobbies and preferences, thereby reducing stress. [Solution] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an AI robot collaboration unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit to understand the user's hobbies and interests. The provision unit provides content based on the hobbies and interests understood by the analysis unit. The AI ​​robot collaboration unit reduces stress through the content or dialogue provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide content based on users' hobbies and preferences or reduce stress, so there is room for improvement.

[0005] The system according to the embodiment aims to provide content based on the user's hobbies and preferences, thereby reducing stress. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an AI robot collaboration unit. The collection unit collects user data. The analysis unit analyzes the data collected by the collection unit to understand the user's hobbies and interests. The provision unit provides content based on the hobbies and interests understood by the analysis unit. The AI ​​robot collaboration unit reduces stress through the content or dialogue provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide content based on the user's hobbies and preferences, thereby reducing stress. [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 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 healing provision system according to an embodiment of the present invention collects data on the family, hobbies, and interests of individual company employees and general users from their smartphones and various app data, and periodically provides content that falls into the "healing" category. The healing provision system collects data on the user's family structure, hobbies, and interests from their smartphones and various apps. For example, it collects data such as photos taken with the user's smartphone, content posted to social media, and songs played in music apps. This data is collected with the user's permission. Next, AI analyzes the collected data to understand the user's hobbies and interests. For example, it analyzes the genres of music the user frequently listens to and the types of images and videos frequently viewed. This allows it to identify content that the user finds "healing." It then periodically provides content that falls into the identified "healing" category. The content provided is selected from the collected data, such as music, images, videos, and comics posted on social media. For example, it provides a playlist of the user's favorite music, images of the user's favorite scenery, and relaxing videos. It also works with AI robots to reduce stress through dialogue. Through dialogue with the user, the AI ​​robot provides advice and relaxation methods to reduce the user's stress. For example, when the user is feeling stressed, the AI ​​robot can play relaxing music or suggest relaxation methods. This mechanism allows users to feel "healed" in their daily lives and reduce stress. For example, when tired from work, listening to relaxing music or watching relaxing videos can help refresh the mind and body. In addition, through dialogue with the AI ​​robot, users can receive specific advice on how to reduce stress. In this way, the healing provision system collects, analyzes, and provides user data, thereby reducing stress.

[0029] The healing provision system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an AI robot collaboration unit. The collection unit collects user data. The user data includes, but is not limited to, behavioral data, location information, and purchase history. The collection unit collects data such as the user's family structure, hobbies, and interests from a smartphone or various apps. For example, the collection unit collects data such as photos taken by the user with the smartphone, content posted to social media, and songs played on a music app. The collection unit collects data with the user's permission. The analysis unit analyzes the data collected by the collection unit to identify the user's hobbies and interests. Hobbies and interests include, but are not limited to, music genres, types of movies, and sports preferences. The analysis unit analyzes the collected data to identify, for example, the genres of music the user frequently listens to and the types of images and videos the user frequently views. The analysis unit analyzes the data using AI to identify the user's hobbies and interests. The provision unit provides content based on the hobbies and interests identified by the analysis unit. The content includes, for example, music, videos, articles, etc., but is not limited to these examples. The providing unit periodically provides content such as music, images, videos, and comics posted on social media, for example, based on the analysis results. The providing unit selects content using AI and provides it to the user. The AI ​​robot collaboration unit reduces stress through the content and dialogue provided by the providing unit. The AI ​​robot collaboration unit provides advice and relaxation methods for reducing stress, for example, through dialogue with the user. The AI ​​robot collaboration unit estimates the user's emotions using AI and provides appropriate advice and relaxation methods. In this way, the healing provision system according to the embodiment can collect, analyze, and provide user data to reduce stress.

[0030] The collection unit can collect data such as the user's family structure, hobbies, and interests from the smartphone or each app. The collection unit collects data such as the user's family structure, hobbies, and interests from the smartphone or each app. For example, the collection unit collects data such as photos taken by the user with the smartphone, content posted to social media, and songs played in a music app. The collection unit collects data with the user's permission. By collecting data from the smartphone and each app, detailed information about the user can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartphone or each app into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data to determine the genre of music the user frequently listens to or the type of images or videos the user frequently views. For example, the analysis unit can identify the genre of music the user frequently listens to or the type of images or videos the user frequently views based on the collected data. The analysis unit can also identify the type of images or videos the user frequently views based on the collected data. The analysis unit can analyze the data using AI to identify the user's hobbies and preferences. This allows for a detailed understanding of the user's hobbies and preferences. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0032] The providing unit may periodically provide music, images, videos, comics posted on social media, or other content based on the analysis results. The providing unit may periodically provide content such as music, images, videos, and comics posted on social media based on the analysis results. For example, the providing unit may provide a playlist of music frequently listened to by the user. The providing unit may also provide images of scenery the user likes. Furthermore, the providing unit may also provide relaxing videos. The providing unit may select content using AI and provide it to the user. This allows content that matches the user's hobbies and preferences to be periodically provided. 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 may input the analysis results into a generating AI and cause the generating AI to select content.

[0033] The AI ​​robot collaboration unit can provide advice or relaxation methods to reduce stress through dialogue with the user. The AI ​​robot collaboration unit can provide advice or relaxation methods to reduce stress through dialogue with the user, for example. For example, the AI ​​robot collaboration unit plays relaxing music when the user is feeling stressed. The AI ​​robot collaboration unit can also suggest relaxation methods to the user. Furthermore, the AI ​​robot collaboration unit can provide specific advice to reduce stress through dialogue with the user. The AI ​​robot collaboration unit uses AI to estimate the user's emotions and provide appropriate advice or relaxation methods. This makes it possible to provide specific advice or relaxation methods to reduce the user's stress. Some or all of the above-mentioned processing in the AI ​​robot collaboration unit can be performed using AI, for example, or without using AI. For example, the AI ​​robot collaboration unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0034] The AI ​​robot collaboration unit can play relaxing music when the user is feeling stressed. The AI ​​robot collaboration unit, for example, plays relaxing music when the user is feeling stressed. For example, the AI ​​robot collaboration unit plays classical music when the user is feeling stressed. The AI ​​robot collaboration unit can also play natural sounds when the user is feeling stressed. Furthermore, the AI ​​robot collaboration unit can also play soothing music when the user is feeling stressed. This makes it possible to provide relaxing music when the user is feeling stressed. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's emotional data into the generation AI and cause the generation AI to select relaxing music.

[0035] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit can prioritize collecting data from apps that the user frequently used in the past. The collection unit can also optimize the collection method based on information that was particularly useful among the data collected in the past by the user. Furthermore, the collection unit can analyze the user's past data collection history and select the most efficient collection timing. In this way, the optimal collection method can be selected by analyzing the past data collection history. 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 data collection history into a generation AI and have the generation AI select a collection method.

[0036] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting data related to topics in which the user is currently interested. The collection unit can also filter appropriate data depending on the user's living situation (e.g., at work, on vacation). Furthermore, the collection unit can exclude unnecessary data and collect only necessary data based on the user's areas of interest. This allows for more relevant data to be collected by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to a generation AI and have the generation AI perform data filtering.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is traveling, the collection unit prioritizes collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to events and activities around the user's home. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. In this way, highly relevant data can be collected preferentially 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 AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0038] When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit can prioritize collecting data related to content that the user frequently posts on social media. The collection unit can also analyze the content of posts from accounts the user follows and collect related data. Furthermore, the collection unit can prioritize collecting data related to posts that the user receives many likes and comments on. In this way, related data can be collected by analyzing the user's social media activity. 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 social media activity data into a generation AI and have the generation AI select related data.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis of data with high importance. The analysis unit can also perform a concise analysis of data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This enables efficient analysis by adjusting 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 the generation AI and have the generation AI adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a music analysis algorithm to music data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply a video analysis algorithm to video data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0041] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze older data as needed. Furthermore, the analysis unit can adjust the analysis priority based on the time when the data was collected. This enables efficient analysis by determining 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 the generation AI and have the generation AI determine the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting 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 to the generation AI and have the generation AI adjust the order of analysis.

[0043] The providing unit can adjust the level of detail of the content provided based on the importance of the content when providing the content. For example, the providing unit provides detailed information for content with high importance. The providing unit can also provide concise information for content with low importance. Furthermore, the providing unit can adjust the level of detail of the content provided according to the importance. This enables efficient content provision by adjusting the level of detail of the content provided based on the importance of the content. 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 importance of the content to the generating AI and cause the generating AI to adjust the level of detail of the content provided.

[0044] The providing unit can apply different providing algorithms depending on the content category when providing the content. For example, the providing unit applies a music providing algorithm to music content. The providing unit can also apply an image providing algorithm to image content. The providing unit can also apply a video providing algorithm to video content. This improves the accuracy of provision by applying an appropriate providing algorithm depending on the content category. 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 content category to the generation AI and cause the generation AI to apply the providing algorithm.

[0045] The providing unit can determine the priority of provision based on the time of collection of content at the time of provision. For example, the providing unit provides the latest content preferentially. The providing unit can also provide older content as needed. Furthermore, the providing unit can adjust the priority of provision based on the time of collection of content. This enables efficient content provision by determining the priority of provision based on the time of collection of content. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of collection of content to the generation AI and have the generation AI determine the priority of provision.

[0046] The providing unit can adjust the order of content provision based on the relevance of the content when providing the content. For example, the providing unit provides highly relevant content preferentially. The providing unit can also postpone content with low relevance. Furthermore, the providing unit can adjust the order of content provision according to the relevance of the content. This enables efficient content provision by adjusting the order of content provision based on the relevance of the content. 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 relevance of content to a generating AI and cause the generating AI to adjust the order of content provision.

[0047] During a dialogue, the AI ​​robot collaboration unit can select appropriate dialogue content by referring to the user's past dialogue history. The AI ​​robot collaboration unit selects dialogue content, for example, based on topics in which the user has shown interest in the past. The AI ​​robot collaboration unit can also select dialogue content based on topics that have made the user feel relaxed in the past. Furthermore, the AI ​​robot collaboration unit can analyze the user's past dialogue history and select the most effective dialogue content. In this way, the optimal dialogue content can be selected by referring to the user's past dialogue history. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's past dialogue history into the generation AI and cause the generation AI to select the dialogue content.

[0048] During dialogue, the AI ​​robot collaboration unit can customize the dialogue means based on the user's current living situation. For example, if the user is at work, the AI ​​robot collaboration unit can provide a short dialogue. Also, if the user is on vacation, the AI ​​robot collaboration unit can provide a relaxing dialogue. Furthermore, the AI ​​robot collaboration unit can customize the dialogue means (voice, text, etc.) according to the user's living situation. This allows for customizing the dialogue means based on the user's living situation, making it possible to provide a more appropriate dialogue. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's living situation data into the generation AI and cause the generation AI to customize the dialogue means.

[0049] During a dialogue, the AI ​​robot collaboration unit can select appropriate dialogue content based on the user's geographical location information. For example, if the user is traveling, the AI ​​robot collaboration unit can provide topics related to the user's travel destination. Furthermore, if the user is at home, the AI ​​robot collaboration unit can also provide topics related to events and activities around the user's home. Furthermore, if the user is in a specific location, the AI ​​robot collaboration unit can also provide information related to that location. This makes it possible to select optimal dialogue content by taking the user's geographical location information into consideration. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's geographical location information to the generation AI and cause the generation AI to select dialogue content.

[0050] During a dialogue, the AI ​​robot collaboration unit can analyze the user's social media activity and suggest ways to communicate. For example, the AI ​​robot collaboration unit can provide topics related to content that the user frequently posts on social media. The AI ​​robot collaboration unit can also suggest ways to communicate based on the content posted by accounts the user follows. Furthermore, the AI ​​robot collaboration unit can also provide topics related to posts that the user has many likes or comments on. In this way, by analyzing the user's social media activity, it is possible to suggest optimal ways to communicate. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest ways to communicate.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can also analyze the user's past data collection history and predict the optimal timing for data collection. For example, it predicts the timing for the next data collection based on the time periods during which the user previously permitted data collection. It can also increase the success rate of data collection by avoiding time periods during which the user previously refused data collection. It can also predict the optimal timing for data collection based on the user's lifestyle rhythm. This makes it possible to collect data more efficiently by utilizing the user's past data collection history.

[0053] The analysis unit can also select an analysis method based on the source of data collection. For example, a smartphone-specific analysis method can be applied to data collected from a smartphone. Also, a social networking site-specific analysis method can be applied to data collected from a music app. In this way, the accuracy of the analysis can be improved by selecting an appropriate analysis method depending on the source of data collection.

[0054] The providing unit can also analyze the user's past content usage history and predict the optimal timing for providing content. For example, the next timing for providing content can be predicted based on the time period in which the user used content in the past. Also, by avoiding time periods in which the user did not use content in the past, the success rate of content provision can be increased. Furthermore, the optimal timing for providing content can be predicted based on the user's lifestyle rhythm. This makes it possible to provide content more efficiently by utilizing the user's past content usage history.

[0055] The AI ​​robot collaboration unit can also analyze the user's past conversation history and predict the optimal timing for conversation. For example, it can predict the next conversation timing based on the time of day when the user has previously had a conversation. It can also increase the success rate of a conversation by avoiding time periods when the user has previously refused a conversation. It can also predict the optimal timing for a conversation based on the user's daily rhythm. This makes it possible to utilize past conversation history to enable more efficient conversations.

[0056] The collection unit can also customize the data collection method based on the user's geographical location information. For example, if the user is traveling, tourist information and event information for the travel destination can be collected with priority. Also, if the user is at home, news and weather information for the area around the user's home can be collected with priority. Furthermore, if the user is in a specific location, data related to that location can be collected with priority. This allows more relevant data to be collected taking the user's geographical location information into consideration.

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

[0058] Step 1: The collection unit collects user data. User data includes behavioral data, location information, purchase history, family composition, hobbies and interests, photos taken with a smartphone, content posted to social media, and songs played in music apps. The collection unit collects this data with the user's permission. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's hobbies and preferences. Hobbies and preferences include music genres, types of movies, and sports preferences. The analysis unit uses AI to analyze the data and identify the genres of music the user often listens to and the types of images and videos they often view. Step 3: The provider provides content based on the hobbies and preferences identified by the analysis unit. Content includes music, videos, articles, images, videos, and comics posted on social media. The provider uses AI to select content and provide it to users on a regular basis. Step 4: The AI ​​robot collaboration unit reduces stress through the content and dialogue provided by the provider unit. The AI ​​robot collaboration unit provides advice and relaxation methods to reduce stress through dialogue with the user. It uses AI to estimate the user's emotions and provides appropriate advice and relaxation methods.

[0059] (Example 2) A healing provision system according to an embodiment of the present invention collects data on the family, hobbies, and interests of individual company employees and general users from their smartphones and various app data, and periodically provides content that falls into the "healing" category. The healing provision system collects data on the user's family structure, hobbies, and interests from their smartphones and various apps. For example, it collects data such as photos taken with the user's smartphone, content posted to social media, and songs played in music apps. This data is collected with the user's permission. Next, AI analyzes the collected data to understand the user's hobbies and interests. For example, it analyzes the genres of music the user frequently listens to and the types of images and videos frequently viewed. This allows it to identify content that the user finds "healing." It then periodically provides content that falls into the identified "healing" category. The content provided is selected from the collected data, such as music, images, videos, and comics posted on social media. For example, it provides a playlist of the user's favorite music, images of the user's favorite scenery, and relaxing videos. It also works with AI robots to reduce stress through dialogue. Through dialogue with the user, the AI ​​robot provides advice and relaxation methods to reduce the user's stress. For example, when the user is feeling stressed, the AI ​​robot can play relaxing music or suggest relaxation methods. This mechanism allows users to feel "healed" in their daily lives and reduce stress. For example, when tired from work, listening to relaxing music or watching relaxing videos can help refresh the mind and body. In addition, through dialogue with the AI ​​robot, users can receive specific advice on how to reduce stress. In this way, the healing provision system collects, analyzes, and provides user data, thereby reducing stress.

[0060] The healing provision system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and an AI robot collaboration unit. The collection unit collects user data. The user data includes, but is not limited to, behavioral data, location information, and purchase history. The collection unit collects data such as the user's family structure, hobbies, and interests from a smartphone or various apps. For example, the collection unit collects data such as photos taken by the user with the smartphone, content posted to social media, and songs played on a music app. The collection unit collects data with the user's permission. The analysis unit analyzes the data collected by the collection unit to identify the user's hobbies and interests. Hobbies and interests include, but are not limited to, music genres, types of movies, and sports preferences. The analysis unit analyzes the collected data to identify, for example, the genres of music the user frequently listens to and the types of images and videos the user frequently views. The analysis unit analyzes the data using AI to identify the user's hobbies and interests. The provision unit provides content based on the hobbies and interests identified by the analysis unit. The content includes, for example, music, videos, articles, etc., but is not limited to these examples. The providing unit periodically provides content such as music, images, videos, and comics posted on social media, for example, based on the analysis results. The providing unit selects content using AI and provides it to the user. The AI ​​robot collaboration unit reduces stress through the content and dialogue provided by the providing unit. The AI ​​robot collaboration unit provides advice and relaxation methods for reducing stress, for example, through dialogue with the user. The AI ​​robot collaboration unit estimates the user's emotions using AI and provides appropriate advice and relaxation methods. In this way, the healing provision system according to the embodiment can collect, analyze, and provide user data to reduce stress.

[0061] The collection unit can collect data such as the user's family structure, hobbies, and interests from the smartphone or each app. The collection unit collects data such as the user's family structure, hobbies, and interests from the smartphone or each app. For example, the collection unit collects data such as photos taken by the user with the smartphone, content posted to social media, and songs played in a music app. The collection unit collects data with the user's permission. By collecting data from the smartphone and each app, detailed information about the user can be obtained. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the smartphone or each app into a generation AI and have the generation AI analyze the data.

[0062] The analysis unit can analyze the collected data to determine the genre of music the user frequently listens to or the type of images or videos the user frequently views. For example, the analysis unit can identify the genre of music the user frequently listens to or the type of images or videos the user frequently views based on the collected data. The analysis unit can also identify the type of images or videos the user frequently views based on the collected data. The analysis unit can analyze the data using AI to identify the user's hobbies and preferences. This allows for a detailed understanding of the user's hobbies and preferences. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data.

[0063] The providing unit may periodically provide music, images, videos, comics posted on social media, or other content based on the analysis results. The providing unit may periodically provide content such as music, images, videos, and comics posted on social media based on the analysis results. For example, the providing unit may provide a playlist of music frequently listened to by the user. The providing unit may also provide images of scenery the user likes. Furthermore, the providing unit may also provide relaxing videos. The providing unit may select content using AI and provide it to the user. This allows content that matches the user's hobbies and preferences to be periodically provided. 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 may input the analysis results into a generating AI and cause the generating AI to select content.

[0064] The AI ​​robot collaboration unit can provide advice or relaxation methods to reduce stress through dialogue with the user. The AI ​​robot collaboration unit can provide advice or relaxation methods to reduce stress through dialogue with the user, for example. For example, the AI ​​robot collaboration unit plays relaxing music when the user is feeling stressed. The AI ​​robot collaboration unit can also suggest relaxation methods to the user. Furthermore, the AI ​​robot collaboration unit can provide specific advice to reduce stress through dialogue with the user. The AI ​​robot collaboration unit uses AI to estimate the user's emotions and provide appropriate advice or relaxation methods. This makes it possible to provide specific advice or relaxation methods to reduce the user's stress. Some or all of the above-mentioned processing in the AI ​​robot collaboration unit can be performed using AI, for example, or without using AI. For example, the AI ​​robot collaboration unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0065] The AI ​​robot collaboration unit can play relaxing music when the user is feeling stressed. The AI ​​robot collaboration unit, for example, plays relaxing music when the user is feeling stressed. For example, the AI ​​robot collaboration unit plays classical music when the user is feeling stressed. The AI ​​robot collaboration unit can also play natural sounds when the user is feeling stressed. Furthermore, the AI ​​robot collaboration unit can also play soothing music when the user is feeling stressed. This makes it possible to provide relaxing music when the user is feeling stressed. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's emotional data into the generation AI and cause the generation AI to select relaxing music.

[0066] 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 refrains from collecting data when the user is stressed and collects data when the user is relaxed. The collection unit can also actively collect data and obtain detailed information when the user is relaxed. Furthermore, the collection unit can temporarily stop data collection when the user is busy and resume it when the user is calm. This allows for more appropriate data to be collected by adjusting the timing of data collection according to the user's emotions. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI adjust the timing of data collection.

[0067] The collection unit can analyze the user's past data collection history and select an appropriate collection method. For example, the collection unit can prioritize collecting data from apps that the user frequently used in the past. The collection unit can also optimize the collection method based on information that was particularly useful among the data collected in the past by the user. Furthermore, the collection unit can analyze the user's past data collection history and select the most efficient collection timing. In this way, the optimal collection method can be selected by analyzing the past data collection history. 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 data collection history into a generation AI and have the generation AI select a collection method.

[0068] When collecting data, the collection unit can filter the data based on the user's current living situation and areas of interest. For example, the collection unit prioritizes collecting data related to topics in which the user is currently interested. The collection unit can also filter appropriate data depending on the user's living situation (e.g., at work, on vacation). Furthermore, the collection unit can exclude unnecessary data and collect only necessary data based on the user's areas of interest. This allows for more relevant data to be collected by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's living situation and areas of interest to a generation AI and have the generation AI perform data filtering.

[0069] The collection unit can estimate the user's emotions and determine 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 relaxing content. Furthermore, if the user is having fun, the collection unit can prioritize collecting data related to entertainment. Furthermore, if the user is tired, the collection unit can prioritize collecting data related to refreshing content. This allows more appropriate data to be collected by prioritizing data based on the user's emotions. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's emotion data into a generation AI and have the generation AI determine the priority of the data.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is traveling, the collection unit prioritizes collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data related to events and activities around the user's home. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. In this way, highly relevant data can be collected preferentially 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 AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to select highly relevant data.

[0071] When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit can prioritize collecting data related to content that the user frequently posts on social media. The collection unit can also analyze the content of posts from accounts the user follows and collect related data. Furthermore, the collection unit can prioritize collecting data related to posts that the user receives many likes and comments on. In this way, related data can be collected by analyzing the user's social media activity. 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 social media activity data into a generation AI and have the generation AI select related data.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise and to-the-point analysis results when the user is stressed. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into a generation AI and have the generation AI adjust the way the analysis is presented.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis of data with high importance. The analysis unit can also perform a concise analysis of data with low importance. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This enables efficient analysis by adjusting 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 the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a music analysis algorithm to music data. The analysis unit can also apply an image analysis algorithm to image data. The analysis unit can also apply a video analysis algorithm to video data. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI and have the generation AI apply the analysis algorithm.

[0075] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide a visually appealing analysis result if the user is excited. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis based on the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0076] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also analyze older data as needed. Furthermore, the analysis unit can adjust the analysis priority based on the time when the data was collected. This enables efficient analysis by determining 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 the generation AI and have the generation AI determine the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can adjust the order of analysis according to the relevance of the data. This enables efficient analysis by adjusting 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 to the generation AI and have the generation AI adjust the order of analysis.

[0078] The providing unit can estimate the user's emotions and adjust the way the content is presented based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide the content using a calm expression. If the user is stressed, the providing unit can also provide the content using a simple, highly visible expression. Furthermore, if the user is excited, the providing unit can also provide the content using a visually stimulating expression. This allows more appropriate content to be provided by adjusting the way the content is presented based on the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into a generating AI and cause the generating AI to adjust the way the content is presented.

[0079] The providing unit can adjust the level of detail of the content provided based on the importance of the content when providing the content. For example, the providing unit provides detailed information for content with high importance. The providing unit can also provide concise information for content with low importance. Furthermore, the providing unit can adjust the level of detail of the content provided according to the importance. This enables efficient content provision by adjusting the level of detail of the content provided based on the importance of the content. 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 importance of the content to the generating AI and cause the generating AI to adjust the level of detail of the content provided.

[0080] The providing unit can apply different providing algorithms depending on the content category when providing the content. For example, the providing unit applies a music providing algorithm to music content. The providing unit can also apply an image providing algorithm to image content. The providing unit can also apply a video providing algorithm to video content. This improves the accuracy of provision by applying an appropriate providing algorithm depending on the content category. 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 content category to the generation AI and cause the generation AI to apply the providing algorithm.

[0081] The providing unit can estimate the user's emotions and adjust the length of the content to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide short, to-the-point content. Furthermore, if the user is relaxed, the providing unit can provide longer content with detailed explanations. Furthermore, if the user is excited, the providing unit can provide content with visually stimulating effects. By adjusting the length of the content based on the user's emotions, more appropriate content can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the length of the content.

[0082] The providing unit can determine the priority of provision based on the time of collection of content at the time of provision. For example, the providing unit provides the latest content preferentially. The providing unit can also provide older content as needed. Furthermore, the providing unit can adjust the priority of provision based on the time of collection of content. This enables efficient content provision by determining the priority of provision based on the time of collection of content. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the time of collection of content to the generation AI and have the generation AI determine the priority of provision.

[0083] The providing unit can adjust the order of content provision based on the relevance of the content when providing the content. For example, the providing unit provides highly relevant content preferentially. The providing unit can also postpone content with low relevance. Furthermore, the providing unit can adjust the order of content provision according to the relevance of the content. This enables efficient content provision by adjusting the order of content provision based on the relevance of the content. 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 relevance of content to a generating AI and cause the generating AI to adjust the order of content provision.

[0084] The AI ​​robot collaboration unit can estimate the user's emotions and adjust the content of the dialogue based on the estimated user's emotions. For example, if the user is feeling stressed, the AI ​​robot collaboration unit can provide a relaxing topic. Furthermore, if the user is relaxed, the AI ​​robot collaboration unit can also provide an interesting topic. Furthermore, if the user is excited, the AI ​​robot collaboration unit can also provide a calming topic. This makes it possible to provide a more appropriate dialogue by adjusting the content of the dialogue based on the user's emotions. Some or all of the above-mentioned processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the content of the dialogue.

[0085] During a dialogue, the AI ​​robot collaboration unit can select appropriate dialogue content by referring to the user's past dialogue history. The AI ​​robot collaboration unit selects dialogue content, for example, based on topics in which the user has shown interest in the past. The AI ​​robot collaboration unit can also select dialogue content based on topics that have made the user feel relaxed in the past. Furthermore, the AI ​​robot collaboration unit can analyze the user's past dialogue history and select the most effective dialogue content. In this way, the optimal dialogue content can be selected by referring to the user's past dialogue history. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's past dialogue history into the generation AI and cause the generation AI to select the dialogue content.

[0086] During dialogue, the AI ​​robot collaboration unit can customize the dialogue means based on the user's current living situation. For example, if the user is at work, the AI ​​robot collaboration unit can provide a short dialogue. Also, if the user is on vacation, the AI ​​robot collaboration unit can provide a relaxing dialogue. Furthermore, the AI ​​robot collaboration unit can customize the dialogue means (voice, text, etc.) according to the user's living situation. This allows for customizing the dialogue means based on the user's living situation, making it possible to provide a more appropriate dialogue. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's living situation data into the generation AI and cause the generation AI to customize the dialogue means.

[0087] The AI ​​robot collaboration unit can estimate the user's emotions and determine the priority of dialogues based on the estimated user's emotions. For example, if the user is feeling stressed, the AI ​​robot collaboration unit can prioritize providing relaxing dialogues. Furthermore, if the user is relaxed, the AI ​​robot collaboration unit can also prioritize providing interesting dialogues. Furthermore, if the user is excited, the AI ​​robot collaboration unit can also prioritize providing calm dialogues. In this way, by determining the priority of dialogues based on the user's emotions, more appropriate dialogues can be provided. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input user's emotion data into the generation AI and cause the generation AI to determine the priority of dialogues.

[0088] During a dialogue, the AI ​​robot collaboration unit can select appropriate dialogue content based on the user's geographical location information. For example, if the user is traveling, the AI ​​robot collaboration unit can provide topics related to the user's travel destination. Furthermore, if the user is at home, the AI ​​robot collaboration unit can also provide topics related to events and activities around the user's home. Furthermore, if the user is in a specific location, the AI ​​robot collaboration unit can also provide information related to that location. This makes it possible to select optimal dialogue content by taking the user's geographical location information into consideration. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's geographical location information to the generation AI and cause the generation AI to select dialogue content.

[0089] During a dialogue, the AI ​​robot collaboration unit can analyze the user's social media activity and suggest ways to communicate. For example, the AI ​​robot collaboration unit can provide topics related to content that the user frequently posts on social media. The AI ​​robot collaboration unit can also suggest ways to communicate based on the content posted by accounts the user follows. Furthermore, the AI ​​robot collaboration unit can also provide topics related to posts that the user has many likes or comments on. In this way, by analyzing the user's social media activity, it is possible to suggest optimal ways to communicate. Some or all of the above-described processing in the AI ​​robot collaboration unit may be performed using AI, for example, or may be performed without using AI. For example, the AI ​​robot collaboration unit can input the user's social media activity data into the generation AI and cause the generation AI to suggest ways to communicate. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and AI robot collaboration unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user data using the camera 42 and communication I / F 44 of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand the user's hobbies and preferences. The provision unit, for example, is realized by the control unit 46A of the smart device 14 and provides content based on the analysis results. The AI ​​robot collaboration unit, for example, is realized by the control unit 46A of the smart device 14 and the specific processing unit 290 of the data processing device 12 and provides advice and relaxation methods to reduce stress through dialogue with the user. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and AI robot collaboration 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 communication I / F 44 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to understand the user's hobbies and preferences. The provision unit, realized, for example, by the control unit 46A of the smart glasses 214, provides content based on the analysis results. The AI ​​robot collaboration unit, realized, for example, by the control unit 46A of the smart glasses 214 and the specific processing unit 290 of the data processing device 12, provides advice and relaxation methods for reducing stress through dialogue with the user. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and AI robot collaboration 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 communication I / F 44 of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to understand the user's hobbies and preferences. The provision unit, realized, for example, by the control unit 46A of the headset-type terminal 314, provides content based on the analysis results. The AI ​​robot collaboration unit, realized, for example, by the control unit 46A of the headset-type terminal 314 and the specific processing unit 290 of the data processing device 12, provides advice and relaxation methods for reducing stress through dialogue with the user. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, provision unit, and AI robot collaboration 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 communication I / F 44 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data to understand the user's hobbies and preferences. The provision unit, realized, for example, by the control unit 46A of the robot 414, provides content based on the analysis results. The AI ​​robot collaboration unit, realized, for example, by the control unit 46A of the robot 414 and the specific processing unit 290 of the data processing device 12, provides advice and relaxation methods for reducing stress through dialogue with the user.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be analyzed with priority. If the user is relaxed, data related to hobbies and interests can be analyzed with priority. Furthermore, if the user is excited, data related to entertainment can be analyzed with priority. Thus, by determining the priority of analysis based on the user's emotions, more appropriate data analysis is possible.

[0092] The providing unit can also estimate the user's emotions and adjust the type of content to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, relaxing music or videos can be provided. If the user is relaxed, content related to hobbies can be provided. Furthermore, if the user is excited, content with a high level of entertainment can be provided. In this way, by adjusting the type of content based on the user's emotions, more appropriate content can be provided.

[0093] The collection unit can also estimate the user's emotions and adjust the frequency of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced, and if the user is relaxed, the frequency of data collection can be increased. Also, if the user is busy, data collection can be temporarily stopped and resumed when the user has calmed down. In this way, by adjusting the frequency of data collection according to the user's emotions, more appropriate data can be collected.

[0094] The providing unit can also estimate the user's emotions and adjust the timing of content to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, relaxing content can be provided immediately. Also, if the user is relaxed, content can be provided periodically. Furthermore, if the user is excited, highly entertaining content can be provided at an appropriate time. In this way, by adjusting the timing of content provision based on the user's emotions, more appropriate content can be provided.

[0095] The AI ​​robot collaboration unit can also estimate the user's emotions and adjust the tone of the conversation based on the estimated user emotions. For example, if the user is feeling stressed, the conversation can be conducted in a calm and soothing tone. If the user is relaxed, the conversation can be conducted in a friendly and light-hearted tone. Furthermore, if the user is excited, the conversation can be conducted in an energetic and lively tone. This makes it possible to provide a more appropriate conversation by adjusting the tone of the conversation based on the user's emotions.

[0096] The collection unit can also analyze the user's past data collection history and predict the optimal timing for data collection. For example, it predicts the timing for the next data collection based on the time periods during which the user previously permitted data collection. It can also increase the success rate of data collection by avoiding time periods during which the user previously refused data collection. It can also predict the optimal timing for data collection based on the user's lifestyle rhythm. This makes it possible to collect data more efficiently by utilizing the user's past data collection history.

[0097] The analysis unit can also select an analysis method based on the source of data collection. For example, a smartphone-specific analysis method can be applied to data collected from a smartphone. Also, a social networking site-specific analysis method can be applied to data collected from a music app. In this way, the accuracy of the analysis can be improved by selecting an appropriate analysis method depending on the source of data collection.

[0098] The providing unit can also analyze the user's past content usage history and predict the optimal timing for providing content. For example, the next timing for providing content can be predicted based on the time period in which the user used content in the past. Also, by avoiding time periods in which the user did not use content in the past, the success rate of content provision can be increased. Furthermore, the optimal timing for providing content can be predicted based on the user's lifestyle rhythm. This makes it possible to provide content more efficiently by utilizing the user's past content usage history.

[0099] The AI ​​robot collaboration unit can also analyze the user's past conversation history and predict the optimal timing for conversation. For example, it can predict the next conversation timing based on the time of day when the user has previously had a conversation. It can also increase the success rate of a conversation by avoiding time periods when the user has previously refused a conversation. It can also predict the optimal timing for a conversation based on the user's daily rhythm. This makes it possible to utilize past conversation history to enable more efficient conversations.

[0100] The collection unit can also customize the data collection method based on the user's geographical location information. For example, if the user is traveling, tourist information and event information for the travel destination can be collected with priority. Also, if the user is at home, news and weather information for the area around the user's home can be collected with priority. Furthermore, if the user is in a specific location, data related to that location can be collected with priority. This allows more relevant data to be collected taking the user's geographical location information into consideration.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects user data. User data includes behavioral data, location information, purchase history, family composition, hobbies and interests, photos taken with a smartphone, content posted to social media, and songs played in music apps. The collection unit collects this data with the user's permission. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the user's hobbies and preferences. Hobbies and preferences include music genres, types of movies, and sports preferences. The analysis unit uses AI to analyze the data and identify the genres of music the user often listens to and the types of images and videos they often view. Step 3: The provider provides content based on the hobbies and preferences identified by the analysis unit. Content includes music, videos, articles, images, videos, and comics posted on social media. The provider uses AI to select content and provide it to users on a regular basis. Step 4: The AI ​​robot collaboration unit reduces stress through the content and dialogue provided by the provider unit. The AI ​​robot collaboration unit provides advice and relaxation methods to reduce stress through dialogue with the user. It uses AI to estimate the user's emotions and provides appropriate advice and relaxation methods.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user data; an analysis unit that analyzes the data collected by the collection unit and grasps the user's hobbies and preferences; a providing unit that provides content based on the hobbies and interests grasped by the analyzing unit; An AI robot collaboration unit that reduces stress through the content or dialogue provided by the provision unit; Equipped with A system characterized by:

2. The collecting unit Collect data such as the user's family structure, hobbies, and preferences from their smartphone or each app. The system of claim 1 .

3. The analysis unit Analyzing the collected data to understand the genre of music you listen to or the types of images or videos you view The system of claim 1 .

4. The providing unit Based on the analysis results, we will periodically provide music, images, videos, comics posted on social media, or other content. The system of claim 1 .

5. The AI ​​robot collaboration unit Interacting with users to provide advice or relaxation techniques to reduce stress The system of claim 1 .

6. The AI ​​robot collaboration unit Play relaxing music when the user is feeling stressed The system of claim 1 .

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

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

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

10. The collecting unit Estimate user emotions and prioritize data collection based on the estimated user emotions The system of claim 1 .

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