system

Abstract

The system according to this embodiment aims to allow users to visually and audibly review their daily digital experiences. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's actions on their smartphone. The analysis unit analyzes the actions data collected by the collection unit. The generation unit generates a video based on the data analyzed by the analysis unit. The provision unit provides the video generated by the generation unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is no opportunity for a user to visually and auditorily review their digital experience of the day, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user to visually and auditorily review their digital experience of the day.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's actions on their smartphone. The analysis unit analyzes the actions data collected by the collection unit. The generation unit generates a video based on the data analyzed by the analysis unit. The provision unit provides the video generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows a user to visually and audibly review their daily digital experiences. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An entertainment service according to an embodiment of the present invention is a system for reviewing daily digital experiences via a smartphone. This system collects actions performed by the user on their smartphone, analyzes them using a generating AI, and generates a video similar to a movie trailer, allowing the user to visually and audibly review their day. For example, actions performed by the user on their smartphone include health management, games, searching, taking photos, shopping, and using messaging apps. This behavioral data is automatically collected by the generating AI. Next, based on the collected behavioral data, the generating AI generates a video similar to a movie trailer. The generating AI analyzes the collected data and processes it so that the user can visually and audibly review their daily digital experiences. For example, this may include scenes of chatting with friends on a messaging app, shopping using online payment, or browsing the news. This video compresses and condenses the user's daily digital experiences into a single video, similar to a movie trailer. The generated video is designed so that the user can visually and audibly enjoy reviewing their day. For example, the user's facial expressions and actions within the video are presented in a way that resembles a movie trailer. This allows users to reflect on their day from an objective perspective and gain new insights. This service is particularly useful for heavy smartphone users and is expected to be offered as an app or have an entry point on the top page of messaging apps or portal sites. For example, if 100 million users use this service, it is expected to generate 100 million yen in revenue by adding just 1 yen to the annual communication fee. This allows entertainment services to allow users to visually and aurally review their daily digital experiences.

[0029] The entertainment service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavior on their smartphone. The collection unit collects data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. The collection unit can automatically collect this data using a generation AI. The analysis unit analyzes the behavioral data collected by the collection unit. The analysis unit processes the data so that the user can visually and audibly review their daily digital experience, for example, using methods such as data classification, pattern recognition, and statistical analysis. The analysis unit can analyze the collected data using a generation AI and extract the user's behavioral patterns. The generation unit generates a video based on the data analyzed by the analysis unit. The generation unit can compress and condense the user's daily digital experience into a single video, like a movie trailer, based on the collected data. The generation unit can analyze the collected data using a generation AI and generate a video that visually and audibly represents the user's behavior. The providing unit provides the user with the video generated by the generating unit. The providing unit designs the generated video so that the user can visually and aurally enjoy it while reviewing their day. The providing unit can use generation AI to optimize how the generated video is provided to the user. As a result, the entertainment service according to the embodiment allows the user to visually and aurally review their day's digital experiences.

[0030] The data collection unit collects information about the user's activities on their smartphone. For example, it collects data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. Specifically, for health management app usage history, it collects detailed data such as when the user exercised, the amount of exercise, heart rate, and calorie consumption. For game play history, it records which games the user played, for how long, their progress within the game, and achievements. For search history, it collects information such as the keywords the user searched for and which websites they visited. For photos and videos taken, it collects detailed information including metadata such as the date and time of shooting, location, and type of subject. For shopping history, it collects information such as which products the user purchased, the date and time of purchase, the purchase price, and where they were purchased. For messaging app usage history, it collects information such as which apps the user used, the number of messages sent and received, and the content of the conversations. The data collection unit can automatically collect this data using generative AI. The generation AI operates on the user's smartphone, extracting necessary data from each application and sending it to a central database. This allows the collection unit to efficiently and accurately collect diverse user behavior data and provide it to the analysis and generation units. Furthermore, the collection unit uses encryption technology in data collection and transmission to protect user privacy and ensure data security.

[0031] The analysis unit analyzes the behavioral data collected by the collection unit. The analysis unit processes the data in a way that allows users to visually and aurally review their daily digital experiences, using methods such as data classification, pattern recognition, and statistical analysis. Specifically, data classification organizes the collected data into categories such as health management, games, search, shopping, and messaging. Pattern recognition extracts user behavior patterns, analyzing, for example, tendencies to use specific apps at specific times or to engage in specific activities on specific days of the week. Statistical analysis calculates the frequency and time allocation of user behaviors, as well as the usage rate of each category, based on the collected data. The analysis unit can also use generative AI to analyze the collected data and extract user behavior patterns. Generative AI uses machine learning algorithms to automatically detect significant patterns and trends from large amounts of data. For example, it can detect patterns such as a user using a health management app at a certain time every morning, followed by playing a specific game. Furthermore, based on user behavior data, the analysis unit infers user interests and preferences and provides foundational data for suggesting personalized entertainment content. This allows the analysis unit to analyze the user's digital experience in detail and provide the generation unit with information to generate effective content.

[0032] The generation unit generates videos based on data analyzed by the analysis unit. For example, the generation unit compresses and condenses a user's daily digital experience into a single video, similar to a movie trailer, based on the collected data. Specifically, it organizes the user's behavioral data chronologically and extracts important events and highlights. For example, it picks out the user's main activities of the day, such as morning exercise, midday gaming, afternoon shopping, and evening messaging. Using generation AI, the generation unit can analyze the collected data and generate videos that visually and audibly represent the user's behavior. The generation AI uses image recognition and speech synthesis technology to realistically reproduce the user's behavior. For example, it combines photos and videos taken by the user to visually represent the events of the day, and adds background music and narration to create content that is also enjoyable audibly. Furthermore, the generation unit can customize the video style and theme according to the user's preferences. For example, if the user likes action movies, action movie-style effects and music can be incorporated into the video editing. This allows the generation unit to deliver a user's daily digital experience as engaging video content, enabling users to look back on it while enjoying themselves.

[0033] The delivery unit provides users with videos generated by the generation unit. For example, the delivery unit designs the generated videos so that users can visually and aurally enjoy them while reflecting on their day. Specifically, it designs the video playback interface to be user-friendly, allowing for easy playback, pause, rewind, and fast-forwarding. The delivery unit can use generation AI to optimize how generated videos are delivered to users. The generation AI learns the optimal delivery method based on user viewing history and feedback, and customizes it to the user's preferences. For example, if a user frequently watches videos at night, the delivery unit will deliver videos via push notifications at night. Also, if a user frequently watches videos on a specific device (smartphone, tablet, PC, etc.), the delivery unit will provide videos in a format optimized for that device. Furthermore, the delivery unit collects user feedback to improve the video content and delivery method. For example, if a user likes a particular scene, that scene can be highlighted, or similar content can be suggested. This allows the delivery unit to deliver videos to users in the most optimal way, enhancing the entertainment experience.

[0034] The data collection unit can collect data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. For example, the data collection unit can collect data such as steps taken, heart rate, and calorie consumption as part of the health management app usage history. The data collection unit can also collect data such as play time, score, and characters used as part of the game play history. The data collection unit can also collect data such as search keywords, search date and time, and clicks on search results as part of the search history. The data collection unit can also collect data such as the date and time of shooting, location, and metadata as part of the photos and videos taken. The data collection unit can also collect data such as purchased items, purchase date and time, and purchase amount as part of the shopping history. The data collection unit can also collect data such as the content of sent and received messages, sending date and time, and recipients as part of the messaging app usage history. This allows the data collection unit to review the user's digital experience in more detail by collecting diverse behavioral data. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, the data collection unit can input user behavior data from their smartphone into a generating AI, which can then automatically collect the data.

[0035] The analysis unit can analyze the collected data and process it so that users can visually and aurally review their daily digital experience. The analysis unit analyzes the collected data using methods such as data classification, pattern recognition, and statistical analysis. As data classification, the analysis unit can classify the collected data into categories. As pattern recognition, the analysis unit can extract user behavior patterns from the collected data. As statistical analysis, the analysis unit can analyze the statistical characteristics of the collected data. In this way, the analysis unit can analyze the collected data and process it so that users can visually and aurally review it, thereby enabling them to gain a deeper understanding of their daily digital experience. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then automatically analyze the data.

[0036] The generation unit can compress and condense a user's daily digital experience into a single video, much like a movie trailer, based on the collected data. For example, the generation unit can analyze the collected data, extract important scenes, and generate a video. To save time, the generation unit can compress the collected data and generate a video that can be viewed in a short amount of time. To extract important scenes, the generation unit can select particularly important scenes from the user's actions and generate a video based on them. In this way, the generation unit compresses and condenses a user's daily digital experience into a single video, much like a movie trailer, allowing the user to review their day in a short amount of time. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the collected data into a generation AI, and the generation AI can automatically generate a video.

[0037] The service provider can design the generated video so that users can look back on their day while enjoying it visually and aurally. For example, the service provider can add effects to the generated video to make it visually enjoyable. The service provider can select music that is appropriate for the video to make it aurally enjoyable. The service provider can add visual effects to the video to make it more appealing. The service provider can select music that matches the content of the video to provide a video that is visually and aurally enjoyable. In this way, the service provider can design the generated video so that users can look back on their day as entertainment, while enjoying it visually and aurally. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the generated video into a generation AI, and the generation AI can automatically add effects and music.

[0038] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data from apps that the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and select the most efficient data collection method. The data collection unit can also collect data from the user's past behavior history at specific time periods. This allows the data collection unit to select the optimal data collection method by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's past behavior data into a generative AI, which can then automatically select a data collection method.

[0039] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's currently used applications. The data collection unit can also prioritize collecting data related to the user's areas of interest. The data collection unit can also filter out unnecessary data based on the user's current activities. This allows the data collection unit to collect more relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's current activity data into a generative AI, which can then automatically filter the data.

[0040] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize collecting data related to that location. If the user is on the move, the data collection unit can also prioritize collecting data related to the user's destination. If the user is at home, the data collection unit can also prioritize collecting data related to the user's home. In this way, the data collection unit can collect more useful data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI, which can then automatically collect highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze a user's social media friendships and collect relevant data. The data collection unit can also analyze the content of a user's social media posts and collect relevant data. In this way, the data collection unit can efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI, and the generative AI can automatically collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can automatically adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health management data. The analysis unit can also apply game-related analysis algorithms to game data. The analysis unit can also apply shopping-related analysis algorithms to shopping data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can automatically apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis according to the data collection period. As a result, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the data collection period into the generation AI, and the generation AI can automatically determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can automatically adjust the order of analysis.

[0046] The generation unit can adjust the level of detail in a video based on the importance of the data during video generation. For example, the generation unit can generate a detailed video for high-importance data. It can also generate a simplified video for low-importance data. The generation unit can also determine the priority of videos according to the importance of the data. This allows the generation unit to efficiently generate videos by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can automatically adjust the level of detail in the video.

[0047] The generation unit can apply different generation algorithms depending on the data category when generating videos. For example, the generation unit can apply a health-related generation algorithm to health management data. The generation unit can also apply a game-related generation algorithm to game data. The generation unit can also apply a shopping-related generation algorithm to shopping data. By applying different generation algorithms depending on the data category, the generation unit can generate videos with higher accuracy. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI, and the generation AI can automatically apply an appropriate generation algorithm.

[0048] The generation unit can determine the priority of videos based on the data collection timing when generating videos. For example, the generation unit can prioritize the inclusion of the latest data in the videos. The generation unit can also incorporate the latest data into the videos while referring to past data. The generation unit can also determine the priority of videos according to the data collection timing. This allows the generation unit to prioritize the inclusion of the latest data in the videos by determining the priority of videos based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data collection timing into the generation AI, and the generation AI can automatically determine the priority of videos.

[0049] The generation unit can adjust the order of videos based on the relevance of the data during video generation. For example, the generation unit can prioritize reflecting highly relevant data in the video. The generation unit can also postpone less relevant data. The generation unit can also adjust the order of videos according to the relevance of the data. In this way, the generation unit can generate more relevant videos by adjusting the order of videos based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI, and the generation AI can automatically adjust the order of the videos.

[0050] The service provider can select the optimal delivery method by referring to the user's past viewing history when delivering videos. For example, the service provider can analyze the trends of videos the user has watched in the past and select the optimal delivery method. The service provider can also prioritize delivering genres preferred by the user based on their past viewing history. The service provider can also select the optimal delivery timing based on the user's viewing history. In this way, the service provider can select the optimal delivery method by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's viewing history data into a generation AI, and the generation AI can automatically select the optimal delivery method.

[0051] The delivery unit can select the optimal delivery method based on the user's device information when delivering videos. For example, if the user is using a smartphone, the delivery unit will select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can also select a delivery method optimized for a larger screen. If the user is using a smartwatch, the delivery unit can also select a concise and highly visible delivery method. In this way, the delivery unit can deliver videos in a more appropriate manner by selecting the optimal delivery method based on the user's device information. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input the user's device information into a generation AI, and the generation AI can automatically select the optimal delivery method.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The data collection unit can collect not only user behavior data but also user environment data. For example, the data collection unit can collect environmental data such as weather information, temperature, and humidity of the user's location. By combining and analyzing user behavior data and environmental data, a more detailed digital experience can be provided. Furthermore, the data collection unit can also collect data such as the intensity of sound and light around the user. This allows for an understanding of the environment in which the user's actions took place, providing a more realistic review. Some or all of the processing described above in the data collection unit may be performed using generative AI, or it may be performed without using generative AI.

[0054] The analysis unit can estimate the user's health status based on the user's behavioral data. For example, the analysis unit can analyze data such as the user's steps, heart rate, and calorie consumption to evaluate the user's health status. Furthermore, the analysis unit can analyze the user's sleep data to evaluate the quality of sleep. This allows the user to reflect on their health status and identify areas for improvement. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0055] The generation unit can predict a user's future behavior based on user behavior data. For example, the generation unit can analyze a user's past behavior patterns and predict their behavior for the next day. Furthermore, the generation unit can also predict future behavior based on the user's schedule and calendar data. This allows the user to predict their future behavior and make plans. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0056] The provider can add interactive elements to the generated video. For example, the provider can add a function that displays detailed information when the user clicks on a specific scene in the video. Furthermore, the provider can also provide a function that allows the user to select scenes in the video and change the playback order. This allows the user to customize the video to their liking and enjoy it more. Some or all of the above processing in the provider may be performed using a generation AI, or it may be performed without a generation AI.

[0057] The service provider can provide a function to share the generated videos on social media. For example, the service provider can add a function that allows users to easily post the generated videos to social media. Furthermore, the service provider can also provide a function to add tags and comments to the videos. This allows users to share their digital experiences with others and deepen their interactions. Some or all of the above processing in the service provider may be performed using or without a generative AI.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The data collection unit collects the user's activity on their smartphone. The data collection unit collects data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. The data collection unit can automatically collect this data using generative AI. Step 2: The analysis unit analyzes the behavioral data collected by the collection unit. The analysis unit processes the data in a way that allows users to visually and audibly review their daily digital experiences, for example, using methods such as data classification, pattern recognition, and statistical analysis. The analysis unit can use generative AI to analyze the collected data and extract user behavioral patterns. Step 3: The generation unit generates a video based on the data analyzed by the analysis unit. For example, the generation unit can compress and condense a user's daily digital experience into a single video, similar to a movie trailer, based on the collected data. Using generation AI, the generation unit can analyze the collected data and generate a video that visually and audibly represents the user's behavior. Step 4: The delivery unit provides the user with the video generated by the generation unit. The delivery unit designs the generated video so that the user can visually and aurally enjoy it while reviewing their day. The delivery unit can use generation AI to optimize how the generated video is delivered to the user.

[0060] (Example of form 2) An entertainment service according to an embodiment of the present invention is a system for reviewing daily digital experiences via a smartphone. This system collects actions performed by the user on their smartphone, analyzes them using a generating AI, and generates a video similar to a movie trailer, allowing the user to visually and audibly review their day. For example, actions performed by the user on their smartphone include health management, games, searching, taking photos, shopping, and using messaging apps. This behavioral data is automatically collected by the generating AI. Next, based on the collected behavioral data, the generating AI generates a video similar to a movie trailer. The generating AI analyzes the collected data and processes it so that the user can visually and audibly review their daily digital experiences. For example, this may include scenes of chatting with friends on a messaging app, shopping using online payment, or browsing the news. This video compresses and condenses the user's daily digital experiences into a single video, similar to a movie trailer. The generated video is designed so that the user can visually and audibly enjoy reviewing their day. For example, the user's facial expressions and actions within the video are presented in a way that resembles a movie trailer. This allows users to reflect on their day from an objective perspective and gain new insights. This service is particularly useful for heavy smartphone users and is expected to be offered as an app or have an entry point on the top page of messaging apps or portal sites. For example, if 100 million users use this service, it is expected to generate 100 million yen in revenue by adding just 1 yen to the annual communication fee. This allows entertainment services to allow users to visually and aurally review their daily digital experiences.

[0061] The entertainment service according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavior on their smartphone. The collection unit collects data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. The collection unit can automatically collect this data using a generation AI. The analysis unit analyzes the behavioral data collected by the collection unit. The analysis unit processes the data so that the user can visually and audibly review their daily digital experience, for example, using methods such as data classification, pattern recognition, and statistical analysis. The analysis unit can analyze the collected data using a generation AI and extract the user's behavioral patterns. The generation unit generates a video based on the data analyzed by the analysis unit. The generation unit can compress and condense the user's daily digital experience into a single video, like a movie trailer, based on the collected data. The generation unit can analyze the collected data using a generation AI and generate a video that visually and audibly represents the user's behavior. The providing unit provides the user with the video generated by the generating unit. The providing unit designs the generated video so that the user can visually and aurally enjoy it while reviewing their day. The providing unit can use generation AI to optimize how the generated video is provided to the user. As a result, the entertainment service according to the embodiment allows the user to visually and aurally review their day's digital experiences.

[0062] The data collection unit collects information about the user's activities on their smartphone. For example, it collects data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. Specifically, for health management app usage history, it collects detailed data such as when the user exercised, the amount of exercise, heart rate, and calorie consumption. For game play history, it records which games the user played, for how long, their progress within the game, and achievements. For search history, it collects information such as the keywords the user searched for and which websites they visited. For photos and videos taken, it collects detailed information including metadata such as the date and time of shooting, location, and type of subject. For shopping history, it collects information such as which products the user purchased, the date and time of purchase, the purchase price, and where they were purchased. For messaging app usage history, it collects information such as which apps the user used, the number of messages sent and received, and the content of the conversations. The data collection unit can automatically collect this data using generative AI. The generation AI operates on the user's smartphone, extracting necessary data from each application and sending it to a central database. This allows the collection unit to efficiently and accurately collect diverse user behavior data and provide it to the analysis and generation units. Furthermore, the collection unit uses encryption technology in data collection and transmission to protect user privacy and ensure data security.

[0063] The analysis unit analyzes the behavioral data collected by the collection unit. The analysis unit processes the data in a way that allows users to visually and aurally review their daily digital experiences, using methods such as data classification, pattern recognition, and statistical analysis. Specifically, data classification organizes the collected data into categories such as health management, games, search, shopping, and messaging. Pattern recognition extracts user behavior patterns, analyzing, for example, tendencies to use specific apps at specific times or to engage in specific activities on specific days of the week. Statistical analysis calculates the frequency and time allocation of user behaviors, as well as the usage rate of each category, based on the collected data. The analysis unit can also use generative AI to analyze the collected data and extract user behavior patterns. Generative AI uses machine learning algorithms to automatically detect significant patterns and trends from large amounts of data. For example, it can detect patterns such as a user using a health management app at a certain time every morning, followed by playing a specific game. Furthermore, based on user behavior data, the analysis unit infers user interests and preferences and provides foundational data for suggesting personalized entertainment content. This allows the analysis unit to analyze the user's digital experience in detail and provide the generation unit with information to generate effective content.

[0064] The generation unit generates videos based on data analyzed by the analysis unit. For example, the generation unit compresses and condenses a user's daily digital experience into a single video, similar to a movie trailer, based on the collected data. Specifically, it organizes the user's behavioral data chronologically and extracts important events and highlights. For example, it picks out the user's main activities of the day, such as morning exercise, midday gaming, afternoon shopping, and evening messaging. Using generation AI, the generation unit can analyze the collected data and generate videos that visually and audibly represent the user's behavior. The generation AI uses image recognition and speech synthesis technology to realistically reproduce the user's behavior. For example, it combines photos and videos taken by the user to visually represent the events of the day, and adds background music and narration to create content that is also enjoyable audibly. Furthermore, the generation unit can customize the video style and theme according to the user's preferences. For example, if the user likes action movies, action movie-style effects and music can be incorporated into the video editing. This allows the generation unit to deliver a user's daily digital experience as engaging video content, enabling users to look back on it while enjoying themselves.

[0065] The delivery unit provides users with videos generated by the generation unit. For example, the delivery unit designs the generated videos so that users can visually and aurally enjoy them while reflecting on their day. Specifically, it designs the video playback interface to be user-friendly, allowing for easy playback, pause, rewind, and fast-forwarding. The delivery unit can use generation AI to optimize how generated videos are delivered to users. The generation AI learns the optimal delivery method based on user viewing history and feedback, and customizes it to the user's preferences. For example, if a user frequently watches videos at night, the delivery unit will deliver videos via push notifications at night. Also, if a user frequently watches videos on a specific device (smartphone, tablet, PC, etc.), the delivery unit will provide videos in a format optimized for that device. Furthermore, the delivery unit collects user feedback to improve the video content and delivery method. For example, if a user likes a particular scene, that scene can be highlighted, or similar content can be suggested. This allows the delivery unit to deliver videos to users in the most optimal way, enhancing the entertainment experience.

[0066] The data collection unit can collect data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. For example, the data collection unit can collect data such as steps taken, heart rate, and calorie consumption as part of the health management app usage history. The data collection unit can also collect data such as play time, score, and characters used as part of the game play history. The data collection unit can also collect data such as search keywords, search date and time, and clicks on search results as part of the search history. The data collection unit can also collect data such as the date and time of shooting, location, and metadata as part of the photos and videos taken. The data collection unit can also collect data such as purchased items, purchase date and time, and purchase amount as part of the shopping history. The data collection unit can also collect data such as the content of sent and received messages, sending date and time, and recipients as part of the messaging app usage history. This allows the data collection unit to review the user's digital experience in more detail by collecting diverse behavioral data. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI. For example, the data collection unit can input user behavior data from their smartphone into a generating AI, which can then automatically collect the data.

[0067] The analysis unit can analyze the collected data and process it so that users can visually and aurally review their daily digital experience. The analysis unit analyzes the collected data using methods such as data classification, pattern recognition, and statistical analysis. As data classification, the analysis unit can classify the collected data into categories. As pattern recognition, the analysis unit can extract user behavior patterns from the collected data. As statistical analysis, the analysis unit can analyze the statistical characteristics of the collected data. In this way, the analysis unit can analyze the collected data and process it so that users can visually and aurally review it, thereby enabling them to gain a deeper understanding of their daily digital experience. Some or all of the above processing in the analysis unit may be performed using generative AI, or it may be performed without generative AI. For example, the analysis unit can input the collected data into a generative AI, which can then automatically analyze the data.

[0068] The generation unit can compress and condense a user's daily digital experience into a single video, much like a movie trailer, based on the collected data. For example, the generation unit can analyze the collected data, extract important scenes, and generate a video. To save time, the generation unit can compress the collected data and generate a video that can be viewed in a short amount of time. To extract important scenes, the generation unit can select particularly important scenes from the user's actions and generate a video based on them. In this way, the generation unit compresses and condenses a user's daily digital experience into a single video, much like a movie trailer, allowing the user to review their day in a short amount of time. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the collected data into a generation AI, and the generation AI can automatically generate a video.

[0069] The service provider can design the generated video so that users can look back on their day while enjoying it visually and aurally. For example, the service provider can add effects to the generated video to make it visually enjoyable. The service provider can select music that is appropriate for the video to make it aurally enjoyable. The service provider can add visual effects to the video to make it more appealing. The service provider can select music that matches the content of the video to provide a video that is visually and aurally enjoyable. In this way, the service provider can design the generated video so that users can look back on their day as entertainment, while enjoying it visually and aurally. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the generated video into a generation AI, and the generation AI can automatically add effects and music.

[0070] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during times when the user is relaxed. If the user is excited, the data collection unit can also collect data after the excitement has subsided. If the user is tired, the data collection unit can also collect data during rest. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the timing of data collection.

[0071] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit may prioritize collecting data from apps that the user has frequently used in the past. The data collection unit can also analyze the user's past behavior patterns and select the most efficient data collection method. The data collection unit can also collect data from the user's past behavior history at specific time periods. This allows the data collection unit to select the optimal data collection method by analyzing the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's past behavior data into a generative AI, which can then automatically select a data collection method.

[0072] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's currently used applications. The data collection unit can also prioritize collecting data related to the user's areas of interest. The data collection unit can also filter out unnecessary data based on the user's current activities. This allows the data collection unit to collect more relevant data by filtering data based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's current activity data into a generative AI, which can then automatically filter the data.

[0073] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting data related to relaxation. If the user is excited, the data collection unit may also prioritize collecting data related to excitement. If the user is tired, the data collection unit may also prioritize collecting data related to rest. In this way, the data collection unit can prioritize collecting more important data by determining the priority of data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can automatically estimate emotions and determine the priority of data.

[0074] The data collection unit can prioritize collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific location, the data collection unit will prioritize collecting data related to that location. If the user is on the move, the data collection unit can also prioritize collecting data related to the user's destination. If the user is at home, the data collection unit can also prioritize collecting data related to the user's home. In this way, the data collection unit can collect more useful data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's geographical location data into a generative AI, which can then automatically collect highly relevant data.

[0075] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on information shared by the user on social media. The data collection unit can also analyze a user's social media friendships and collect relevant data. The data collection unit can also analyze the content of a user's social media posts and collect relevant data. In this way, the data collection unit can efficiently collect relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media data into a generative AI, and the generative AI can automatically collect relevant data.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will display the analysis results in a relaxed manner. If the user is excited, the analysis unit can also display the analysis results in a manner that reflects the excitement. If the user is tired, the analysis unit can also display the analysis results in a simple and highly visible manner. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can automatically estimate the emotions and adjust the presentation of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI, and the generative AI can automatically adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply health-related analysis algorithms to health management data. The analysis unit can also apply game-related analysis algorithms to game data. The analysis unit can also apply shopping-related analysis algorithms to shopping data. This allows the analysis unit to perform more accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the data category into a generative AI, and the generative AI can automatically apply an appropriate analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit will prioritize analyzing data related to relaxation. If the user is excited, the analysis unit may also prioritize analyzing data related to excitement. If the user is tired, the analysis unit may also prioritize analyzing data related to rest. In this way, the analysis unit can prioritize the analysis of more important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can automatically estimate emotions and determine the priority of analysis.

[0080] The analysis unit can determine the priority of analysis based on the data collection period during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also determine the priority of analysis according to the data collection period. As a result, the analysis unit can prioritize the analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the data collection period into the generation AI, and the generation AI can automatically determine the priority of analysis.

[0081] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. The analysis unit may also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows the analysis unit to perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI, and the generative AI can automatically adjust the order of analysis.

[0082] The generation unit can estimate the user's emotions and adjust the expression of the video generation based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate a video with a relaxed expression. If the user is excited, the generation unit can also generate a video with an expression that reflects the excitement. If the user is tired, the generation unit can also generate a video with a simple and highly visible expression. In this way, the generation unit can generate more appropriate videos by adjusting the expression of the video generation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can automatically estimate the emotions and adjust the expression of the video generation.

[0083] The generation unit can adjust the level of detail in a video based on the importance of the data during video generation. For example, the generation unit can generate a detailed video for high-importance data. It can also generate a simplified video for low-importance data. The generation unit can also determine the priority of videos according to the importance of the data. This allows the generation unit to efficiently generate videos by adjusting the level of detail based on the importance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the importance of the data into the generation AI, and the generation AI can automatically adjust the level of detail in the video.

[0084] The generation unit can apply different generation algorithms depending on the data category when generating videos. For example, the generation unit can apply a health-related generation algorithm to health management data. The generation unit can also apply a game-related generation algorithm to game data. The generation unit can also apply a shopping-related generation algorithm to shopping data. By applying different generation algorithms depending on the data category, the generation unit can generate videos with higher accuracy. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the data category into the generation AI, and the generation AI can automatically apply an appropriate generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the video length based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a longer video. If the user is excited, the generation unit can also generate a shorter video. If the user is tired, the generation unit can generate a short, concise video. In this way, the generation unit can generate videos of a more appropriate length by adjusting the video length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit can input user emotion data into the generation AI, which can automatically estimate the emotions and adjust the video length.

[0086] The generation unit can determine the priority of videos based on the data collection timing when generating videos. For example, the generation unit can prioritize the inclusion of the latest data in the videos. The generation unit can also incorporate the latest data into the videos while referring to past data. The generation unit can also determine the priority of videos according to the data collection timing. This allows the generation unit to prioritize the inclusion of the latest data in the videos by determining the priority of videos based on the data collection timing. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the data collection timing into the generation AI, and the generation AI can automatically determine the priority of videos.

[0087] The generation unit can adjust the order of videos based on the relevance of the data during video generation. For example, the generation unit can prioritize reflecting highly relevant data in the video. The generation unit can also postpone less relevant data. The generation unit can also adjust the order of videos according to the relevance of the data. In this way, the generation unit can generate more relevant videos by adjusting the order of videos based on the relevance of the data. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the relevance of the data into the generation AI, and the generation AI can automatically adjust the order of the videos.

[0088] The service provider can estimate the user's emotions and adjust the video delivery method based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver the video in a relaxed atmosphere. If the user is excited, the service provider can deliver the video in an atmosphere that reflects the excitement. If the user is tired, the service provider can deliver the video in a simple and easy-to-understand manner. In this way, the service provider can deliver the video in a more appropriate way by adjusting the delivery method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the video delivery method.

[0089] The service provider can select the optimal delivery method by referring to the user's past viewing history when delivering videos. For example, the service provider can analyze the trends of videos the user has watched in the past and select the optimal delivery method. The service provider can also prioritize delivering genres preferred by the user based on their past viewing history. The service provider can also select the optimal delivery timing based on the user's viewing history. In this way, the service provider can select the optimal delivery method by referring to the user's past viewing history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's viewing history data into a generation AI, and the generation AI can automatically select the optimal delivery method.

[0090] The delivery unit can estimate the user's emotions and adjust the timing of video delivery based on the estimated emotions. For example, if the user is relaxed, the delivery unit will deliver the video during a relaxed time. If the user is excited, the delivery unit can deliver the video after the excitement has subsided. If the user is tired, the delivery unit can deliver the video during rest. In this way, the delivery unit can deliver videos at a more appropriate time by adjusting the timing of video delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using generative AI or not. For example, the delivery unit can input user emotion data into a generative AI, which can automatically estimate the emotions and adjust the timing of video delivery.

[0091] The delivery unit can select the optimal delivery method based on the user's device information when delivering videos. For example, if the user is using a smartphone, the delivery unit will select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can also select a delivery method optimized for a larger screen. If the user is using a smartwatch, the delivery unit can also select a concise and highly visible delivery method. In this way, the delivery unit can deliver videos in a more appropriate manner by selecting the optimal delivery method based on the user's device information. Some or all of the above processing in the delivery unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the delivery unit can input the user's device information into a generation AI, and the generation AI can automatically select the optimal delivery method.

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

[0093] The data collection unit can collect not only user behavior data but also user environment data. For example, the data collection unit can collect environmental data such as weather information, temperature, and humidity of the user's location. By combining and analyzing user behavior data and environmental data, a more detailed digital experience can be provided. Furthermore, the data collection unit can also collect data such as the intensity of sound and light around the user. This allows for an understanding of the environment in which the user's actions took place, providing a more realistic review. Some or all of the processing described above in the data collection unit may be performed using generative AI, or it may be performed without using generative AI.

[0094] The analysis unit can estimate the user's health status based on the user's behavioral data. For example, the analysis unit can analyze data such as the user's steps, heart rate, and calorie consumption to evaluate the user's health status. Furthermore, the analysis unit can analyze the user's sleep data to evaluate the quality of sleep. This allows the user to reflect on their health status and identify areas for improvement. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0095] The generation unit can predict a user's future behavior based on user behavior data. For example, the generation unit can analyze a user's past behavior patterns and predict their behavior for the next day. Furthermore, the generation unit can also predict future behavior based on the user's schedule and calendar data. This allows the user to predict their future behavior and make plans. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI.

[0096] The provider can add interactive elements to the generated video. For example, the provider can add a function that displays detailed information when the user clicks on a specific scene in the video. Furthermore, the provider can also provide a function that allows the user to select scenes in the video and change the playback order. This allows the user to customize the video to their liking and enjoy it more. Some or all of the above processing in the provider may be performed using a generation AI, or it may be performed without a generation AI.

[0097] The service provider can provide a function to share the generated videos on social media. For example, the service provider can add a function that allows users to easily post the generated videos to social media. Furthermore, the service provider can also provide a function to add tags and comments to the videos. This allows users to share their digital experiences with others and deepen their interactions. Some or all of the above processing in the service provider may be performed using or without a generative AI.

[0098] The data collection unit can estimate the user's emotions and adjust the content of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit will prioritize collecting stress-related data. If the user is relaxed, the data collection unit can also prioritize collecting relaxation-related data. In this way, the data collection unit can collect more relevant data by adjusting the content of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the data collection unit may be performed using generative AI, or it may be performed without using generative AI.

[0099] The analysis unit can estimate the user's emotions and adjust the timing of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit will perform the analysis during a relaxed period. If the user is excited, the analysis unit can also perform the analysis after the excitement has subsided. In this way, the analysis unit can perform the analysis at a more appropriate time by adjusting the timing of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without using generative AI.

[0100] The generation unit can estimate the user's emotions and adjust the video style based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a relaxed style video. If the user is excited, the generation unit can also generate a video style that reflects excitement. In this way, the generation unit can generate more appropriate videos by adjusting the video style based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI.

[0101] The service provider can estimate the user's emotions and customize how the video is delivered based on those emotions. For example, if the user is relaxed, the service provider can deliver the video in a relaxed atmosphere. If the user is excited, the service provider can deliver the video in an atmosphere that reflects that excitement. This allows the service provider to deliver videos in a more appropriate way by customizing how they are delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above-described processes in the service provider may be performed using generative AI or not.

[0102] The delivery unit can estimate the user's emotions and adjust the timing of video delivery based on the estimated emotions. For example, if the delivery unit is relaxed, it will deliver the video during a relaxed time. If the delivery unit is excited, it can also deliver the video after the excitement has subsided. In this way, the delivery unit can deliver videos at a more appropriate time by adjusting the timing of video delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Some or all of the above processing in the delivery unit may be performed using generative AI, or it may be performed without using generative AI.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The data collection unit collects the user's activity on their smartphone. The data collection unit collects data such as the usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and messaging app usage history. The data collection unit can automatically collect this data using generative AI. Step 2: The analysis unit analyzes the behavioral data collected by the collection unit. The analysis unit processes the data in a way that allows users to visually and audibly review their daily digital experiences, for example, using methods such as data classification, pattern recognition, and statistical analysis. The analysis unit can use generative AI to analyze the collected data and extract user behavioral patterns. Step 3: The generation unit generates a video based on the data analyzed by the analysis unit. For example, the generation unit can compress and condense a user's daily digital experience into a single video, similar to a movie trailer, based on the collected data. Using generation AI, the generation unit can analyze the collected data and generate a video that visually and audibly represents the user's behavior. Step 4: The delivery unit provides the user with the video generated by the generation unit. The delivery unit designs the generated video so that the user can visually and aurally enjoy it while reviewing their day. The delivery unit can use generation AI to optimize how the generated video is delivered to the user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects behavioral data on the user's smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavioral data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects behavioral data from the user's smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavioral data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed data. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects behavioral data on the user's smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the collected behavioral data. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a video based on the analyzed data. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects behavioral data from the user's smartphone. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected behavioral data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a video based on the analyzed data. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated video to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A data collection unit that collects user activity on their smartphones, An analysis unit analyzes the behavioral data collected by the aforementioned collection unit, A generation unit that generates a video based on the data analyzed by the analysis unit, The system includes a providing unit that provides the video generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects data such as usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and usage history of messaging apps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed and processed to allow users to visually and audibly review their daily digital experiences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on the collected data, the user's daily digital experience is compressed and abridged into a single, unified version, much like a movie trailer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The generated video is designed to allow users to reflect on their day while enjoying it visually and aurally. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the video's presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When generating a video, adjust the level of detail in the video based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating videos, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and adjusts the video length based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating videos, the priority of the videos is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating videos, the order of the videos is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts how videos are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing videos, the system selects the optimal delivery method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of video delivery based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing videos, the optimal delivery method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects user activity on their smartphones, An analysis unit analyzes the behavioral data collected by the aforementioned collection unit, A generation unit that generates a video based on the data analyzed by the analysis unit, The system includes a providing unit that provides the video generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is It collects data such as usage history of health management apps, game play history, search history, photos and videos taken, shopping history, and usage history of messaging apps. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed and processed to allow users to visually and audibly review their daily digital experiences. The system according to feature 1.

4. The generating unit is Based on the collected data, the user's daily digital experience is compressed and abridged into a single, unified version, much like a movie trailer. The system according to feature 1.

5. The aforementioned supply unit is, The generated video is designed to allow users to reflect on their day while enjoying it visually and aurally. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system according to feature 1.

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