system

Abstract

The system according to this embodiment aims to predict the user's future interests and generate AI animations that prompt the user to take the next action based on those predictions. [Solution] The system according to the embodiment comprises a collection unit, a prediction unit, and a generation unit. The collection unit collects tap information from the user's smartphone. The prediction unit predicts the user's future interests based on the tap information collected by the collection unit. The generation unit generates AI animations based on the future interests predicted by the prediction unit.

Description

Technical Field

[0006] , , ,

[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, including 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 a problem that it is difficult to accurately predict a user's interests and provide content that prompts the next action.

[0005] The system according to the embodiment aims to predict a user's future interests and generate an AI anime that prompts the next action based on that.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a prediction unit, and a generation unit. The collection unit collects tap information from the user's smartphone. The prediction unit predicts the user's future interests based on the tap information collected by the collection unit. The generation unit generates AI animations based on the future interests predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to this embodiment can predict the user's future interests and generate AI animations that prompt the user to take the next action based on those predictions. [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, etc. The communication I / F manages communications between multiple 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) The future-predicting AI animation generation system according to an embodiment of the present invention is a system that utilizes smartphone tap information to predict the user's future interests and generates AI animations that prompt the next action (purchase, study, travel) based on that prediction. The future-predicting AI animation generation system solves the problem that conventional recommended videos, which are based on search words and location information, can feel stalker-like and stressful to users. The future-predicting AI animation generation system solves this problem by utilizing smartphone tap information. First, the future-predicting AI animation generation system collects tap information from the user's smartphone. Specifically, it collects data such as tap time (in microseconds), the interval between taps, and the frequency of operations that are considered to be accidental taps. This data is not long-term, but only the most recent few data points are used. Next, the future-predicting AI animation generation system predicts the user's future interests based on the collected tap information. The prediction intentionally excludes content related to the user's searches and actions over the past few months. This eliminates the feeling of being stalked and makes it possible to predict the next action that the user had not yet thought of. Based on the prediction results, the future-predicting AI animation generation system generates an AI animation. The AI-generated animations are limited to themes that viewers can realistically act upon (purchase, study, travel) if they choose to do so. For example, short animations are generated on themes such as new hobbies, travel destinations, or learning materials that users might be interested in next. This system allows users to come up with their next desired action from the recommended videos. This leads users to feel a sense of new discovery and to actually take action. Furthermore, by accumulating this data, it can be used for zero-order analysis. Companies can use data from when viewers have not yet started anything to make optimal approaches. In addition, the generated AI animations are distributed as multilingual short videos on video content media. This allows them to be distributed globally as future-oriented recommended videos even without any past information on the viewer. This system can also be used to sell the analysis results for a fee to strategic departments in private companies and government agencies that are responsible for zero-order analysis.By using machine learning to track the process by which viewers perceive discovery, become interested, and take action, this can be utilized in strategic planning. This allows the future-predicting AI animation generation system to predict users' future interests and generate AI animations that prompt their next actions based on those predictions.

[0029] The AI ​​animation generation system according to the embodiment comprises a collection unit, a prediction unit, and a generation unit. The collection unit collects tap information from the user's smartphone. The collection unit collects data such as tap time (in microseconds), the interval between taps, and the frequency of operations considered to be accidental taps. The collection unit uses only the most recent few data points, not long-term data. The collection unit measures tap time in milliseconds and records the interval between taps. The collection unit can also count the frequency of operations considered to be accidental taps. The prediction unit predicts the user's future interests based on the tap information collected by the collection unit. The prediction unit makes predictions by intentionally excluding content related to the user's searches and actions over the past few months. This allows the prediction unit to eliminate the feeling of being stalked and predict the user's next actions that they had not yet thought of. The prediction unit makes predictions based only on tap information, for example, by excluding past search history and location information. The prediction unit can use AI to predict the user's future interests from the tap information. The generation unit generates AI animations based on future interests predicted by the prediction unit. The generation unit generates AI animations limited to themes that viewers might actually take action on (purchase, study, travel) in the future. For example, the generation unit generates short animations on themes such as new hobbies, travel destinations, or learning content that users might become interested in next. The generation unit can use AI to generate AI animations on themes that viewers are likely to take action on next. The generation unit can also generate the generated AI animations as short videos that support multiple languages. For example, the generation unit generates short videos that support multiple languages, such as English, Japanese, and French. The generation unit can also distribute the generated AI animations on video content media. For example, the generation unit widely distributes the generated AI animations through video content media. As a result, the future prediction AI animation generation system according to this embodiment can predict the user's future interests and generate AI animations that encourage the next action based on those predictions.

[0030] The data collection unit collects tap information from the user's smartphone. Specifically, the unit measures tap time in milliseconds and records the interval between taps. This allows for a detailed understanding of the user's operation patterns. Furthermore, the unit can also count the frequency of operations that are considered to be accidental taps. The frequency of accidental taps can serve as an indicator of fluctuations in the user's operation accuracy and attention span. The data collection unit uses only the most recent few data points, rather than long-term data. This enables real-time predictions based on the latest operation status. For example, the data collection unit measures the tap time in milliseconds when the user operates an application and records the interval between taps. In addition, by counting the frequency of operations that are considered to be accidental taps, it is possible to understand the user's operation patterns in detail. This allows the data collection unit to understand the user's operation patterns in real time and provide this information to the prediction unit. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the prediction and generation units. Also, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The prediction unit predicts the user's future interests based on tap information collected by the data collection unit. Specifically, the prediction unit intentionally excludes content related to the user's searches and actions over the past few months when making predictions. This eliminates the feeling of being stalked and allows for the prediction of the user's next actions that they may not have yet thought of. For example, the prediction unit excludes past search history and location information and makes predictions based solely on tap information. The prediction unit can use AI to predict the user's future interests from tap information. Specifically, the AI ​​analyzes data such as tap timing and intervals, and the frequency of accidental taps to learn the user's operation patterns. This allows for highly accurate prediction of themes and actions that the user is likely to be interested in next. Furthermore, the prediction unit can also predict long-term fluctuations in interests by utilizing past data and statistical information. For example, based on past tap information, it can predict fluctuations in interests during specific time periods or days of the week and predict future actions. In addition, the prediction unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the prediction unit to not only predict interests in real time, but also to handle long-term fluctuations in interests and anomaly detection, thereby improving the reliability and accuracy of the entire system.

[0032] The generation unit generates AI animations based on future interests predicted by the prediction unit. Specifically, the generation unit generates AI animations limited to themes that viewers are likely to take action on (purchase, study, travel) in real life. For example, it generates short animations on themes such as new hobbies, travel destinations, or learning content that users are likely to become interested in. The generation unit can use AI to generate AI animations on themes that viewers are likely to take action on. Specifically, the generation AI automatically generates storylines, character settings, and scene compositions based on interest data provided by the prediction unit. This allows for the creation of engaging animations that capture the user's interest in a short amount of time. Furthermore, the generation unit can also generate the generated AI animations as short videos with multilingual support. For example, it can generate short videos that support multiple languages ​​such as English, Japanese, and French. This allows it to cater to users in different language regions and reach a wide audience. The generation unit can also distribute the generated AI animations through video content media. For example, it can widely distribute the generated AI animations through video content media. This allows the generation unit to quickly generate and widely distribute high-quality AI animations based on users' future interests. Furthermore, the generation unit can collect user feedback and continuously improve the generation process and content quality. As a result, the generation unit can always provide high-quality AI animations that respond to the latest interests.

[0033] The data collection unit can collect tap time, the interval between taps, and the frequency of operations considered to be accidental taps. For example, the data collection unit measures tap time in milliseconds and records the interval between taps. The data collection unit can also count the frequency of operations considered to be accidental taps. For example, the data collection unit uses the smartphone's touch sensor to measure tap time. The data collection unit uses the timestamp of the touch event to record the interval between taps. The data collection unit analyzes the location and strength of the taps to count the frequency of operations considered to be accidental taps. In this way, the data collection unit can more accurately understand user behavior by collecting detailed data on tap information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input tap information into an AI, and the AI ​​can analyze the tap information.

[0034] The prediction unit can make predictions by intentionally excluding content related to the user's searches and actions over the past few months. For example, the prediction unit can exclude past search history and location information and make predictions based only on tap information. This allows the prediction unit to eliminate the feeling of being stalked and predict the user's next actions that they had not yet thought of. For example, the prediction unit filters the list of search keywords to exclude past search history. The prediction unit ignores location data to exclude location information. The prediction unit analyzes tap data to make predictions based only on tap information. This allows the prediction unit to eliminate the feeling of being stalked by predicting future interests that are not based on past actions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input tap information into AI, and the AI ​​can predict future interests.

[0035] The generation unit can generate AI animations limited to themes that viewers are likely to take action on in real life. For example, the generation unit can generate short animations on themes such as new hobbies, travel destinations, or learning content that users are likely to become interested in next. The generation unit can generate AI animations on themes that viewers are likely to take action on next. For example, the generation unit can generate short animations on the theme of new hobbies. The generation unit can also generate short animations on the theme of travel destinations. The generation unit can also generate short animations on the theme of learning content. In this way, the generation unit can encourage real action by generating AI animations on themes that viewers are likely to take action on next. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input future interests predicted by the prediction unit into the AI, and the AI ​​can generate an AI animation.

[0036] The generation unit can generate the generated AI animation as a short video with multilingual support. The generation unit can generate short videos that support multiple languages, such as English, Japanese, and French. The generation unit can generate the generated AI animation as a short video with multilingual support. The generation unit can generate, for example, an English-language short video. The generation unit can also generate a Japanese-language short video. The generation unit can also generate a French-language short video. In this way, the generation unit can support users who speak different languages ​​by generating multilingual short videos. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the generated AI animation into an AI, and the AI ​​can generate a multilingual short video.

[0037] The generation unit can distribute the generated AI animation on video content media. The generation unit can widely distribute the generated AI animation through video content media, for example. The generation unit can distribute the generated AI animation on video content media. The generation unit can also distribute the AI ​​animation through other video content media. This allows the generation unit to reach many users by widely distributing the generated AI animation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the generated AI animation into AI, which can optimize distribution on video content media.

[0038] The data collection unit can analyze the user's device usage patterns when collecting tap information and select the optimal collection method. For example, the data collection unit can identify the times when the user frequently uses the device and concentrate data collection during those times. The data collection unit can also collect data when the user is using a specific app. If the user is using the device for an extended period, the data collection unit can adjust the collection frequency to conserve battery power. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on the user's device usage patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's device usage patterns into AI, which can then select the optimal collection method.

[0039] The data collection unit can adjust the types of data it collects when collecting tap information, taking into account the user's app usage history. For example, the data collection unit can prioritize collecting tap information for apps that the user frequently uses. The data collection unit can also focus on collecting tap information for apps that the user has recently installed. If the user has been using a particular app for a long time, the data collection unit can collect detailed tap information for that app. This allows the data collection unit to collect more relevant data by adjusting the types of data it collects based on the user's app usage history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's app usage history into AI and adjust the types of data that AI collects.

[0040] The data collection unit can adjust the collection frequency when collecting tap information, taking into account the battery level of the user's device. For example, if the battery level is low, the collection unit can reduce the collection frequency to conserve battery power. If the battery level is sufficient, the collection unit can also increase the collection frequency to acquire more detailed data. If the battery level is moderate, the collection unit can also adjust the collection frequency appropriately. In this way, the collection unit can collect data while conserving battery power by adjusting the collection frequency based on the battery level of the user's device. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the device's battery level into the AI, which can then adjust the collection frequency.

[0041] The data collection unit can adjust the accuracy of the data it collects when collecting tap information, taking into account the screen resolution of the user's device. For example, if a high-resolution device is being used, the data collection unit will collect detailed tap information. If a low-resolution device is being used, the data collection unit may also reduce the accuracy of the data it collects. If a medium-resolution device is being used, the data collection unit may also adjust the accuracy of the data it collects appropriately. In this way, the data collection unit can collect data of appropriate accuracy by adjusting the accuracy of the data it collects based on the screen resolution of the user's device. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the device's screen resolution into the AI ​​and adjust the accuracy of the data collected by the AI.

[0042] The prediction unit can optimize its prediction algorithm by referring to the user's device usage history during prediction. For example, the prediction unit adjusts the prediction algorithm based on the history of apps the user frequently uses. The prediction unit can also make predictions considering the history of apps the user uses during specific time periods. The prediction unit can also optimize its prediction algorithm based on the history of apps the user has used for extended periods. This improves the accuracy of predictions by optimizing the prediction algorithm based on the user's device usage history. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's device usage history into AI, which can then optimize the prediction algorithm.

[0043] The prediction unit can improve the accuracy of its predictions based on the user's current living situation. For example, if the user is at work, the prediction unit can predict work-related interests. If the user is on vacation, the prediction unit can also predict leisure-related interests. If the user is at home, the prediction unit can also predict interests related to home life. This allows the prediction unit to make more relevant predictions by improving its accuracy based on the user's current living situation. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's living situation data into the AI, which can then improve the accuracy of its predictions.

[0044] The prediction unit can adjust its prediction algorithm during prediction, taking into account the user's device usage environment (e.g., indoors or outdoors). For example, if the user is indoors, the prediction unit predicts interests related to indoor activities. If the user is outdoors, the prediction unit can also predict interests related to outdoor activities. If the user is on the move, the prediction unit can also predict interests related to movement. This allows the prediction unit to make more accurate predictions by adjusting its prediction algorithm based on the user's usage environment. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user usage environment data into the AI, which can then adjust the prediction algorithm.

[0045] The prediction unit can improve the accuracy of its predictions by considering the time of day the user uses their device. For example, if the user uses their device in the morning, the prediction unit will predict interests related to morning activities. If the user uses their device in the evening, the prediction unit can also predict interests related to evening activities. If the user uses their device in the daytime, the prediction unit can also predict interests related to daytime activities. This allows the prediction unit to make more relevant predictions by improving the accuracy of its predictions based on the user's usage time. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user usage time data into the AI, which can then improve the accuracy of its predictions.

[0046] The generation unit can select the most suitable theme when generating AI-generated anime by referring to the user's past viewing history. For example, the generation unit can select a similar theme based on themes of anime the user has watched in the past. The generation unit can also select the most suitable theme based on the genres of anime the user has watched in the past. The generation unit can also select the most suitable theme based on the viewing time of anime the user has watched in the past. As a result, the generation unit can generate more relevant anime by selecting the most suitable theme based on the user's past viewing history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the user's viewing history data into AI, and the AI ​​can select the most suitable theme.

[0047] The generation unit can customize themes based on the user's current living situation when generating AI animations. For example, if the user is at work, the generation unit can generate AI animations with work-related themes. If the user is on vacation, the generation unit can also generate AI animations with leisure-related themes. If the user is at home, the generation unit can also generate AI animations with home-life-related themes. This allows the generation unit to generate more relevant animations by customizing themes based on the user's current living situation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user living situation data into the AI, which can then customize themes.

[0048] The generation unit can select the optimal display format when generating AI animations, taking into account the screen size of the user's device. For example, if the user is using a smartphone, the generation unit provides a display format that matches the screen size. If the user is using a tablet, the generation unit can also provide a display format optimized for a larger screen. If the user is using a smartwatch, the generation unit can also provide a concise and highly visible display format. In this way, the generation unit can provide highly visible animations by selecting the optimal display format based on the screen size of the user's device. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the screen size data of the user's device into the AI, and the AI ​​can select the optimal display format.

[0049] The generation unit can generate AI animations with sound, taking into account the user's device's audio settings. For example, if the user has turned on audio settings, the generation unit will generate an AI animation with sound. If the user has turned off audio settings, the generation unit can also generate an AI animation with subtitles. If the user has customized audio settings, the generation unit can also generate an AI animation with sound based on those settings. This allows the generation unit to provide animations tailored to the user's preferences by generating animations with sound based on the user's device's audio settings. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's audio setting data into an AI, which can then generate an animation with sound.

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

[0051] The future-predictive AI anime generation system can optimize anime viewing time based on the user's device usage history. For example, if the user uses the device for only a short time, it can generate a short anime. If the user uses the device for a long time, it can also generate a feature-length anime. If the user uses the device during a specific time period, the length of the anime can be adjusted to suit that time period. This improves the user's viewing experience by providing the optimal viewing time based on the user's device usage history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's device usage history data into the AI, which can then optimize the anime viewing time.

[0052] The future-predictive AI animation generation system can adjust the animation generation method considering the battery level of the user's device. For example, if the battery level is low, it can generate a lightweight animation to conserve battery power. If the battery level is sufficient, it can also generate a high-quality animation. If the battery level is moderate, it can also generate an animation of moderate quality. This allows for an optimal animation based on the user's device's battery level, improving the viewing experience while conserving battery power. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input device battery level data into the AI, which can then adjust the animation generation method.

[0053] The future-predictive AI animation generation system can adjust the graphic quality of the animation considering the screen resolution of the user's device. For example, it can provide high-quality graphics when a high-resolution device is used, lightweight graphics when a low-resolution device is used, and graphics of moderate quality when a medium-resolution device is used. This allows for the provision of highly visible animation by providing optimal graphic quality based on the user's device's screen resolution. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input device screen resolution data into the AI, which can then adjust the graphic quality.

[0054] The future-predicting AI animation generation system can adjust the animation theme considering the user's device usage environment (e.g., indoors or outdoors). For example, if the user is indoors, it can provide animations with themes suitable for indoor use. If the user is outdoors, it can provide animations with themes related to outdoor activities. If the user is on the move, it can provide animations with themes related to travel. This allows for the provision of more relevant animations by offering the most appropriate theme based on the user's usage environment. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user usage environment data into the AI, which can then adjust the theme.

[0055] The future-predictive AI animation generation system can adjust the animation's audio quality considering the user's device's audio settings. For example, if the user has high-quality audio settings, it will provide high-quality audio. If the user has low-quality audio settings, it can also provide lighter audio. If the user has customized audio settings, the system can adjust the audio quality based on those settings. This improves the viewing experience by providing optimal audio quality based on the user's device's audio settings. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's audio setting data into the AI, which can then adjust the audio quality.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection unit collects tap information from the user's smartphone. The data collection unit collects data such as tap duration (in microseconds), the interval between taps, and the frequency of operations that are considered to be accidental taps. The data collection unit uses only the most recent few data points, not long-term data. For example, the data collection unit measures tap duration in milliseconds and records the interval between taps. The data collection unit can also count the frequency of operations that are considered to be accidental taps. Step 2: The prediction unit predicts the user's future interests based on the tap information collected by the collection unit. The prediction unit makes predictions by, for example, intentionally excluding content related to the user's searches and actions over the past few months. This allows the prediction unit to eliminate the feeling of being stalked and predict the user's next actions that they hadn't yet thought of. The prediction unit makes predictions based only on tap information, for example, by excluding past search history and location information. The prediction unit can use AI to predict the user's future interests from the tap information. Step 3: The generation unit generates AI animations based on future interests predicted by the prediction unit. The generation unit generates AI animations limited to themes that viewers might actually take action on (purchase, study, travel) in the future. For example, the generation unit generates short animations on themes such as new hobbies, travel destinations, or learning content that users might become interested in next. The generation unit can use AI to generate AI animations on themes that viewers are likely to take action on next. The generation unit can also generate the generated AI animations as short videos with multilingual support. For example, the generation unit can generate short videos that support multiple languages, such as English, Japanese, and French. The generation unit can also distribute the generated AI animations on video content media. For example, the generation unit widely distributes the generated AI animations through video content media.

[0058] (Example of form 2) The future-predicting AI animation generation system according to an embodiment of the present invention is a system that utilizes smartphone tap information to predict the user's future interests and generates AI animations that prompt the next action (purchase, study, travel) based on that prediction. The future-predicting AI animation generation system solves the problem that conventional recommended videos, which are based on search words and location information, can feel stalker-like and stressful to users. The future-predicting AI animation generation system solves this problem by utilizing smartphone tap information. First, the future-predicting AI animation generation system collects tap information from the user's smartphone. Specifically, it collects data such as tap time (in microseconds), the interval between taps, and the frequency of operations that are considered to be accidental taps. This data is not long-term, but only the most recent few data points are used. Next, the future-predicting AI animation generation system predicts the user's future interests based on the collected tap information. The prediction intentionally excludes content related to the user's searches and actions over the past few months. This eliminates the feeling of being stalked and makes it possible to predict the next action that the user had not yet thought of. Based on the prediction results, the future-predicting AI animation generation system generates an AI animation. The AI-generated animations are limited to themes that viewers can realistically act upon (purchase, study, travel) if they choose to do so. For example, short animations are generated on themes such as new hobbies, travel destinations, or learning materials that users might be interested in next. This system allows users to come up with their next desired action from the recommended videos. This leads users to feel a sense of new discovery and to actually take action. Furthermore, by accumulating this data, it can be used for zero-order analysis. Companies can use data from when viewers have not yet started anything to make optimal approaches. In addition, the generated AI animations are distributed as multilingual short videos on video content media. This allows them to be distributed globally as future-oriented recommended videos even without any past information on the viewer. This system can also be used to sell the analysis results for a fee to strategic departments in private companies and government agencies that are responsible for zero-order analysis.By using machine learning to track the process by which viewers perceive discovery, become interested, and take action, this can be utilized in strategic planning. This allows the future-predicting AI animation generation system to predict users' future interests and generate AI animations that prompt their next actions based on those predictions.

[0059] The AI ​​animation generation system according to the embodiment comprises a collection unit, a prediction unit, and a generation unit. The collection unit collects tap information from the user's smartphone. The collection unit collects data such as tap time (in microseconds), the interval between taps, and the frequency of operations considered to be accidental taps. The collection unit uses only the most recent few data points, not long-term data. The collection unit measures tap time in milliseconds and records the interval between taps. The collection unit can also count the frequency of operations considered to be accidental taps. The prediction unit predicts the user's future interests based on the tap information collected by the collection unit. The prediction unit makes predictions by intentionally excluding content related to the user's searches and actions over the past few months. This allows the prediction unit to eliminate the feeling of being stalked and predict the user's next actions that they had not yet thought of. The prediction unit makes predictions based only on tap information, for example, by excluding past search history and location information. The prediction unit can use AI to predict the user's future interests from the tap information. The generation unit generates AI animations based on future interests predicted by the prediction unit. The generation unit generates AI animations limited to themes that viewers might actually take action on (purchase, study, travel) in the future. For example, the generation unit generates short animations on themes such as new hobbies, travel destinations, or learning content that users might become interested in next. The generation unit can use AI to generate AI animations on themes that viewers are likely to take action on next. The generation unit can also generate the generated AI animations as short videos that support multiple languages. For example, the generation unit generates short videos that support multiple languages, such as English, Japanese, and French. The generation unit can also distribute the generated AI animations on video content media. For example, the generation unit widely distributes the generated AI animations through video content media. As a result, the future prediction AI animation generation system according to this embodiment can predict the user's future interests and generate AI animations that encourage the next action based on those predictions.

[0060] The data collection unit collects tap information from the user's smartphone. Specifically, the unit measures tap time in milliseconds and records the interval between taps. This allows for a detailed understanding of the user's operation patterns. Furthermore, the unit can also count the frequency of operations that are considered to be accidental taps. The frequency of accidental taps can serve as an indicator of fluctuations in the user's operation accuracy and attention span. The data collection unit uses only the most recent few data points, rather than long-term data. This enables real-time predictions based on the latest operation status. For example, the data collection unit measures the tap time in milliseconds when the user operates an application and records the interval between taps. In addition, by counting the frequency of operations that are considered to be accidental taps, it is possible to understand the user's operation patterns in detail. This allows the data collection unit to understand the user's operation patterns in real time and provide this information to the prediction unit. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, the collected data is stored on a cloud server and made accessible to the prediction and generation units. Also, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0061] The prediction unit predicts the user's future interests based on tap information collected by the data collection unit. Specifically, the prediction unit intentionally excludes content related to the user's searches and actions over the past few months when making predictions. This eliminates the feeling of being stalked and allows for the prediction of the user's next actions that they may not have yet thought of. For example, the prediction unit excludes past search history and location information and makes predictions based solely on tap information. The prediction unit can use AI to predict the user's future interests from tap information. Specifically, the AI ​​analyzes data such as tap timing and intervals, and the frequency of accidental taps to learn the user's operation patterns. This allows for highly accurate prediction of themes and actions that the user is likely to be interested in next. Furthermore, the prediction unit can also predict long-term fluctuations in interests by utilizing past data and statistical information. For example, based on past tap information, it can predict fluctuations in interests during specific time periods or days of the week and predict future actions. In addition, the prediction unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the prediction unit to not only predict interests in real time, but also to handle long-term fluctuations in interests and anomaly detection, thereby improving the reliability and accuracy of the entire system.

[0062] The generation unit generates AI animations based on future interests predicted by the prediction unit. Specifically, the generation unit generates AI animations limited to themes that viewers are likely to take action on (purchase, study, travel) in real life. For example, it generates short animations on themes such as new hobbies, travel destinations, or learning content that users are likely to become interested in. The generation unit can use AI to generate AI animations on themes that viewers are likely to take action on. Specifically, the generation AI automatically generates storylines, character settings, and scene compositions based on interest data provided by the prediction unit. This allows for the creation of engaging animations that capture the user's interest in a short amount of time. Furthermore, the generation unit can also generate the generated AI animations as short videos with multilingual support. For example, it can generate short videos that support multiple languages ​​such as English, Japanese, and French. This allows it to cater to users in different language regions and reach a wide audience. The generation unit can also distribute the generated AI animations through video content media. For example, it can widely distribute the generated AI animations through video content media. This allows the generation unit to quickly generate and widely distribute high-quality AI animations based on users' future interests. Furthermore, the generation unit can collect user feedback and continuously improve the generation process and content quality. As a result, the generation unit can always provide high-quality AI animations that respond to the latest interests.

[0063] The data collection unit can collect tap time, the interval between taps, and the frequency of operations considered to be accidental taps. For example, the data collection unit measures tap time in milliseconds and records the interval between taps. The data collection unit can also count the frequency of operations considered to be accidental taps. For example, the data collection unit uses the smartphone's touch sensor to measure tap time. The data collection unit uses the timestamp of the touch event to record the interval between taps. The data collection unit analyzes the location and strength of the taps to count the frequency of operations considered to be accidental taps. In this way, the data collection unit can more accurately understand user behavior by collecting detailed data on tap information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input tap information into an AI, and the AI ​​can analyze the tap information.

[0064] The prediction unit can make predictions by intentionally excluding content related to the user's searches and actions over the past few months. For example, the prediction unit can exclude past search history and location information and make predictions based only on tap information. This allows the prediction unit to eliminate the feeling of being stalked and predict the user's next actions that they had not yet thought of. For example, the prediction unit filters the list of search keywords to exclude past search history. The prediction unit ignores location data to exclude location information. The prediction unit analyzes tap data to make predictions based only on tap information. This allows the prediction unit to eliminate the feeling of being stalked by predicting future interests that are not based on past actions. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input tap information into AI, and the AI ​​can predict future interests.

[0065] The generation unit can generate AI animations limited to themes that viewers are likely to take action on in real life. For example, the generation unit can generate short animations on themes such as new hobbies, travel destinations, or learning content that users are likely to become interested in next. The generation unit can generate AI animations on themes that viewers are likely to take action on next. For example, the generation unit can generate short animations on the theme of new hobbies. The generation unit can also generate short animations on the theme of travel destinations. The generation unit can also generate short animations on the theme of learning content. In this way, the generation unit can encourage real action by generating AI animations on themes that viewers are likely to take action on next. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input future interests predicted by the prediction unit into the AI, and the AI ​​can generate an AI animation.

[0066] The generation unit can generate the generated AI animation as a short video with multilingual support. The generation unit can generate short videos that support multiple languages, such as English, Japanese, and French. The generation unit can generate the generated AI animation as a short video with multilingual support. The generation unit can generate, for example, an English-language short video. The generation unit can also generate a Japanese-language short video. The generation unit can also generate a French-language short video. In this way, the generation unit can support users who speak different languages ​​by generating multilingual short videos. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the generated AI animation into an AI, and the AI ​​can generate a multilingual short video.

[0067] The generation unit can distribute the generated AI animation on video content media. The generation unit can widely distribute the generated AI animation through video content media, for example. The generation unit can distribute the generated AI animation on video content media. The generation unit can also distribute the AI ​​animation through other video content media. This allows the generation unit to reach many users by widely distributing the generated AI animation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the generated AI animation into AI, which can optimize distribution on video content media.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of tap data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to lessen the user's burden. If the user is relaxed, the data collection unit can also increase the collection frequency to obtain more detailed data. If the user is in a hurry, the data collection unit can shorten the collection timing to quickly obtain data. In this way, the data collection unit can reduce the user's burden by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the collection timing.

[0069] The data collection unit can analyze the user's device usage patterns when collecting tap information and select the optimal collection method. For example, the data collection unit can identify the times when the user frequently uses the device and concentrate data collection during those times. The data collection unit can also collect data when the user is using a specific app. If the user is using the device for an extended period, the data collection unit can adjust the collection frequency to conserve battery power. This enables efficient data collection by allowing the data collection unit to select the optimal collection method based on the user's device usage patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's device usage patterns into AI, which can then select the optimal collection method.

[0070] The data collection unit can adjust the types of data it collects when collecting tap information, taking into account the user's app usage history. For example, the data collection unit can prioritize collecting tap information for apps that the user frequently uses. The data collection unit can also focus on collecting tap information for apps that the user has recently installed. If the user has been using a particular app for a long time, the data collection unit can collect detailed tap information for that app. This allows the data collection unit to collect more relevant data by adjusting the types of data it collects based on the user's app usage history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's app usage history into AI and adjust the types of data that AI collects.

[0071] The data collection unit can estimate the user's emotions and determine the priority of tap information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting important tap information. If the user is relaxed, the data collection unit can also collect detailed tap information. If the user is stressed, the data collection unit can minimize the amount of tap information collected. This allows the data collection unit to prioritize the collection of important data by determining the priority of tap information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 AI, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of tap information to collect.

[0072] The data collection unit can adjust the collection frequency when collecting tap information, taking into account the battery level of the user's device. For example, if the battery level is low, the collection unit can reduce the collection frequency to conserve battery power. If the battery level is sufficient, the collection unit can also increase the collection frequency to acquire more detailed data. If the battery level is moderate, the collection unit can also adjust the collection frequency appropriately. In this way, the collection unit can collect data while conserving battery power by adjusting the collection frequency based on the battery level of the user's device. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the device's battery level into the AI, which can then adjust the collection frequency.

[0073] The data collection unit can adjust the accuracy of the data it collects when collecting tap information, taking into account the screen resolution of the user's device. For example, if a high-resolution device is being used, the data collection unit will collect detailed tap information. If a low-resolution device is being used, the data collection unit may also reduce the accuracy of the data it collects. If a medium-resolution device is being used, the data collection unit may also adjust the accuracy of the data it collects appropriately. In this way, the data collection unit can collect data of appropriate accuracy by adjusting the accuracy of the data it collects based on the screen resolution of the user's device. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the device's screen resolution into the AI ​​and adjust the accuracy of the data collected by the AI.

[0074] The prediction unit can estimate the user's emotions and adjust its prediction method for future interests based on the estimated emotions. For example, if the user is relaxed, the prediction unit can predict a wide range of interests. If the user is stressed, the prediction unit can narrow its prediction to specific interests. If the user is excited, the prediction unit can predict new interests. This allows the prediction unit to predict future interests more accurately by adjusting its prediction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into an AI, which can then adjust the prediction method.

[0075] The prediction unit can optimize its prediction algorithm by referring to the user's device usage history during prediction. For example, the prediction unit adjusts the prediction algorithm based on the history of apps the user frequently uses. The prediction unit can also make predictions considering the history of apps the user uses during specific time periods. The prediction unit can also optimize its prediction algorithm based on the history of apps the user has used for extended periods. This improves the accuracy of predictions by optimizing the prediction algorithm based on the user's device usage history. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's device usage history into AI, which can then optimize the prediction algorithm.

[0076] The prediction unit can improve the accuracy of its predictions based on the user's current living situation. For example, if the user is at work, the prediction unit can predict work-related interests. If the user is on vacation, the prediction unit can also predict leisure-related interests. If the user is at home, the prediction unit can also predict interests related to home life. This allows the prediction unit to make more relevant predictions by improving its accuracy based on the user's current living situation. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's living situation data into the AI, which can then improve the accuracy of its predictions.

[0077] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. If the user is in a hurry, the prediction unit can also provide a display method that gets straight to the point. In this way, the prediction unit can provide a highly visible display by adjusting the display method of the prediction results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into AI, and the AI ​​can adjust the display method of the prediction results.

[0078] The prediction unit can adjust its prediction algorithm during prediction, taking into account the user's device usage environment (e.g., indoors or outdoors). For example, if the user is indoors, the prediction unit predicts interests related to indoor activities. If the user is outdoors, the prediction unit can also predict interests related to outdoor activities. If the user is on the move, the prediction unit can also predict interests related to movement. This allows the prediction unit to make more accurate predictions by adjusting its prediction algorithm based on the user's usage environment. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user usage environment data into the AI, which can then adjust the prediction algorithm.

[0079] The prediction unit can improve the accuracy of its predictions by considering the time of day the user uses their device. For example, if the user uses their device in the morning, the prediction unit will predict interests related to morning activities. If the user uses their device in the evening, the prediction unit can also predict interests related to evening activities. If the user uses their device in the daytime, the prediction unit can also predict interests related to daytime activities. This allows the prediction unit to make more relevant predictions by improving the accuracy of its predictions based on the user's usage time. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input user usage time data into the AI, which can then improve the accuracy of its predictions.

[0080] The generation unit can estimate the user's emotions and adjust the theme of the AI ​​animation it generates based on the estimated user emotions. For example, if the user is relaxed, the generation unit will generate an AI animation with a relaxing theme. If the user is excited, the generation unit can also generate an AI animation with an exciting theme. If the user is stressed, the generation unit can also generate an AI animation with a stress-relieving theme. In this way, the generation unit can generate animations with more appropriate themes by adjusting the theme of the AI ​​animation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the AI, and the AI ​​can adjust the theme of the AI ​​animation.

[0081] The generation unit can select the most suitable theme when generating AI-generated anime by referring to the user's past viewing history. For example, the generation unit can select a similar theme based on themes of anime the user has watched in the past. The generation unit can also select the most suitable theme based on the genres of anime the user has watched in the past. The generation unit can also select the most suitable theme based on the viewing time of anime the user has watched in the past. As a result, the generation unit can generate more relevant anime by selecting the most suitable theme based on the user's past viewing history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the user's viewing history data into AI, and the AI ​​can select the most suitable theme.

[0082] The generation unit can customize themes based on the user's current living situation when generating AI animations. For example, if the user is at work, the generation unit can generate AI animations with work-related themes. If the user is on vacation, the generation unit can also generate AI animations with leisure-related themes. If the user is at home, the generation unit can also generate AI animations with home-life-related themes. This allows the generation unit to generate more relevant animations by customizing themes based on the user's current living situation. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user living situation data into the AI, which can then customize themes.

[0083] The generation unit can estimate the user's emotions and adjust the display method of the AI ​​animation generated based on the estimated user emotions. For example, if the user is nervous, the generation unit can provide a simple and highly visible display method. If the user is relaxed, the generation unit can also provide a display method that includes detailed information. If the user is in a hurry, the generation unit can also provide a display method that gets straight to the point. In this way, the generation unit can provide a highly visible display by adjusting the display method of the AI ​​animation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the AI, and the AI ​​can adjust the display method.

[0084] The generation unit can select the optimal display format when generating AI animations, taking into account the screen size of the user's device. For example, if the user is using a smartphone, the generation unit provides a display format that matches the screen size. If the user is using a tablet, the generation unit can also provide a display format optimized for a larger screen. If the user is using a smartwatch, the generation unit can also provide a concise and highly visible display format. In this way, the generation unit can provide highly visible animations by selecting the optimal display format based on the screen size of the user's device. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the screen size data of the user's device into the AI, and the AI ​​can select the optimal display format.

[0085] The generation unit can generate AI animations with sound, taking into account the user's device's audio settings. For example, if the user has turned on audio settings, the generation unit will generate an AI animation with sound. If the user has turned off audio settings, the generation unit can also generate an AI animation with subtitles. If the user has customized audio settings, the generation unit can also generate an AI animation with sound based on those settings. This allows the generation unit to provide animations tailored to the user's preferences by generating animations with sound based on the user's device's audio settings. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's audio setting data into an AI, which can then generate an animation with sound.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The future-predicting AI animation generation system can estimate the user's emotions and dynamically change the animation's storyline based on those emotions. For example, if the user is relaxed, it can provide a calm and relaxing storyline. If the user is excited, it can provide a storyline that includes action and adventure elements. If the user is stressed, it can provide a soothing storyline that helps relieve stress. This allows for a more personalized animation experience by providing an optimal storyline tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the AI, which can then adjust the storyline.

[0088] The future-predictive AI anime generation system can optimize anime viewing time based on the user's device usage history. For example, if the user uses the device for only a short time, it can generate a short anime. If the user uses the device for a long time, it can also generate a feature-length anime. If the user uses the device during a specific time period, the length of the anime can be adjusted to suit that time period. This improves the user's viewing experience by providing the optimal viewing time based on the user's device usage history. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's device usage history data into the AI, which can then optimize the anime viewing time.

[0089] The future-predicting AI animation generation system can estimate the user's emotions and dynamically change the animation's music and sound effects based on those estimated emotions. For example, if the user is relaxed, it can provide calming music and sound effects. If the user is excited, it can provide fast-paced music and sound effects. If the user is stressed, it can provide soothing music and sound effects. This allows for a more personalized animation experience by providing optimal music and sound effects according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the AI, which can then adjust the music and sound effects.

[0090] The future-predictive AI animation generation system can adjust the animation generation method considering the battery level of the user's device. For example, if the battery level is low, it can generate a lightweight animation to conserve battery power. If the battery level is sufficient, it can also generate a high-quality animation. If the battery level is moderate, it can also generate an animation of moderate quality. This allows for an optimal animation based on the user's device's battery level, improving the viewing experience while conserving battery power. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input device battery level data into the AI, which can then adjust the animation generation method.

[0091] The future-predicting AI animation generation system can estimate the user's emotions and dynamically change the facial expressions and movements of the animated character based on the estimated emotions. For example, if the user is relaxed, the character's facial expressions and movements can be made gentler. If the user is excited, the character's facial expressions and movements can be made more lively. If the user is stressed, the character's facial expressions and movements can be made more soothing. This allows for a more personalized animation experience by providing the optimal character facial expressions and movements according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's emotion data into the AI, which can then adjust the character's facial expressions and movements.

[0092] The future-predictive AI animation generation system can adjust the graphic quality of the animation considering the screen resolution of the user's device. For example, it can provide high-quality graphics when a high-resolution device is used, lightweight graphics when a low-resolution device is used, and graphics of moderate quality when a medium-resolution device is used. This allows for the provision of highly visible animation by providing optimal graphic quality based on the user's device's screen resolution. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input device screen resolution data into the AI, which can then adjust the graphic quality.

[0093] The future-predicting AI animation generation system can estimate the user's emotions and dynamically change the animation's colors and design based on those emotions. For example, if the user is relaxed, it can provide calm colors and designs. If the user is excited, it can provide vibrant colors and designs. If the user is stressed, it can provide soothing colors and designs. This allows for a more personalized animation experience by providing optimal colors and designs tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the AI, which can then adjust the colors and design.

[0094] The future-predicting AI animation generation system can adjust the animation theme considering the user's device usage environment (e.g., indoors or outdoors). For example, if the user is indoors, it can provide animations with themes suitable for indoor use. If the user is outdoors, it can provide animations with themes related to outdoor activities. If the user is on the move, it can provide animations with themes related to travel. This allows for the provision of more relevant animations by offering the most appropriate theme based on the user's usage environment. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user usage environment data into the AI, which can then adjust the theme.

[0095] The future-predicting AI animation generation system can estimate the user's emotions and dynamically change the tone and style of the animation's narration based on the estimated emotions. For example, if the user is relaxed, it can provide a calm tone of narration. If the user is excited, it can provide an energetic tone of narration. If the user is stressed, it can provide a soothing tone of narration. This allows for a more personalized animation experience by providing optimal narration tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the AI, which can then adjust the tone and style of the narration.

[0096] The future-predictive AI animation generation system can adjust the animation's audio quality considering the user's device's audio settings. For example, if the user has high-quality audio settings, it will provide high-quality audio. If the user has low-quality audio settings, it can also provide lighter audio. If the user has customized audio settings, the system can adjust the audio quality based on those settings. This improves the viewing experience by providing optimal audio quality based on the user's device's audio settings. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the user's audio setting data into the AI, which can then adjust the audio quality.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection unit collects tap information from the user's smartphone. The data collection unit collects data such as tap duration (in microseconds), the interval between taps, and the frequency of operations that are considered to be accidental taps. The data collection unit uses only the most recent few data points, not long-term data. For example, the data collection unit measures tap duration in milliseconds and records the interval between taps. The data collection unit can also count the frequency of operations that are considered to be accidental taps. Step 2: The prediction unit predicts the user's future interests based on the tap information collected by the collection unit. The prediction unit makes predictions by, for example, intentionally excluding content related to the user's searches and actions over the past few months. This allows the prediction unit to eliminate the feeling of being stalked and predict the user's next actions that they hadn't yet thought of. The prediction unit makes predictions based only on tap information, for example, by excluding past search history and location information. The prediction unit can use AI to predict the user's future interests from the tap information. Step 3: The generation unit generates AI animations based on future interests predicted by the prediction unit. The generation unit generates AI animations limited to themes that viewers might actually take action on (purchase, study, travel) in the future. For example, the generation unit generates short animations on themes such as new hobbies, travel destinations, or learning content that users might become interested in next. The generation unit can use AI to generate AI animations on themes that viewers are likely to take action on next. The generation unit can also generate the generated AI animations as short videos with multilingual support. For example, the generation unit can generate short videos that support multiple languages, such as English, Japanese, and French. The generation unit can also distribute the generated AI animations on video content media. For example, the generation unit widely distributes the generated AI animations through video content media.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the collection unit, prediction unit, and generation 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 tap information using the touch panel 38A and microphone 38B of the smart device 14. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future interests based on the collected tap information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an AI animation based on the predicted interests. The generation unit can also be implemented by the control unit 46A of the smart device 14, and the generated AI animation can be distributed as a multilingual short video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the collection unit, prediction unit, and generation 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 tap information using the microphone 238 and camera 42 of the smart glasses 214. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future interests based on the collected tap information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an AI animation based on the predicted interests. The generation unit can also be implemented by the control unit 46A of the smart glasses 214, and the generated AI animation can be distributed as a multilingual short video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, prediction unit, and generation 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 tap information using the microphone 238 and camera 42 of the headset terminal 314. The prediction unit is implemented by the specific processing unit 290 of the data processing device 12 and predicts the user's future interests based on the collected tap information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates an AI animation based on the predicted interests. The generation unit can also be implemented by the control unit 46A of the headset terminal 314, and the generated AI animation can be distributed as a multilingual short video. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, prediction unit, and generation 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 tap information using the robot 414's microphone 238 and camera 42. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the user's future interests based on the collected tap information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an AI animation based on the predicted interests. The generation unit can also be implemented by the control unit 46A of the robot 414, and the generated AI animation can be distributed as a multilingual short video. 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A collection unit that collects tap information from the user's smartphone, Based on the tap information collected by the aforementioned collection unit, a prediction unit predicts the user's future interests and preferences. The system comprises a generation unit that generates AI animations based on future interests predicted by the prediction unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on tap duration, the interval between taps, and the frequency of actions that are considered accidental taps. The system described in Appendix 1, characterized by the features described herein. (Note 3) The prediction unit, The prediction intentionally excludes content related to the user's searches and behavior over the past few months. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates animations limited to themes that viewers would actually want to act upon in real life. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate the AI ​​animation as a short video with multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The generated AI animation will be distributed on video content media. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of tap data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting tap data, the system analyzes the user's device usage patterns and selects the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting tap information, we adjust the types of data collected based on the user's app usage history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of tap information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting tap information, the collection frequency is adjusted considering the battery level of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting tap information, the accuracy of the collected data is adjusted considering the screen resolution of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, We estimate the user's emotions and adjust the prediction method for future interests based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, During prediction, the prediction algorithm is optimized by referencing the user's device usage history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, During prediction, the accuracy of the prediction is improved based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, When making predictions, the prediction algorithm is adjusted to take into account the user's device usage environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The prediction unit, When making predictions, we improve the accuracy of the predictions by taking into account the time periods when users' devices are being used. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the theme of the AI ​​animation generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating AI animations, the system selects the most suitable theme by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating AI animations, the theme is customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts how the AI ​​animations generated based on those estimated emotions are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating AI animations, the system selects the optimal display format considering the screen size of the user's device. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating AI animations, the system takes into account the user's device's audio settings to create animations with sound. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 collection unit that collects tap information from the user's smartphone, Based on the tap information collected by the aforementioned collection unit, a prediction unit predicts the user's future interests and preferences. The system comprises a generation unit that generates AI animations based on future interests predicted by the prediction unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on tap duration, the interval between taps, and the frequency of actions that are considered accidental taps. The system according to feature 1.

3. The prediction unit, The prediction intentionally excludes content related to the user's searches and behavior over the past few months. The system according to feature 1.

4. The generating unit is The AI ​​generates animations limited to themes that viewers would actually want to act upon in real life. The system according to feature 1.

5. The generating unit is Generate the AI ​​animation as a short video with multilingual support. The system according to feature 1.

6. The generating unit is The generated AI animation will be distributed on video content media. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of tap data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting tap data, the system analyzes the user's device usage patterns and selects the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting tap information, we adjust the types of data collected based on the user's app usage history. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of tap information to collect based on the estimated user emotions. The system according to feature 1.

Images