system

Abstract

The system according to the embodiment aims to enable users to easily generate restored videos based on past photographs and historical data. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of past photos and historical data from a user. The analysis unit analyzes the data received by the reception unit. The generation unit generates a video based on information extracted by the analysis unit. The provision unit provides the video generated by the generation unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to generate videos based on past photographs and historical data, making it difficult for users to easily relive past memories.

[0005] The system according to the embodiment aims to enable users to easily generate restored videos based on past photographs and historical data. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of past photos and historical data from a user. The analysis unit analyzes the data received by the reception unit. The generation unit generates a video based on information extracted by the analysis unit. The provision unit provides the video generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment allows users to easily generate restored videos based on past photographs and historical data. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A restored video generation system according to an embodiment of the present invention uses a generation AI to generate restored videos using past photographs and historical data. In this restored video generation system, a user inputs past photographs and historical data (such as locations, eras, and historical background), and the generation AI analyzes the data to generate videos that recreate events from that time. These videos realistically recreate past events and scenery, allowing the user to reminisce. For example, a user inputs family photos, memorable photos of friends, newspaper articles from that time, and data on historical events. This information is input into the generation AI, which then analyzes the input data. The generation AI then extracts information to recreate the scenery and events from that time based on the photographs and historical data. For example, the generation AI analyzes the locations, people, and events from that time, and generates a video based on that information. The generated videos realistically recreate past events and scenery, allowing the user to reminisce. For example, a video recreating scenes from family life from that time based on family photographs, or a video recreating the location and circumstances of a historical event based on a historical event, can be generated. This system allows users to enjoy videos that realistically recreate their past memories. For example, to reminisce about memories with family and friends, you can generate videos based on past photos and historical data and enjoy them together. You can also generate videos based on data from that time to learn about historical events and use them as part of an educational program. This allows the restored video generation system to use generative AI to generate and provide restored videos based on past photos and historical data.

[0029] A restored video generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of past photographs and historical data from a user. Examples of data input by the user include, but are not limited to, family photos, memorable photos with friends, newspaper articles from the time, and data on historical events. The reception unit can receive, for example, digital photo data or scanned newspaper articles. The reception unit can also convert the data input by the user into a format suitable for the generation AI. The analysis unit uses the generation AI to analyze the data received by the reception unit. The analysis unit analyzes, for example, locations, people, and events from the time in the photograph, and extracts information for generating a video based on the information. The generation AI analyzes the input data using technologies such as deep learning and GAN (generative artificial network). For example, the analysis unit identifies the geographic location information of locations in the photograph and extracts information for recreating the scenery from the time. The analysis unit can also analyze the characteristics of people in the photograph and extract information for recreating the events from the time. The generation unit uses a generation AI to generate a video based on the information extracted by the analysis unit. The generation unit generates, for example, a video that realistically recreates past events or scenery. The generation AI generates realistic videos using technologies such as deep learning and GAN. For example, the generation unit generates a video that recreates a scene or event from a time based on the places and people in a photograph. The generation unit can also generate a video that recreates the location and circumstances of a historical event based on the historical event. The provision unit provides the video generated by the generation unit to a user. The provision unit can provide the generated video, for example, in a streaming or download format. The provision unit can also provide a user interface for the user to view the generated video. For example, the provision unit provides the generated video to the user through a web application or a mobile application. As a result, the restored video generation system according to the embodiment allows the user to generate and provide a restored video using the generation AI based on past photographs and historical data.

[0030] The analysis unit can analyze the places, people, and events that occurred in the photograph and generate a video based on that information. For example, the analysis unit identifies the geographic location information of the place in the photograph and extracts information for recreating the scenery at that time. For example, the analysis unit identifies the geographic location information of the place in the photograph and extracts information for recreating the scenery at that time. The analysis unit can also analyze the characteristics of the person in the photograph and extract information for recreating the events at that time. For example, the analysis unit can analyze the facial features of the person in the photograph and determine the situation in which the person was. The analysis unit can also analyze the events that occurred in the photograph and determine how the events unfolded. For example, the analysis unit can analyze the background information of the events in the photograph and determine how the events unfolded. This allows the analysis unit to analyze the information in the photograph and use it to generate a video. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about places, people, and events in a photo into the generation AI, which then analyzes that information. The generation AI then analyzes the input data using technologies such as deep learning and GAN. This allows the analysis unit to analyze the information in the photo and use it to generate a video.

[0031] The generation unit can use generative AI to generate videos that realistically recreate past events and scenery. The generation unit generates, for example, videos that realistically recreate past events and scenery. The generative AI generates realistic videos using technologies such as deep learning and GAN (generative artificial network). For example, the generation unit generates videos that recreate the scenery and events of the time based on the places and people in a photograph. The generation unit can also generate videos that recreate the place and situation where a historical event occurred based on historical event data. For example, the generation unit generates videos that recreate the place and situation where the event occurred based on historical event data. The generation unit uses generative AI to generate videos based on information for realistically recreating past events and scenery. For example, the generation unit generates videos that recreate the scenery and events of the time based on information about the places and people in a photograph. The generation unit can also generate videos that recreate the place and situation where the event occurred based on historical event data. In this way, the generation unit can use generative AI to generate videos that realistically recreate past events and scenery. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs information about the places, people, and events in the photo into the generation AI, which then generates a video based on that information. The generation AI generates realistic videos using technologies such as deep learning and GAN. This allows the generation unit to use the generation AI to generate videos that realistically recreate past events and scenery.

[0032] The providing unit can provide the generated video to the user, allowing the user to reminisce about past memories. The providing unit can provide the generated video, for example, in a streaming or download format. The providing unit can also provide a user interface for the user to view the generated video. For example, the providing unit can provide the generated video to the user through a web application or a mobile application. The providing unit can provide the generated video to the user, allowing the user to reminisce about past memories. For example, the providing unit can provide the generated video in a streaming or download format. The providing unit can also provide a user interface for the user to view the generated video. For example, the providing unit can provide the generated video to the user through a web application or a mobile application. In this way, the providing unit can provide the generated video to the user, allowing the user to reminisce about past memories. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the generated video into the generation AI, which then provides the video based on that information. The generation AI generates realistic videos using technologies such as deep learning and GAN. This allows the providing unit to provide the generated video to the user, allowing the user to look back on past memories.

[0033] The reception unit can input data such as family photos, memorable photos with friends, newspaper articles from that time, or historical events. For example, the reception unit can accept digital photo data or scanned newspaper articles. The reception unit can also convert data input by the user into a format suitable for the generation AI. For example, the reception unit can convert digital photo data into a format suitable for the generation AI and input it to the generation AI. The reception unit can also convert scanned newspaper articles into a format suitable for the generation AI and input it to the generation AI. This allows the reception unit to accept a variety of data. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit can input data such as family photos, memorable photos with friends, newspaper articles from that time, or historical events into the generation AI, and the generation AI analyzes the data based on that information. The generation AI analyzes the input data using technologies such as deep learning and GAN. This allows the reception unit to accept a variety of data.

[0034] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit prioritizes reception of data formats (e.g., photos, text, etc.) that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (e.g., voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a data format to be used during a specific time period based on the user's past input history. This allows the reception unit to select the optimal reception method based on the user's past input history. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past input history data into the generation AI, which analyzes the data and selects the optimal reception method. The generation AI analyzes the input history data using technologies such as deep learning and machine learning. This allows the reception unit to select the optimal reception method based on the user's past input history.

[0035] The reception unit can perform filtering based on the user's current areas of interest and living situation when receiving the data. For example, the reception unit prioritizes receiving data related to historical events in which the user is currently interested. The reception unit can also filter related data based on the user's living situation (e.g., family structure and occupation). Furthermore, the reception unit can prioritize receiving data related to places the user has recently visited. This allows the reception unit to filter data based on the user's areas of interest and living situation. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs data related to the user's areas of interest and living situation into the generation AI, which analyzes the data and performs filtering. The generation AI uses technologies such as deep learning and machine learning to filter the data based on the user's areas of interest and living situation. This allows the reception unit to filter data based on the user's areas of interest and living situation.

[0036] The reception unit can prioritize receiving highly relevant data based on the user's geographical location information during reception. For example, the reception unit can prioritize receiving past photos and historical data related to the user's current location. The reception unit can also prioritize receiving data related to places the user has lived in the past. Furthermore, the reception unit can prioritize receiving data related to photos taken by the user at travel destinations. This allows the reception unit to prioritize receiving highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's geographical location information into the generation AI, which analyzes the data and prioritizes receiving highly relevant data. The generation AI uses technologies such as deep learning and machine learning to filter data based on the geographical location information. This allows the reception unit to prioritize receiving highly relevant data based on the user's geographical location information.

[0037] The reception unit may analyze the user's social media activity and receive relevant data upon reception. For example, the reception unit may prioritize reception of data related to photos and posts shared by the user on social media. The reception unit may also prioritize reception of data related to historical events the user is following on social media. Furthermore, the reception unit may also prioritize reception of data related to posts the user has "liked" on social media. This allows the reception unit to receive relevant data based on the user's social media activity. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's social media activity data into the generation AI, which analyzes the data and receives relevant data. The generation AI may use technologies such as deep learning and machine learning to filter data based on the social media activity. This allows the reception unit to receive relevant data based on the user's social media activity.

[0038] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit may perform a detailed analysis of data related to important historical events. The analysis unit may also analyze memorable photos of family and friends by emphasizing emotional elements. The analysis unit may also perform a simplified analysis of general landscape photos. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the importance of the data into the generation AI, which analyzes the data and adjusts the level of detail. The generation AI uses technologies such as deep learning and machine learning to adjust the level of detail of the analysis based on the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies an image analysis algorithm to photo data. The analysis unit can also apply a natural language processing algorithm to text data. Furthermore, the analysis unit can apply a voice analysis algorithm to voice data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the data category into the generation AI, which analyzes the data and applies an appropriate analysis algorithm. The generation AI uses technologies such as deep learning and machine learning to apply an analysis algorithm depending on the data category. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category.

[0040] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also prioritize analysis of data submitted by a user during a specific time period. Furthermore, the analysis unit can also prioritize analysis of data that the user frequently submitted in the past. This allows the analysis unit to determine the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the time of data submission into the generation AI, which analyzes the data and determines the priority. The generation AI uses technologies such as deep learning and machine learning to determine the analysis priority based on the time of submission. This allows the analysis unit to determine the analysis priority based on the time of data submission.

[0041] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also prioritize analysis of highly relevant data based on the user's past input history. Furthermore, the analysis unit can also prioritize analysis of data related to the user's current field of interest. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the relevance of the data into the generation AI, which analyzes the data and adjusts the order. The generation AI adjusts the order of analysis based on the relevance using technologies such as deep learning and machine learning. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0042] The generation unit can adjust the level of detail of the generated data based on the importance of the data during generation. For example, the generation unit reproduces data related to important historical events in detail. The generation unit can also reproduce memorable photos of family and friends with an emphasis on emotional elements. Furthermore, the generation unit can also perform a simplified reproduction of general landscape photos. This allows the generation unit to adjust the level of detail of the generated data based on the importance of the data. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the importance of the data into the generation AI, which analyzes the data and adjusts the level of detail. The generation AI uses technologies such as deep learning and machine learning to adjust the level of detail of the generated data based on the importance of the data. This allows the generation unit to adjust the level of detail of the generated data based on the importance of the data.

[0043] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit applies an image generation algorithm to photo data. The generation unit can also apply a natural language generation algorithm to text data. Furthermore, the generation unit can apply a voice generation algorithm to voice data. This allows the generation unit to apply an appropriate generation algorithm depending on the data category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the data category into the generation AI, which analyzes the data and applies an appropriate generation algorithm. The generation AI uses technologies such as deep learning and machine learning to apply a generation algorithm depending on the data category. This allows the generation unit to apply an appropriate generation algorithm depending on the data category.

[0044] At the time of generation, the generation unit can determine the generation priority based on the time of data submission. For example, the generation unit prioritizes the generation of recently submitted data. The generation unit can also prioritize the generation of data submitted by a user during a specific time period. Furthermore, the generation unit can also prioritize the generation of data that the user frequently submitted in the past. This allows the generation unit to determine the generation priority based on the time of data submission. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the time of data submission into the generation AI, which analyzes the data and determines the priority. The generation AI determines the generation priority based on the time of submission using technologies such as deep learning and machine learning. This allows the generation unit to determine the generation priority based on the time of data submission.

[0045] The generation unit can adjust the order of generation based on the relevance of the data during generation. For example, the generation unit prioritizes generating highly relevant data. The generation unit can also prioritize generating highly relevant data based on the user's past input history. Furthermore, the generation unit can also prioritize generating data related to the user's current field of interest. This allows the generation unit to adjust the order of generation based on the relevance of the data. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the relevance of the data into the generation AI, which analyzes the data and adjusts the order. The generation AI adjusts the order of generation based on the relevance using technologies such as deep learning and machine learning. This allows the generation unit to adjust the order of generation based on the relevance of the data.

[0046] The providing unit can select an appropriate display method by referring to the user's past viewing history when providing the content. For example, the providing unit selects the optimal display method by referring to the display methods of videos the user has previously viewed. The providing unit can also preferentially suggest specific display methods based on the user's past viewing history. Furthermore, the providing unit can analyze the user's past viewing history and select the display method that is easiest for viewing. This allows the providing unit to select the optimal display method based on the user's past viewing history. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's viewing history data into the generation AI, which analyzes the data and selects the optimal display method. The generation AI analyzes the viewing history data using technologies such as deep learning and machine learning. This allows the providing unit to select the optimal display method based on the user's past viewing history.

[0047] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the providing unit to select the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's device information into the generation AI, which analyzes the data and selects the optimal display method. The generation AI uses technologies such as deep learning and machine learning to select the display method based on the device information. This allows the providing unit to select the optimal display method based on the user's device information.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can evaluate the reliability of data entered by the user and transmit highly reliable data to the analysis unit with priority. For example, the reception unit can evaluate the reliability of the data source or provider and filter out unreliable data. The reception unit can also check the consistency and integrity of the data and exclude data with inconsistencies. Furthermore, the reception unit can evaluate the freshness and recency of the data and process older data with lower priority. This allows the reception unit to transmit highly reliable data with priority to the analysis unit.

[0050] The analysis unit can learn the user's past input data and automatically identify and analyze similar data. For example, the analysis unit can learn the characteristics of previously input photos and determine whether a newly input photo is similar to the past data. The analysis unit can also learn patterns of past text data and analyze whether newly input text is related to the past data. Furthermore, the analysis unit can learn the characteristics of past voice data and determine whether newly input voice matches the past data. This allows the analysis unit to efficiently perform analysis by utilizing the user's past input data.

[0051] The generation unit can add interactive elements to the video to be generated, allowing the user to make selections and operations within the video. For example, the generation unit can display options within the video, allowing the user to make a selection and change the development of the story. The generation unit can also place interactive objects within the video, allowing the user to operate them and display additional information. Furthermore, the generation unit can incorporate quizzes and surveys into the video, allowing the content of the video to change as the user answers. In this way, the generation unit can enable the user to enjoy the video with a more immersive feeling.

[0052] The providing unit can collect user feedback on the generated video and improve the quality of the video based on that feedback. For example, the providing unit provides an interface that allows users to input ratings and comments after watching a video. The providing unit can also analyze the user's viewing history and viewing time to identify which parts of the video particularly attracted the user's interest. Furthermore, the providing unit can analyze the user's feedback and reflect it in the next video generation, thereby providing a video that better meets the user's needs. In this way, the providing unit can utilize the user's feedback to continuously improve the quality of the video.

[0053] The reception unit can anonymize or encrypt data to protect the privacy of data entered by the user. For example, the reception unit anonymizes data including the user's personal information so that a specific individual cannot be identified. The reception unit can also encrypt data when transmitting it to prevent unauthorized access by third parties. Furthermore, the reception unit can also encrypt data when saving it to ensure data security. This allows the reception unit to handle data safely while protecting the user's privacy.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit allows the user to input past photos and historical data. The data input by the user can include, for example, family photos, memorable photos with friends, newspaper articles from that time, and data on historical events. The reception unit can accept digital photo data and scanned newspaper articles, and can also convert the data input by the user into a format suitable for the generative AI. Step 2: The analysis unit uses the generation AI to analyze the data received by the reception unit. The analysis unit analyzes the locations, people, and events that appear in the photos, and extracts information to generate a video based on this. The generation AI uses technologies such as deep learning and GAN (generative artificial network) to analyze the input data. Step 3: The generator uses a generation AI to generate a video based on the information extracted by the analysis unit. The generator generates a video that realistically recreates past events and scenery. The generator AI uses technologies such as deep learning and GAN to generate realistic videos. Step 4: The providing unit provides the video generated by the generating unit to the user. The providing unit can provide the generated video in a streaming or download format. The providing unit can also provide a user interface for the user to view the generated video.

[0056] (Example 2) A restored video generation system according to an embodiment of the present invention uses a generation AI to generate restored videos using past photographs and historical data. In this restored video generation system, a user inputs past photographs and historical data (such as locations, eras, and historical background), and the generation AI analyzes the data to generate videos that recreate events from that time. These videos realistically recreate past events and scenery, allowing the user to reminisce. For example, a user inputs family photos, memorable photos of friends, newspaper articles from that time, and data on historical events. This information is input into the generation AI, which then analyzes the input data. The generation AI then extracts information to recreate the scenery and events from that time based on the photographs and historical data. For example, the generation AI analyzes the locations, people, and events from that time, and generates a video based on that information. The generated videos realistically recreate past events and scenery, allowing the user to reminisce. For example, a video recreating scenes from family life from that time based on family photographs, or a video recreating the location and circumstances of a historical event based on a historical event, can be generated. This system allows users to enjoy videos that realistically recreate their past memories. For example, to reminisce about memories with family and friends, you can generate videos based on past photos and historical data and enjoy them together. You can also generate videos based on data from that time to learn about historical events and use them as part of an educational program. This allows the restored video generation system to use generative AI to generate and provide restored videos based on past photos and historical data.

[0057] A restored video generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of past photographs and historical data from a user. Examples of data input by the user include, but are not limited to, family photos, memorable photos with friends, newspaper articles from the time, and data on historical events. The reception unit can receive, for example, digital photo data or scanned newspaper articles. The reception unit can also convert the data input by the user into a format suitable for the generation AI. The analysis unit uses the generation AI to analyze the data received by the reception unit. The analysis unit analyzes, for example, locations, people, and events from the time in the photograph, and extracts information for generating a video based on the information. The generation AI analyzes the input data using technologies such as deep learning and GAN (generative artificial network). For example, the analysis unit identifies the geographic location information of locations in the photograph and extracts information for recreating the scenery from the time. The analysis unit can also analyze the characteristics of people in the photograph and extract information for recreating the events from the time. The generation unit uses a generation AI to generate a video based on the information extracted by the analysis unit. The generation unit generates, for example, a video that realistically recreates past events or scenery. The generation AI generates realistic videos using technologies such as deep learning and GAN. For example, the generation unit generates a video that recreates a scene or event from a time based on the places and people in a photograph. The generation unit can also generate a video that recreates the location and circumstances of a historical event based on the historical event. The provision unit provides the video generated by the generation unit to a user. The provision unit can provide the generated video, for example, in a streaming or download format. The provision unit can also provide a user interface for the user to view the generated video. For example, the provision unit provides the generated video to the user through a web application or a mobile application. As a result, the restored video generation system according to the embodiment allows the user to generate and provide a restored video using the generation AI based on past photographs and historical data.

[0058] The analysis unit can analyze the places, people, and events that occurred in the photograph and generate a video based on that information. For example, the analysis unit identifies the geographic location information of the place in the photograph and extracts information for recreating the scenery at that time. For example, the analysis unit identifies the geographic location information of the place in the photograph and extracts information for recreating the scenery at that time. The analysis unit can also analyze the characteristics of the person in the photograph and extract information for recreating the events at that time. For example, the analysis unit can analyze the facial features of the person in the photograph and determine the situation in which the person was. The analysis unit can also analyze the events that occurred in the photograph and determine how the events unfolded. For example, the analysis unit can analyze the background information of the events in the photograph and determine how the events unfolded. This allows the analysis unit to analyze the information in the photograph and use it to generate a video. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs information about places, people, and events in a photo into the generation AI, which then analyzes that information. The generation AI then analyzes the input data using technologies such as deep learning and GAN. This allows the analysis unit to analyze the information in the photo and use it to generate a video.

[0059] The generation unit can use generative AI to generate videos that realistically recreate past events and scenery. The generation unit generates, for example, videos that realistically recreate past events and scenery. The generative AI generates realistic videos using technologies such as deep learning and GAN (generative artificial network). For example, the generation unit generates videos that recreate the scenery and events of the time based on the places and people in a photograph. The generation unit can also generate videos that recreate the place and situation where a historical event occurred based on historical event data. For example, the generation unit generates videos that recreate the place and situation where the event occurred based on historical event data. The generation unit uses generative AI to generate videos based on information for realistically recreating past events and scenery. For example, the generation unit generates videos that recreate the scenery and events of the time based on information about the places and people in a photograph. The generation unit can also generate videos that recreate the place and situation where the event occurred based on historical event data. In this way, the generation unit can use generative AI to generate videos that realistically recreate past events and scenery. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs information about the places, people, and events in the photo into the generation AI, which then generates a video based on that information. The generation AI generates realistic videos using technologies such as deep learning and GAN. This allows the generation unit to use the generation AI to generate videos that realistically recreate past events and scenery.

[0060] The providing unit can provide the generated video to the user, allowing the user to reminisce about past memories. The providing unit can provide the generated video, for example, in a streaming or download format. The providing unit can also provide a user interface for the user to view the generated video. For example, the providing unit can provide the generated video to the user through a web application or a mobile application. The providing unit can provide the generated video to the user, allowing the user to reminisce about past memories. For example, the providing unit can provide the generated video in a streaming or download format. The providing unit can also provide a user interface for the user to view the generated video. For example, the providing unit can provide the generated video to the user through a web application or a mobile application. In this way, the providing unit can provide the generated video to the user, allowing the user to reminisce about past memories. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the generated video into the generation AI, which then provides the video based on that information. The generation AI generates realistic videos using technologies such as deep learning and GAN. This allows the providing unit to provide the generated video to the user, allowing the user to look back on past memories.

[0061] The reception unit can input data such as family photos, memorable photos with friends, newspaper articles from that time, or historical events. For example, the reception unit can accept digital photo data or scanned newspaper articles. The reception unit can also convert data input by the user into a format suitable for the generation AI. For example, the reception unit can convert digital photo data into a format suitable for the generation AI and input it to the generation AI. The reception unit can also convert scanned newspaper articles into a format suitable for the generation AI and input it to the generation AI. This allows the reception unit to accept a variety of data. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit can input data such as family photos, memorable photos with friends, newspaper articles from that time, or historical events into the generation AI, and the generation AI analyzes the data based on that information. The generation AI analyzes the input data using technologies such as deep learning and GAN. This allows the reception unit to accept a variety of data.

[0062] The reception unit can estimate the user's emotions and adjust the timing of receiving input data based on the estimated user emotions. For example, if the user is feeling nostalgic, the reception unit can quickly receive input data and immediately proceed to analysis. Furthermore, if the user is feeling sad, the reception unit can slowly receive input data and wait until the user calms down. Furthermore, if the user is excited, the reception unit can quickly receive input data to maintain the user's excitement. This allows the reception unit to adjust the timing of receiving input data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's facial expression data and voice data into the generation AI, which then analyzes the data and estimates the user's emotions. This allows the reception unit to adjust the timing of receiving input data based on the user's emotions.

[0063] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit prioritizes reception of data formats (e.g., photos, text, etc.) that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (e.g., voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest a data format to be used during a specific time period based on the user's past input history. This allows the reception unit to select the optimal reception method based on the user's past input history. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's past input history data into the generation AI, which analyzes the data and selects the optimal reception method. The generation AI analyzes the input history data using technologies such as deep learning and machine learning. This allows the reception unit to select the optimal reception method based on the user's past input history.

[0064] The reception unit can perform filtering based on the user's current areas of interest and living situation when receiving the data. For example, the reception unit prioritizes receiving data related to historical events in which the user is currently interested. The reception unit can also filter related data based on the user's living situation (e.g., family structure and occupation). Furthermore, the reception unit can prioritize receiving data related to places the user has recently visited. This allows the reception unit to filter data based on the user's areas of interest and living situation. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs data related to the user's areas of interest and living situation into the generation AI, which analyzes the data and performs filtering. The generation AI uses technologies such as deep learning and machine learning to filter the data based on the user's areas of interest and living situation. This allows the reception unit to filter data based on the user's areas of interest and living situation.

[0065] The reception unit can estimate the user's emotions and determine the priority of data to be received based on the estimated user emotions. For example, if the user is feeling nostalgic, the reception unit can prioritize receiving past photo data. Furthermore, if the user is feeling sad, the reception unit can prioritize receiving data related to historical events. Furthermore, if the user is excited, the reception unit can prioritize receiving data about newspaper articles and events from that time. This allows the reception unit to determine the priority of data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit inputs the user's facial expression data and voice data into the generation AI, which then analyzes the data and estimates the user's emotions. This allows the reception unit to determine the priority of data based on the user's emotions.

[0066] The reception unit can prioritize receiving highly relevant data based on the user's geographical location information during reception. For example, the reception unit can prioritize receiving past photos and historical data related to the user's current location. The reception unit can also prioritize receiving data related to places the user has lived in the past. Furthermore, the reception unit can prioritize receiving data related to photos taken by the user at travel destinations. This allows the reception unit to prioritize receiving highly relevant data based on the user's geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's geographical location information into the generation AI, which analyzes the data and prioritizes receiving highly relevant data. The generation AI uses technologies such as deep learning and machine learning to filter data based on the geographical location information. This allows the reception unit to prioritize receiving highly relevant data based on the user's geographical location information.

[0067] The reception unit may analyze the user's social media activity and receive relevant data upon reception. For example, the reception unit may prioritize reception of data related to photos and posts shared by the user on social media. The reception unit may also prioritize reception of data related to historical events the user is following on social media. Furthermore, the reception unit may also prioritize reception of data related to posts the user has "liked" on social media. This allows the reception unit to receive relevant data based on the user's social media activity. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit inputs the user's social media activity data into the generation AI, which analyzes the data and receives relevant data. The generation AI may use technologies such as deep learning and machine learning to filter data based on the social media activity. This allows the reception unit to receive relevant data based on the user's social media activity.

[0068] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling nostalgic, the analysis unit presents the analysis results in an expression that elicits the user's emotions. Furthermore, if the user is feeling sad, the analysis unit can present the analysis results in an expression that soothes the user's emotions. Furthermore, if the user is excited, the analysis unit can present the analysis results in an expression that enhances the user's emotions. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's facial expression data and voice data into the generation AI, which then analyzes the data to estimate the user's emotions. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0069] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit may perform a detailed analysis of data related to important historical events. The analysis unit may also analyze memorable photos of family and friends by emphasizing emotional elements. The analysis unit may also perform a simplified analysis of general landscape photos. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the importance of the data into the generation AI, which analyzes the data and adjusts the level of detail. The generation AI uses technologies such as deep learning and machine learning to adjust the level of detail of the analysis based on the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0070] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies an image analysis algorithm to photo data. The analysis unit can also apply a natural language processing algorithm to text data. Furthermore, the analysis unit can apply a voice analysis algorithm to voice data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the data category into the generation AI, which analyzes the data and applies an appropriate analysis algorithm. The generation AI uses technologies such as deep learning and machine learning to apply an analysis algorithm depending on the data category. This allows the analysis unit to apply an appropriate analysis algorithm depending on the data category.

[0071] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit is performed using the generation AI. For example, the analysis unit inputs the user's facial expression data and voice data into the generation AI, which then analyzes the data and estimates the user's emotions. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0072] During analysis, the analysis unit can determine the analysis priority based on the time of data submission. For example, the analysis unit prioritizes analysis of recently submitted data. The analysis unit can also prioritize analysis of data submitted by a user during a specific time period. Furthermore, the analysis unit can also prioritize analysis of data that the user frequently submitted in the past. This allows the analysis unit to determine the analysis priority based on the time of data submission. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the time of data submission into the generation AI, which analyzes the data and determines the priority. The generation AI uses technologies such as deep learning and machine learning to determine the analysis priority based on the time of submission. This allows the analysis unit to determine the analysis priority based on the time of data submission.

[0073] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also prioritize analysis of highly relevant data based on the user's past input history. Furthermore, the analysis unit can also prioritize analysis of data related to the user's current field of interest. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the relevance of the data into the generation AI, which analyzes the data and adjusts the order. The generation AI adjusts the order of analysis based on the relevance using technologies such as deep learning and machine learning. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0074] The generation unit can estimate the user's emotions and adjust the expression method of the generated video based on the estimated user emotions. For example, if the user is feeling nostalgic, the generation unit can generate a video using an expression method that elicits the emotion. Furthermore, if the user is feeling sad, the generation unit can generate a video using an expression method that soothes the emotion. Furthermore, if the user is excited, the generation unit can generate a video using an expression method that enhances the emotion. This allows the generation unit to adjust the expression method of the video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data and voice data into the generation AI, which analyzes the data and estimates the user's emotions. This allows the generation unit to adjust the expression method of the video based on the user's emotions.

[0075] The generation unit can adjust the level of detail of the generated data based on the importance of the data during generation. For example, the generation unit reproduces data related to important historical events in detail. The generation unit can also reproduce memorable photos of family and friends with an emphasis on emotional elements. Furthermore, the generation unit can also perform a simplified reproduction of general landscape photos. This allows the generation unit to adjust the level of detail of the generated data based on the importance of the data. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the importance of the data into the generation AI, which analyzes the data and adjusts the level of detail. The generation AI uses technologies such as deep learning and machine learning to adjust the level of detail of the generated data based on the importance of the data. This allows the generation unit to adjust the level of detail of the generated data based on the importance of the data.

[0076] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit applies an image generation algorithm to photo data. The generation unit can also apply a natural language generation algorithm to text data. Furthermore, the generation unit can apply a voice generation algorithm to voice data. This allows the generation unit to apply an appropriate generation algorithm depending on the data category. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the data category into the generation AI, which analyzes the data and applies an appropriate generation algorithm. The generation AI uses technologies such as deep learning and machine learning to apply a generation algorithm depending on the data category. This allows the generation unit to apply an appropriate generation algorithm depending on the data category.

[0077] The generation unit can estimate the user's emotions and adjust the length of the video to be generated based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point video. Furthermore, if the user is relaxed, the generation unit can generate a longer video with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. This allows the generation unit to adjust the length of the video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's facial expression data and voice data into the generation AI, which then analyzes the data to estimate the user's emotions. This allows the generation unit to adjust the length of the video based on the user's emotions.

[0078] At the time of generation, the generation unit can determine the generation priority based on the time of data submission. For example, the generation unit prioritizes the generation of recently submitted data. The generation unit can also prioritize the generation of data submitted by a user during a specific time period. Furthermore, the generation unit can also prioritize the generation of data that the user frequently submitted in the past. This allows the generation unit to determine the generation priority based on the time of data submission. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the time of data submission into the generation AI, which analyzes the data and determines the priority. The generation AI determines the generation priority based on the time of submission using technologies such as deep learning and machine learning. This allows the generation unit to determine the generation priority based on the time of data submission.

[0079] The generation unit can adjust the order of generation based on the relevance of the data during generation. For example, the generation unit prioritizes generating highly relevant data. The generation unit can also prioritize generating highly relevant data based on the user's past input history. Furthermore, the generation unit can also prioritize generating data related to the user's current field of interest. This allows the generation unit to adjust the order of generation based on the relevance of the data. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit inputs the relevance of the data into the generation AI, which analyzes the data and adjusts the order. The generation AI adjusts the order of generation based on the relevance using technologies such as deep learning and machine learning. This allows the generation unit to adjust the order of generation based on the relevance of the data.

[0080] The providing unit can estimate the user's emotions and adjust the display method of the video to be provided based on the estimated user's emotions. For example, if the user is feeling nostalgic, the providing unit can provide the video in a display method that elicits the emotion. Furthermore, if the user is feeling sad, the providing unit can provide the video in a display method that soothes the emotion. Furthermore, if the user is excited, the providing unit can provide the video in a display method that enhances the emotion. This allows the providing unit to adjust the display method of the video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's facial expression data and voice data into the generation AI, which analyzes the data and estimates the user's emotions. This allows the providing unit to adjust the display method of the video based on the user's emotions.

[0081] The providing unit can select an appropriate display method by referring to the user's past viewing history when providing the content. For example, the providing unit selects the optimal display method by referring to the display methods of videos the user has previously viewed. The providing unit can also preferentially suggest specific display methods based on the user's past viewing history. Furthermore, the providing unit can analyze the user's past viewing history and select the display method that is easiest for viewing. This allows the providing unit to select the optimal display method based on the user's past viewing history. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's viewing history data into the generation AI, which analyzes the data and selects the optimal display method. The generation AI analyzes the viewing history data using technologies such as deep learning and machine learning. This allows the providing unit to select the optimal display method based on the user's past viewing history.

[0082] The providing unit can estimate the user's emotions and adjust the operation procedures of the video to be provided based on the estimated user's emotions. For example, if the user is feeling nostalgic, the providing unit can provide operation procedures that elicit the emotion. Furthermore, if the user is feeling sad, the providing unit can provide operation procedures that soothe the emotion. Furthermore, if the user is excited, the providing unit can provide operation procedures that enhance the emotion. This allows the providing unit to adjust the operation procedures of the video based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit inputs the user's facial expression data and voice data into the generation AI, which analyzes the data and estimates the user's emotions. This allows the providing unit to adjust the operation procedures of the video based on the user's emotions.

[0083] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This allows the providing unit to select the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit inputs the user's device information into the generation AI, which analyzes the data and selects the optimal display method. The generation AI uses technologies such as deep learning and machine learning to select the display method based on the device information. This allows the providing unit to select the optimal display method based on the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14, and the user inputs past photos and historical data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a video based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides the generated video to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214, and a user inputs past photos and historical data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a video based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides the generated video to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314, and the user inputs past photos and historical data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a video based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides the generated video to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and the user inputs past photos and historical data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the input data using a generation AI. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a video based on the analyzed information. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides the generated video to the user.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The reception unit can evaluate the reliability of data entered by the user and transmit highly reliable data to the analysis unit with priority. For example, the reception unit can evaluate the reliability of the data source or provider and filter out unreliable data. The reception unit can also check the consistency and integrity of the data and exclude data with inconsistencies. Furthermore, the reception unit can evaluate the freshness and recency of the data and process older data with lower priority. This allows the reception unit to transmit highly reliable data with priority to the analysis unit.

[0086] The analysis unit can learn the user's past input data and automatically identify and analyze similar data. For example, the analysis unit can learn the characteristics of previously input photos and determine whether a newly input photo is similar to the past data. The analysis unit can also learn patterns of past text data and analyze whether newly input text is related to the past data. Furthermore, the analysis unit can learn the characteristics of past voice data and determine whether newly input voice matches the past data. This allows the analysis unit to efficiently perform analysis by utilizing the user's past input data.

[0087] The generation unit can add interactive elements to the video to be generated, allowing the user to make selections and operations within the video. For example, the generation unit can display options within the video, allowing the user to make a selection and change the development of the story. The generation unit can also place interactive objects within the video, allowing the user to operate them and display additional information. Furthermore, the generation unit can incorporate quizzes and surveys into the video, allowing the content of the video to change as the user answers. In this way, the generation unit can enable the user to enjoy the video with a more immersive feeling.

[0088] The providing unit can collect user feedback on the generated video and improve the quality of the video based on that feedback. For example, the providing unit provides an interface that allows users to input ratings and comments after watching a video. The providing unit can also analyze the user's viewing history and viewing time to identify which parts of the video particularly attracted the user's interest. Furthermore, the providing unit can analyze the user's feedback and reflect it in the next video generation, thereby providing a video that better meets the user's needs. In this way, the providing unit can utilize the user's feedback to continuously improve the quality of the video.

[0089] The reception unit can anonymize or encrypt data to protect the privacy of data entered by the user. For example, the reception unit anonymizes data including the user's personal information so that a specific individual cannot be identified. The reception unit can also encrypt data when transmitting it to prevent unauthorized access by third parties. Furthermore, the reception unit can also encrypt data when saving it to ensure data security. This allows the reception unit to handle data safely while protecting the user's privacy.

[0090] The reception unit can estimate the user's emotions and customize the method of receiving input data based on the estimated user emotions. For example, if the user is feeling nostalgic, the reception unit can preferentially receive data of old photos and memories. If the user is feeling sad, the reception unit can also suggest a data reception method that will soothe the user's emotions. Furthermore, if the user is excited, the reception unit can also provide a data reception method that will enhance the user's emotions. In this way, the reception unit can provide the optimal data reception method according to the user's emotions.

[0091] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated user's emotions. For example, if the user is feeling nostalgic, the analysis unit can present the analysis results in an expression method that elicits the emotion. Also, if the user is feeling sad, the analysis unit can present the analysis results in an expression method that soothes the emotion. Furthermore, if the user is excited, the analysis unit can present the analysis results in an expression method that enhances the emotion. In this way, the analysis unit can adjust the presentation method of the analysis results based on the user's emotions.

[0092] The generation unit can estimate the user's emotion and adjust the music and sound effects of the video to be generated based on the estimated user's emotion. For example, if the user is feeling nostalgic, the generation unit can use nostalgic music that elicits the emotion. If the user is feeling sad, the generation unit can use calm music that soothes the emotion. Furthermore, if the user is excited, the generation unit can use energetic music that enhances the emotion. In this way, the generation unit can adjust the music and sound effects of the video based on the user's emotion.

[0093] The providing unit can estimate the user's emotions and adjust the viewing environment of the video to be provided based on the estimated user's emotions. For example, if the user feels nostalgic, the providing unit can provide a viewing environment that elicits the emotion. Also, if the user feels sad, the providing unit can provide a viewing environment that soothes the emotion. Furthermore, if the user is excited, the providing unit can provide a viewing environment that enhances the emotion. In this way, the providing unit can provide an optimal viewing environment based on the user's emotions.

[0094] The providing unit can estimate the user's emotions and adjust the subtitles and narration of the video to be provided based on the estimated user's emotions. For example, if the user feels nostalgic, the providing unit can add narration that elicits the emotion. Also, if the user feels sad, the providing unit can provide subtitles and narration that soothe the emotion. Furthermore, if the user is excited, the providing unit can provide subtitles and narration that enhance the emotion. In this way, the providing unit can provide optimal subtitles and narration based on the user's emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The reception unit allows the user to input past photos and historical data. The data input by the user can include, for example, family photos, memorable photos with friends, newspaper articles from that time, and data on historical events. The reception unit can accept digital photo data and scanned newspaper articles, and can also convert the data input by the user into a format suitable for the generative AI. Step 2: The analysis unit uses the generation AI to analyze the data received by the reception unit. The analysis unit analyzes the locations, people, and events that appear in the photos, and extracts information to generate a video based on this. The generation AI uses technologies such as deep learning and GAN (generative artificial network) to analyze the input data. Step 3: The generator uses a generation AI to generate a video based on the information extracted by the analysis unit. The generator generates a video that realistically recreates past events and scenery. The generator AI uses technologies such as deep learning and GAN to generate realistic videos. Step 4: The providing unit provides the video generated by the generating unit to the user. The providing unit can provide the generated video in a streaming or download format. The providing unit can also provide a user interface for the user to view the generated video.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception section where users can input past photos and historical data; an analysis unit that analyzes the data accepted by the acceptance unit; a generation unit that generates a video based on the information extracted by the analysis unit; a providing unit that provides the video generated by the generating unit to a user; Equipped with A system characterized by:

2. The analysis unit Analyzes the places, people, and events that appear in the photos and generates videos based on them 2. The system of claim 1.

3. The generation unit Using generative AI to generate videos that realistically recreate past events and scenes 2. The system of claim 1.

4. The providing unit The generated video is provided to the user, allowing the user to reminisce about past memories.

2. The system of claim 1.

5. The reception unit Enter family photos or photos of memories with friends, newspaper articles from that time, or data on historical events.

2. The system of claim 1.

6. The reception unit Estimates user emotions and adjusts the timing of accepting input data based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit At check-in, filtering is performed based on the user's current interests and life situation.

2. The system of claim 1.

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