System

Abstract

An object of the system according to the embodiment is to provide a virtual reality experience or an animation video by reconstructing a past moment based on fragmentary memories or episodes.SOLUTION: A system according to an embodiment includes a storage collection unit, a reconstruction unit, a virtual reality providing unit, and an animation generation unit. The memory collection unit collects fragmentary memories or episodes of the user. The reconstruction unit reconstructs visuals and stories based on the memories and episodes collected by the memory collection unit. The virtual reality providing unit provides a virtual reality experience based on the visual and the story reconstructed by the reconstructing unit. The animation generation unit generates an animation video based on the visual and the story reconstructed by the reconstruction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to recreate past moments based on fragmentary memories or episodes, limiting the means by which people can rediscover memories of the deceased.

[0005] The system according to the embodiment aims to reconstruct past moments based on fragmented memories and episodes, and provide virtual reality experiences and animated videos. [Means for solving the problem]

[0006] The system according to the embodiment includes a memory collection unit, a reconstruction unit, a virtual reality provision unit, and an animation generation unit. The memory collection unit collects fragmentary memories and episodes of a user. The reconstruction unit reconstructs visuals and a story based on the memories and episodes collected by the memory collection unit. The virtual reality provision unit provides a virtual reality experience based on the visuals and story reconstructed by the reconstruction unit. The animation generation unit generates an animation video based on the visuals and story reconstructed by the reconstruction unit. [Effects of the Invention]

[0007] The system according to the embodiment can reconstruct past moments based on fragmented memories and episodes, and provide virtual reality experiences and animated videos. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The memory revival AI system according to an embodiment of the present invention uses a generative AI to reconstruct past moments using visuals and stories based on the user's fragmented memories and episodes, providing virtual reality experiences and animated videos. This allows the user to recall forgotten memories and rediscover precious moments with the deceased.

[0029] A memory revival AI system according to an embodiment includes a memory collection unit, a reconstruction unit, a virtual reality provision unit, and an animation generation unit. The memory collection unit collects fragmentary memories and episodes from a user. For example, it can collect voice memos and diaries provided by the user, as well as episodes from family and friends. The memory collection unit can also use an emotion estimation function to evaluate the emotional value of the memories and episodes provided by the user and prioritize collection of particularly emotionally strong episodes. The reconstruction unit uses a generation AI to reconstruct visuals and stories based on the memories and episodes collected by the memory collection unit. For example, the generation AI can analyze the user's past photos and videos to generate more realistic visuals. The reconstruction unit can also use the emotion estimation function to evaluate the emotional impact of the reconstructed visuals and stories and select the version that most moves the user. The virtual reality provision unit provides a virtual reality experience based on the visuals and stories reconstructed by the reconstruction unit. For example, a user can wear a VR headset and experience past moments recreated by the generation AI in virtual reality. The virtual reality providing unit can also use an emotion estimation function to analyze the user's emotional reactions during the virtual reality experience in real time and dynamically adjust the experience content. The animation generating unit generates an animated video based on the visuals and story reconstructed by the reconstruction unit. For example, an animated video is created based on the visuals and story reconstructed by the generation AI and provided to the user. This allows the memory revival AI system according to the embodiment to evoke forgotten memories and rediscover precious moments with the deceased. For example, memories with the deceased can be recreated through a virtual reality experience and enjoyed as a video through an animated video. Furthermore, the interactive experience can deepen the connection with the deceased.

[0030] The memory collection unit can analyze unstructured data such as a user's voice memos and diary entries and automatically extract fragmented memories. The memory collection unit, for example, analyzes voice memos provided by the user and builds a system that automatically extracts fragmented memories. For example, it uses voice recognition technology to extract specific keywords or phrases from the voice memos and identify memory fragments. This allows for the automatic extraction of fragmented memories from unstructured data, thereby collecting more memories.

[0031] When collecting episodes from family and friends, the generation AI automatically conducts interview-style dialogue, allowing for more detailed information to be extracted. The memory collection unit builds a system in which, for example, the generation AI asks questions to family and friends in an interview format to extract detailed episodes. For example, the AI ​​automatically generates questions and collects episodes in a dialogue format. This allows the generation AI to automatically conduct interview-style dialogue, allowing for more detailed information to be collected.

[0032] The memory collection unit allows the generation AI to automatically search the internet for related photos and videos based on memories and episodes provided by the user, and provide them as complementary information. The memory collection unit, for example, builds a system in which the generation AI automatically searches the internet for related photos and videos based on memories and episodes provided by the user. For example, it searches for images and videos related to specific places or events and provides them as complementary information. This enables richer memory reconstruction by automatically searching for related photos and videos and providing them as complementary information.

[0033] The memory collection unit can collect episodes from users of different cultures and regions and perform memory reconstruction taking cultural background into consideration. The memory collection unit, for example, collects episodes from users of different cultures and regions and builds a system that performs memory reconstruction taking cultural background into consideration. For example, it collects episodes related to a specific culture or region and performs memory reconstruction that reflects that background. This makes it possible to perform memory reconstruction taking into consideration the background of different cultures and regions.

[0034] The reconstruction unit can analyze the user's past photos and videos to generate more realistic visuals. For example, the reconstruction unit constructs a system in which a generation AI analyzes the user's past photos and videos to generate more realistic visuals. For example, realistic landscapes and people are reproduced based on data from past photos and videos. This allows the analysis of past photos and videos to generate more realistic visuals, providing the user with a more realistic experience.

[0035] The reconstruction unit can provide a more realistic experience by using voice synthesis that imitates the user's voice and speaking style when reconstructing a story. The reconstruction unit, for example, builds a system that provides a more realistic experience by using voice synthesis that imitates the user's voice and speaking style when reconstructing a story. For example, the reconstruction unit records the user's voice and performs voice synthesis based on that voice. In this way, a more realistic experience can be provided by using voice synthesis that imitates the user's voice and speaking style.

[0036] The reconstruction unit can generate reconstructed visuals and stories in different art styles to provide options to the user. The reconstruction unit, for example, builds a system that generates reconstructed visuals and stories in different art styles to provide options to the user. For example, the reconstruction unit generates watercolor-style or manga-style visuals to provide options to the user. This makes it possible to provide a variety of options to the user by generating them in different art styles.

[0037] The reconstruction unit generates visuals and stories that incorporate different historical backgrounds and future scenarios, and can provide users with a new perspective. The reconstruction unit, for example, builds a system that generates visuals and stories that incorporate different historical backgrounds and provide users with a new perspective. For example, the reconstruction unit reconstructs visuals and stories based on past historical events and future scenarios. This makes it possible to provide users with a new perspective by incorporating different historical backgrounds and future scenarios.

[0038] The virtual reality providing unit can track the user's physical movements and gestures in the virtual reality experience and add interactive elements. The virtual reality providing unit, for example, builds a system that tracks the user's physical movements and gestures in the virtual reality experience and adds interactive elements. For example, the virtual reality providing unit tracks the user's hand movements and body movements to realize interactions within the virtual reality. In this way, by tracking the user's physical movements and gestures and adding interactive elements, a more immersive experience can be provided.

[0039] The virtual reality providing unit prepares multiple scenarios for the virtual reality experience and provides different developments depending on the user's selection, thereby realizing a more personalized experience. The virtual reality providing unit, for example, constructs a system that prepares multiple scenarios for the virtual reality experience and provides different developments depending on the user's selection. For example, different experiences are provided based on the scenario selected by the user. In this way, by preparing multiple scenarios and providing different developments depending on the user's selection, a more personalized experience can be realized.

[0040] The virtual reality providing unit provides a virtual reality experience in a multi-user environment in which multiple users can participate simultaneously, allowing users to relive memories with family and friends. The virtual reality providing unit, for example, builds a system that provides a virtual reality experience in a multi-user environment in which multiple users can participate simultaneously. For example, memories are relived in virtual reality with family and friends. By providing a multi-user environment in which multiple users can participate simultaneously, users can relive memories with family and friends.

[0041] The virtual reality providing unit makes the virtual reality experience available on different devices, thereby broadening the range of access. The virtual reality providing unit, for example, builds a system that makes the virtual reality experience available on different devices (e.g., smartphones, tablets). For example, the virtual reality experience is provided not only on a VR headset, but also on a smartphone or tablet. This makes it possible to broaden the range of access to the virtual reality experience by making it available on different devices.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The memory collection unit can analyze the user's brain waves and detect brain wave patterns when specific memories are activated. For example, when the user listens to specific music or smells a specific scent, the brain wave patterns are analyzed and episodes related to those memories are collected. This allows the user's memories to be collected more accurately using brain wave analysis. Furthermore, based on the brain wave patterns, it is possible to elicit latent memories that the user is not aware of. Furthermore, by using brain wave analysis, it is possible to collect emotions and memories that the user finds difficult to express in words.

[0044] The memory collection unit can analyze a user's SNS posts and message history to extract past events and emotional moments. For example, it can analyze a user's past SNS posts and collect posts related to specific events or occurrences. It can also analyze message history to extract emotional interactions the user had with friends and family. This allows the user's memories to be reconstructed more richly by analyzing SNS posts and message history. It can also recall forgotten memories based on past posts and messages.

[0045] The memory collection unit can analyze the user's lifestyle patterns and behavioral history to collect memories related to specific places and time periods. For example, it can analyze the location information of the user's smartphone to collect memories of frequent visits to specific places. It can also analyze the user's calendar and schedule to collect memories related to specific events and occurrences. By analyzing lifestyle patterns and behavioral history, it is possible to reconstruct the user's memories in more detail. Furthermore, by collecting memories related to specific places and time periods, it is possible to recreate parts of the user's life.

[0046] The memory collection unit can collect related memories based on the user's hobbies and interests. For example, it can collect memories of when the user participated in events or activities related to a particular hobby. It can also collect episodes related to themes that interest the user. By collecting memories based on hobbies and interests, it is possible to reconstruct memories that are particularly valuable to the user. Furthermore, by collecting memories related to hobbies and interests, it is possible to provide an experience that reflects the user's personality.

[0047] The memory collection unit can analyze the user's health data and collect memories related to specific health conditions or physical conditions. For example, it can analyze data from the user's fitness tracker or smartwatch to collect memories related to specific exercises or activities. It can also collect episodes related to the user's health conditions or physical conditions. This makes it possible to reconstruct memories related to the user's health conditions or physical conditions by analyzing the health data. Furthermore, collecting memories related to specific health conditions or physical conditions can also recreate parts of the user's life.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The memory collection unit collects the user's fragmented memories and episodes. For example, it can collect voice memos and diaries provided by the user, as well as episodes from family and friends. It can also use an emotion estimation function to evaluate the emotional value of the memories and episodes provided by the user and prioritize the collection of particularly emotionally strong episodes. Step 2: In the reconstruction section, the generative AI reconstructs visuals and stories based on the memories and episodes collected by the memory collection section. For example, it can analyze the user's past photos and videos to generate more realistic visuals. It can also use emotion estimation to evaluate the emotional impact of the reconstructed visuals and stories and select the version that most moves the user. Step 3: The virtual reality provider provides a virtual reality experience based on the visuals and story reconstructed by the reconstruction unit. For example, a user can wear a VR headset and experience a past moment recreated by the generation AI in virtual reality. The emotion estimation function can also be used to analyze the user's emotional responses in real time during the virtual reality experience and dynamically adjust the experience. Step 4: The animation generation unit generates an animated video based on the visuals and story reconstructed by the reconstruction unit. For example, an animated video is created based on the visuals and story reconstructed by the generation AI and provided to the user.

[0050] (Example 2) The memory revival AI system according to an embodiment of the present invention uses a generative AI to reconstruct past moments using visuals and stories based on the user's fragmented memories and episodes, providing virtual reality experiences and animated videos. This allows the user to recall forgotten memories and rediscover precious moments with the deceased.

[0051] A memory revival AI system according to an embodiment includes a memory collection unit, a reconstruction unit, a virtual reality provision unit, and an animation generation unit. The memory collection unit collects fragmentary memories and episodes from a user. For example, it can collect voice memos and diaries provided by the user, as well as episodes from family and friends. The memory collection unit can also use an emotion estimation function to evaluate the emotional value of the memories and episodes provided by the user and prioritize collection of particularly emotionally strong episodes. The reconstruction unit uses a generation AI to reconstruct visuals and stories based on the memories and episodes collected by the memory collection unit. For example, the generation AI can analyze the user's past photos and videos to generate more realistic visuals. The reconstruction unit can also use the emotion estimation function to evaluate the emotional impact of the reconstructed visuals and stories and select the version that most moves the user. The virtual reality provision unit provides a virtual reality experience based on the visuals and stories reconstructed by the reconstruction unit. For example, a user can wear a VR headset and experience past moments recreated by the generation AI in virtual reality. The virtual reality providing unit can also use an emotion estimation function to analyze the user's emotional reactions during the virtual reality experience in real time and dynamically adjust the experience content. The animation generating unit generates an animated video based on the visuals and story reconstructed by the reconstruction unit. For example, an animated video is created based on the visuals and story reconstructed by the generation AI and provided to the user. This allows the memory revival AI system according to the embodiment to evoke forgotten memories and rediscover precious moments with the deceased. For example, memories with the deceased can be recreated through a virtual reality experience and enjoyed as a video through an animated video. Furthermore, the interactive experience can deepen the connection with the deceased.

[0052] The memory collection unit can use the emotion estimation function to evaluate the emotional value of memories and episodes provided by the user and prioritize collection of episodes that are particularly emotionally strong. The memory collection unit, for example, uses the emotion estimation function on the memories and episodes provided by the user to quantify their emotional value. For example, it can automatically detect particularly moving parts of the episodes told by the user and prioritize collection based on their emotion scores. This allows for the prioritized collection of emotionally strong episodes, thereby providing a more moving experience.

[0053] The memory collection unit can analyze unstructured data such as a user's voice memos and diary entries and automatically extract fragmented memories. The memory collection unit, for example, analyzes voice memos provided by the user and builds a system that automatically extracts fragmented memories. For example, it uses voice recognition technology to extract specific keywords or phrases from the voice memos and identify memory fragments. This allows for the automatic extraction of fragmented memories from unstructured data, thereby collecting more memories.

[0054] When collecting episodes from family and friends, the generation AI automatically conducts interview-style dialogue, allowing for more detailed information to be extracted. The memory collection unit builds a system in which, for example, the generation AI asks questions to family and friends in an interview format to extract detailed episodes. For example, the AI ​​automatically generates questions and collects episodes in a dialogue format. This allows the generation AI to automatically conduct interview-style dialogue, allowing for more detailed information to be collected.

[0055] The memory collection unit allows the generation AI to automatically search the internet for related photos and videos based on memories and episodes provided by the user, and provide them as complementary information. The memory collection unit, for example, builds a system in which the generation AI automatically searches the internet for related photos and videos based on memories and episodes provided by the user. For example, it searches for images and videos related to specific places or events and provides them as complementary information. This enables richer memory reconstruction by automatically searching for related photos and videos and providing them as complementary information.

[0056] The memory collection unit can collect episodes from users of different cultures and regions and perform memory reconstruction taking cultural background into consideration. The memory collection unit, for example, collects episodes from users of different cultures and regions and builds a system that performs memory reconstruction taking cultural background into consideration. For example, it collects episodes related to a specific culture or region and performs memory reconstruction that reflects that background. This makes it possible to perform memory reconstruction taking into consideration the background of different cultures and regions.

[0057] The memory collection unit uses the emotion estimation function to analyze the emotional tone of memories and episodes provided by the user in real time, and the generation AI can suggest questions that will elicit positive emotions. For example, the memory collection unit uses the emotion estimation function to build a system in which the emotional tone of memories and episodes provided by the user in real time is analyzed, and the generation AI suggests questions that will elicit positive emotions. For example, the memory collection unit analyzes the tone of voice and facial expressions when the user speaks and generates appropriate questions. This makes it possible to provide a more moving experience by suggesting questions that will elicit positive emotions.

[0058] The reconstruction unit can use the emotion estimation function to evaluate the emotional impact of the reconstructed visuals and story and select the version that most moves the user. For example, the reconstruction unit uses the emotion estimation function to build a system that evaluates the emotional impact of the reconstructed visuals and story and selects the version that most moves the user. For example, the reconstruction unit selects the optimal version based on the emotion score. This allows the optimal experience to be provided to the user by evaluating the emotional impact and selecting the version that most moves the user.

[0059] The reconstruction unit can analyze the user's past photos and videos to generate more realistic visuals. For example, the reconstruction unit constructs a system in which a generation AI analyzes the user's past photos and videos to generate more realistic visuals. For example, realistic landscapes and people are reproduced based on data from past photos and videos. This allows the analysis of past photos and videos to generate more realistic visuals, providing the user with a more realistic experience.

[0060] The reconstruction unit can provide a more realistic experience by using voice synthesis that imitates the user's voice and speaking style when reconstructing a story. The reconstruction unit, for example, builds a system that provides a more realistic experience by using voice synthesis that imitates the user's voice and speaking style when reconstructing a story. For example, the reconstruction unit records the user's voice and performs voice synthesis based on that voice. In this way, a more realistic experience can be provided by using voice synthesis that imitates the user's voice and speaking style.

[0061] The reconstruction unit can generate reconstructed visuals and stories in different art styles to provide options to the user. The reconstruction unit, for example, builds a system that generates reconstructed visuals and stories in different art styles to provide options to the user. For example, the reconstruction unit generates watercolor-style or manga-style visuals to provide options to the user. This makes it possible to provide a variety of options to the user by generating them in different art styles.

[0062] The reconstruction unit generates visuals and stories that incorporate different historical backgrounds and future scenarios, and can provide users with a new perspective. The reconstruction unit, for example, builds a system that generates visuals and stories that incorporate different historical backgrounds and provide users with a new perspective. For example, the reconstruction unit reconstructs visuals and stories based on past historical events and future scenarios. This makes it possible to provide users with a new perspective by incorporating different historical backgrounds and future scenarios.

[0063] The reconstruction unit can use the emotion estimation function to monitor the user's emotional response to the reconstructed visuals and story in real time and continuously improve the optimal version. The reconstruction unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the reconstructed visuals and story in real time and continuously improves the optimal version. For example, the reconstruction unit analyzes the user's facial expressions and voice and improves the visuals and story based on the emotion score. This makes it possible to provide a more moving experience by monitoring the user's emotional response in real time and continuously improving the optimal version.

[0064] The virtual reality providing unit can use the emotion estimation function to analyze the user's emotional reactions during the virtual reality experience in real time and dynamically adjust the experience content. For example, the virtual reality providing unit uses the emotion estimation function to build a system that analyzes the user's emotional reactions during the virtual reality experience in real time and dynamically adjusts the experience content. For example, the virtual reality providing unit analyzes the user's facial expressions and voice and adjusts the experience content based on the emotion score. In this way, a more personalized experience can be provided by analyzing the user's emotional reactions in real time and dynamically adjusting the experience content.

[0065] The virtual reality providing unit can track the user's physical movements and gestures in the virtual reality experience and add interactive elements. The virtual reality providing unit, for example, builds a system that tracks the user's physical movements and gestures in the virtual reality experience and adds interactive elements. For example, the virtual reality providing unit tracks the user's hand movements and body movements to realize interactions within the virtual reality. In this way, by tracking the user's physical movements and gestures and adding interactive elements, a more immersive experience can be provided.

[0066] The virtual reality providing unit prepares multiple scenarios for the virtual reality experience and provides different developments depending on the user's selection, thereby realizing a more personalized experience. The virtual reality providing unit, for example, constructs a system that prepares multiple scenarios for the virtual reality experience and provides different developments depending on the user's selection. For example, different experiences are provided based on the scenario selected by the user. In this way, by preparing multiple scenarios and providing different developments depending on the user's selection, a more personalized experience can be realized.

[0067] The virtual reality providing unit provides a virtual reality experience in a multi-user environment in which multiple users can participate simultaneously, allowing users to relive memories with family and friends. The virtual reality providing unit, for example, builds a system that provides a virtual reality experience in a multi-user environment in which multiple users can participate simultaneously. For example, memories are relived in virtual reality with family and friends. By providing a multi-user environment in which multiple users can participate simultaneously, users can relive memories with family and friends.

[0068] The virtual reality providing unit makes the virtual reality experience available on different devices, thereby broadening the range of access. The virtual reality providing unit, for example, builds a system that makes the virtual reality experience available on different devices (e.g., smartphones, tablets). For example, the virtual reality experience is provided not only on a VR headset, but also on a smartphone or tablet. This makes it possible to broaden the range of access to the virtual reality experience by making it available on different devices.

[0069] The virtual reality providing unit can use the emotion estimation function to analyze the user's emotional response during the virtual reality experience and add interactive elements to elicit positive emotions. The virtual reality providing unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional response during the virtual reality experience and adds interactive elements to elicit positive emotions. For example, the virtual reality providing unit analyzes the user's facial expressions and voice and adds elements to elicit positive emotions. In this way, a more moving experience can be provided by analyzing the user's emotional response and adding interactive elements to elicit positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The memory collection unit can analyze the user's brain waves and detect brain wave patterns when specific memories are activated. For example, when the user listens to specific music or smells a specific scent, the brain wave patterns are analyzed and episodes related to those memories are collected. This allows the user's memories to be collected more accurately using brain wave analysis. Furthermore, based on the brain wave patterns, it is possible to elicit latent memories that the user is not aware of. Furthermore, by using brain wave analysis, it is possible to collect emotions and memories that the user finds difficult to express in words.

[0072] The memory collection unit can analyze a user's SNS posts and message history to extract past events and emotional moments. For example, it can analyze a user's past SNS posts and collect posts related to specific events or occurrences. It can also analyze message history to extract emotional interactions the user had with friends and family. This allows the user's memories to be reconstructed more richly by analyzing SNS posts and message history. It can also recall forgotten memories based on past posts and messages.

[0073] The memory collection unit can analyze the user's lifestyle patterns and behavioral history to collect memories related to specific places and time periods. For example, it can analyze the location information of the user's smartphone to collect memories of frequent visits to specific places. It can also analyze the user's calendar and schedule to collect memories related to specific events and occurrences. By analyzing lifestyle patterns and behavioral history, it is possible to reconstruct the user's memories in more detail. Furthermore, by collecting memories related to specific places and time periods, it is possible to recreate parts of the user's life.

[0074] The memory collection unit can collect related memories based on the user's hobbies and interests. For example, it can collect memories of when the user participated in events or activities related to a particular hobby. It can also collect episodes related to themes that interest the user. By collecting memories based on hobbies and interests, it is possible to reconstruct memories that are particularly valuable to the user. Furthermore, by collecting memories related to hobbies and interests, it is possible to provide an experience that reflects the user's personality.

[0075] The memory collection unit can analyze the user's health data and collect memories related to specific health conditions or physical conditions. For example, it can analyze data from the user's fitness tracker or smartwatch to collect memories related to specific exercises or activities. It can also collect episodes related to the user's health conditions or physical conditions. This makes it possible to reconstruct memories related to the user's health conditions or physical conditions by analyzing the health data. Furthermore, collecting memories related to specific health conditions or physical conditions can also recreate parts of the user's life.

[0076] The memory collection unit uses its emotion estimation function to analyze the emotional tone of memories and episodes provided by the user in real time, allowing the AI ​​to suggest questions that elicit positive emotions. For example, it can analyze the tone of voice and facial expressions when the user speaks and generate appropriate questions. This allows for a more moving experience by suggesting questions that elicit positive emotions. Furthermore, analyzing emotional tone in real time also makes it possible to dynamically adjust questions according to the user's emotions. Furthermore, suggesting questions that elicit positive emotions can enrich the user's experience.

[0077] The reconstruction unit can use the emotion estimation function to evaluate the emotional impact of the reconstructed visuals and story and select the version that most moves the user. For example, the optimal version is selected based on the emotion score. This allows the optimal experience to be provided to the user by evaluating the emotional impact and selecting the most moving version. Furthermore, by evaluating the emotional impact, it is also possible to provide visuals and stories that correspond to the user's emotions. Furthermore, selecting the optimal version based on the emotion score can make the user's experience more moving.

[0078] The virtual reality providing unit can use the emotion estimation function to analyze the user's emotional response during the virtual reality experience in real time and dynamically adjust the experience content. For example, the virtual reality providing unit can analyze the user's facial expressions and voice and adjust the experience content based on the emotion score. This allows the user's emotional response to be analyzed in real time and the experience content to be dynamically adjusted, thereby providing a more personalized experience. Furthermore, by analyzing the emotional response in real time, it is also possible to provide an experience that corresponds to the user's emotions. Furthermore, by dynamically adjusting the experience content, the user's experience can be made more moving.

[0079] The virtual reality providing unit can use the emotion estimation function to analyze the user's emotional response during the virtual reality experience and add interactive elements to elicit positive emotions. For example, the virtual reality providing unit can analyze the user's facial expressions and voice and add elements to elicit positive emotions. This allows the user to analyze the user's emotional response and add interactive elements to elicit positive emotions, thereby providing a more moving experience. Furthermore, by analyzing the emotional response, it is also possible to provide interactive elements according to the user's emotions. Furthermore, adding elements to elicit positive emotions can further enrich the user's experience.

[0080] The virtual reality providing unit can use the emotion estimation function to monitor the user's emotional responses in real time during the virtual reality experience and continuously improve the optimal version. For example, it can analyze the user's facial expressions and voice and improve the visuals and story based on the emotional score. This allows the unit to provide a more moving experience by monitoring the user's emotional responses in real time and continuously improving the optimal version. Furthermore, by monitoring the emotional responses in real time, it is possible to provide visuals and stories that correspond to the user's emotions. Furthermore, by continuously improving the optimal version, the user's experience can be further enriched.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The memory collection unit collects the user's fragmented memories and episodes. For example, it can collect voice memos and diaries provided by the user, as well as episodes from family and friends. It can also use an emotion estimation function to evaluate the emotional value of the memories and episodes provided by the user and prioritize the collection of particularly emotionally strong episodes. Step 2: In the reconstruction section, the generative AI reconstructs visuals and stories based on the memories and episodes collected by the memory collection section. For example, it can analyze the user's past photos and videos to generate more realistic visuals. It can also use emotion estimation to evaluate the emotional impact of the reconstructed visuals and stories and select the version that most moves the user. Step 3: The virtual reality provider provides a virtual reality experience based on the visuals and story reconstructed by the reconstruction unit. For example, a user can wear a VR headset and experience a past moment recreated by the generation AI in virtual reality. The emotion estimation function can also be used to analyze the user's emotional responses in real time during the virtual reality experience and dynamically adjust the experience. Step 4: The animation generation unit generates an animated video based on the visuals and story reconstructed by the reconstruction unit. For example, an animated video is created based on the visuals and story reconstructed by the generation AI and provided to the user.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[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 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.

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

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

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

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 memory collection unit that collects fragmentary memories and episodes of a user; a reconstruction unit that reconstructs visuals and stories based on the memories and episodes collected by the memory collection unit; a virtual reality providing unit that provides a virtual reality experience based on the visuals and story reconstructed by the reconstruction unit; an animation generation unit that generates an animation video based on the visuals and story reconstructed by the reconstruction unit; A system characterized by:

2. The memory collection unit Using an emotion estimation function, the emotional value of the memories and episodes provided by the user is evaluated, and episodes with particularly strong emotions are preferentially collected.

2. The system of claim 1.

3. The memory collection unit Based on the memories and episodes provided by the user, the generating AI automatically searches the internet for related photos and videos and provides them as complementary information.

2. The system of claim 1.

4. The reconstruction unit Using an emotion estimation function, the emotional impact of the reconstructed visuals and the story is evaluated, and the version that most moves the user is selected.

2. The system of claim 1.

5. The virtual reality providing unit Using an emotion estimation function, the user's emotional responses during the virtual reality experience are analyzed in real time, and the experience content is dynamically adjusted.

2. The system of claim 1.

6. The memory collection unit Collecting these stories from users in different cultures and regions, and reconstructing memories taking cultural background into account 2. The system of claim 1.

7. The reconstruction unit When reconstructing a story, voice synthesis is used to mimic the user's voice and speaking style, providing a more immersive experience.

2. The system of claim 1.

8. The virtual reality providing unit The virtual reality experience is provided in a multi-user environment in which multiple users can participate simultaneously, and the user re-experiences the memories with family and friends.

2. The system of claim 1.

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