System

Abstract

An object of a system according to an embodiment is to share memories when sending a souvenir.SOLUTION: A system includes a memory attachment part, a report generation part, and a line cooperation part. The memory attachment unit attaches a photograph or a report of a trip. The report generation unit generates a report based on the photograph or the report attached by the memory attachment unit. The line cooperation unit transmits the report or photograph generated by the report generation unit through the line.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 limited ways to share memories when sending souvenirs, and has the drawback of being limited to simply sending physical items.

[0005] The system according to the embodiment aims to share memories when sending souvenirs. [Means for solving the problem]

[0006] The system according to the embodiment includes a memory attachment unit, a report generation unit, and a LINE linkage unit. The memory attachment unit attaches photos or reports of the trip. The report generation unit generates a report based on the photos or reports attached by the memory attachment unit. The LINE linkage unit sends the report or photos generated by the report generation unit via LINE. [Effects of the Invention]

[0007] The system according to the embodiment allows users to share memories when sending souvenirs. [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 ChatGPT service, linked to LINE, according to an embodiment of the present invention, is a system that allows users to send souvenirs purchased in Tokyo to friends and relatives with personal memories attached. This system allows users to send travel photos and reports, which can be created manually or by a generation AI. This allows the ChatGPT service, linked to LINE, to share not just souvenirs, but also the sender's travel memories.

[0029] The LINE-linked ChatGPT service according to the embodiment includes a memory attachment unit, a report generation unit, and a LINE linkage unit. The memory attachment unit attaches travel photos and reports. For example, a user can attach photos taken at Tokyo Tower or a report on delicious food eaten in Asakusa. The memory attachment unit can also attach photos taken during a trip or a travel diary written by the user. The report generation unit generates a report using a generation AI based on the photos and reports attached by the memory attachment unit. For example, the generation AI may use a text generation AI (e.g., LLM) to create a report on an experience at Tokyo Tower. The generation AI may also use a multimodal generation AI to generate a report based on the content of the photos and reports. The generation AI may also generate a report based on prompts entered by a user. The LINE linkage unit transmits the reports and photos generated by the report generation unit via LINE. For example, the photos and reports are displayed on the LINE chat screen, allowing the recipient to enjoy their souvenirs while viewing them. The LINE linkage unit can also transmit the generated reports and photos in real time. As a result, the ChatGPT service, which is linked to LINE in the embodiment, allows the recipient to share the sender's travel experience by attaching memories to souvenirs and sending them via LINE.

[0030] The memory attachment unit can automatically analyze the sender's travel route and organize and attach memories for each place visited. For example, the memory attachment unit can automatically analyze the sender's travel route using GPS data and organize photos and reports for each place visited. For example, the photos and reports for each place visited are arranged in chronological order. The memory attachment unit can also analyze the travel route and build a system that organizes memories for each place visited. For example, the photos and reports for each place visited are displayed on a map. The memory attachment unit can also analyze the sender's travel route and automatically organize and attach memories for each place visited. For example, the photos and reports for each place visited are automatically classified. This allows memories to be organized and attached based on the sender's travel route.

[0031] The memory attachment unit can attach an audio message recounting travel memories in the sender's voice. The memory attachment unit, for example, adds a function to attach an audio message to a souvenir, recounting travel memories in the sender's voice. For example, the sender records an episode from the trip and attaches it to the souvenir. The memory attachment unit also builds a system that automatically generates an audio message recounting travel memories in the sender's voice. For example, it converts text entered by the sender into audio and attaches it to the souvenir. The memory attachment unit also adds a function to attach an audio message, recounting travel memories in the sender's voice. For example, the sender records an episode from the trip and automatically attaches it to the souvenir. This makes it possible to attach an audio message recounting travel memories in the sender's voice.

[0032] The memory attachment unit can automatically generate and attach a short video of the sender's travel memories. The memory attachment unit, for example, uses generation AI to build a system that automatically generates a short video of the sender's travel memories. For example, it generates a short video by combining photos and videos. The memory attachment unit also automatically generates a short video of the sender's travel memories and attaches it to a souvenir. For example, it generates a short video that summarizes the highlights of the trip. The memory attachment unit also uses generation AI to automatically generate a short video of the sender's travel memories. For example, it analyzes photos and videos and selects the best scenes to generate a short video. This allows the sender's travel memories to be automatically generated and attached as a short video.

[0033] The report generation unit can refer to the sender's past travel records to create a more detailed and personalized report. For example, the report generation unit uses a generation AI to refer to the sender's past travel records to create a detailed report. For example, the report is generated based on past travel photos and notes. The report generation unit also analyzes the sender's past travel records to build a system that automatically generates personalized reports. For example, the report is generated based on past travel data. The report generation unit also uses a generation AI to refer to the sender's past travel records to create a detailed report. For example, the report is generated based on past travel episodes. This allows the sender to refer to their past travel records to create a detailed and personalized report.

[0034] The report generation unit can analyze the sender's photo and automatically generate a detailed description based on the photo. The report generation unit, for example, builds a system that analyzes the sender's photo and automatically generates a detailed description based on the photo. For example, it analyzes the content of the photo and generates a description. The report generation unit also uses a generation AI to analyze the sender's photo and automatically generate a detailed description. For example, it includes information about the background and subject of the photo in the description. The report generation unit also analyzes the sender's photo and automatically generates a description based on the photo. For example, it creates a story based on the content of the photo and provides it as a description. This makes it possible to analyze the sender's photo and automatically generate a detailed description.

[0035] The report generation unit can add a function to turn the sender's travel memories into a manga-style report. The report generation unit, for example, uses generation AI to add a function to turn the sender's travel memories into a manga-style report. For example, travel episodes are drawn as manga. The report generation unit also adds a function to turn the sender's travel memories into a manga-style report. For example, travel events are expressed as manga. The report generation unit also uses generation AI to turn the sender's travel memories into a manga-style report. For example, travel episodes are drawn as manga. This can add a function to turn the sender's travel memories into a manga-style report.

[0036] The LINE integration unit can add a function to the LINE chat screen that displays the sender's travel route on a map and displays memories for each place visited. For example, the LINE integration unit adds a function to the LINE chat screen that displays the sender's travel route on a map. For example, it displays photos and reports for each place visited. The LINE integration unit also builds a system that displays the sender's travel route on a map and displays memories for each place visited. For example, it maps photos and reports of each place visited on a map. The LINE integration unit also builds a system that displays the sender's travel route on a map and displays memories for each place visited. For example, it displays photos and reports of each place visited on a map. This makes it possible to add a function to display the sender's travel route on a map and display memories for each place visited.

[0037] The LINE integration unit can add a function to display the sender's travel memories in AR on the LINE chat screen. The LINE integration unit, for example, adds a function to display the sender's travel memories in AR on the LINE chat screen. For example, photos and videos of places visited are displayed in AR. The LINE integration unit also builds a system to display the sender's travel memories in AR. For example, it displays AR content on the LINE chat screen. The LINE integration unit also displays the sender's travel memories in AR on the LINE chat screen. For example, it displays photos and videos of places visited in AR. This allows the addition of a function to display the sender's travel memories in AR.

[0038] The LINE integration unit can add a function to display the sender's travel memories as a 3D model on the LINE chat screen. The LINE integration unit, for example, adds a function to display the sender's travel memories as a 3D model on the LINE chat screen. For example, it displays a 3D model of the places visited. The LINE integration unit also builds a system to display the sender's travel memories as a 3D model. For example, it displays a 3D model on the LINE chat screen. The LINE integration unit also builds a system to display the sender's travel memories as a 3D model on the LINE chat screen. For example, it displays a 3D model of the places visited. This allows the addition of a function to display the sender's travel memories as a 3D model.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The ChatGPT service, which is linked to LINE, can also incorporate game elements. For example, it can generate quizzes or mini-games based on the sender's visited places and experiences, allowing the recipient to enjoy them. Incorporating game elements also encourages the recipient to participate more actively and share the sender's memories. This allows for an interactive experience that goes beyond simply sending reports and photos.

[0041] The ChatGPT service, which is linked to LINE, can also be equipped with a health information linking module. This module monitors the sender's health status and can provide travel advice based on that information. For example, it can suggest appropriate rest times and meal recommendations based on the number of steps taken and heart rate. The health information linking module can also generate reports based on the sender's health status. This supports health-conscious travel planning, allowing users to enjoy their trip with peace of mind.

[0042] The ChatGPT service, which is linked to LINE, can also be equipped with an eco-information linking module. This module records the eco-activities of the sender during their trip and generates reports based on that information. For example, if the sender uses eco-friendly accommodations or transportation, that information will be included in the report. The eco-information linking module can also provide advice based on the sender's eco-activities. This will support environmentally conscious travel and raise eco-consciousness.

[0043] The LINE-linked ChatGPT service can also be equipped with a cultural information linking module. This module provides information about the culture and history of the places visited by the sender and can generate reports based on that information. For example, the report can include the historical background and cultural characteristics of the places visited. The cultural information linking module can also provide cultural information based on the sender's interests. This allows for a deeper understanding of the travel experience and provides cultural learning.

[0044] The ChatGPT service, which is linked to LINE, can also include a food information linking module. This module provides information about the food eaten at the places the sender visited and can generate a report based on that information. For example, the report can include local specialties and recipes. The food information linking module can also provide food information based on the sender's food preferences. This can enrich the food experience during travel and deepen knowledge about food.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: In the Memories section, you can attach photos and travel reports. For example, you can attach photos taken at Tokyo Tower or a report on the delicious food you ate in Asakusa. You can also attach photos taken during your trip or travel journals you wrote yourself. Step 2: The report generation unit uses a generation AI to generate a report based on the photos and reports attached by the memory attachment unit. For example, a text generation AI (e.g., LLM) can be used to create a report about the experience at Tokyo Tower. A multimodal generation AI can also be used to generate a report based on the content of the photos and reports. Furthermore, a report can be generated based on prompts entered by the user. Step 3: The LINE integration unit sends the reports and photos generated by the report generation unit via LINE. For example, the photos and reports will be displayed on the LINE chat screen, allowing the recipient to enjoy their souvenirs while looking at them. The generated reports and photos can also be sent in real time.

[0047] (Example 2) The ChatGPT service, linked to LINE, according to an embodiment of the present invention, is a system that allows users to send souvenirs purchased in Tokyo to friends and relatives with personal memories attached. This system allows users to send travel photos and reports, which can be created manually or by a generation AI. This allows the ChatGPT service, linked to LINE, to share not just souvenirs, but also the sender's travel memories.

[0048] The LINE-linked ChatGPT service according to the embodiment includes a memory attachment unit, a report generation unit, and a LINE linkage unit. The memory attachment unit attaches travel photos and reports. For example, a user can attach photos taken at Tokyo Tower or a report on delicious food eaten in Asakusa. The memory attachment unit can also attach photos taken during a trip or a travel diary written by the user. The report generation unit generates a report using a generation AI based on the photos and reports attached by the memory attachment unit. For example, the generation AI may use a text generation AI (e.g., LLM) to create a report on an experience at Tokyo Tower. The generation AI may also use a multimodal generation AI to generate a report based on the content of the photos and reports. The generation AI may also generate a report based on prompts entered by a user. The LINE linkage unit transmits the reports and photos generated by the report generation unit via LINE. For example, the photos and reports are displayed on the LINE chat screen, allowing the recipient to enjoy their souvenirs while viewing them. The LINE linkage unit can also transmit the generated reports and photos in real time. As a result, the ChatGPT service, which is linked to LINE in the embodiment, allows the recipient to share the sender's travel experience by attaching memories to souvenirs and sending them via LINE.

[0049] The memory attachment unit can analyze the sender's emotions and automatically select the most appropriate photos and reports based on those emotions. For example, to analyze the sender's emotions, the memory attachment unit uses a generation AI to analyze the sender's past messages and posts and calculate an emotion score. For example, it may prioritize the selection of photos and reports with strong positive emotions. The memory attachment unit also analyzes the sender's emotions in real time and automatically selects the most appropriate photos and reports based on those emotions. For example, if the sender is feeling happy, it may select photos and reports that match that emotion. The memory attachment unit also builds a system that automatically selects photos and reports that best suit the sender's emotions based on the results of the emotion analysis. For example, it may prioritize the selection of photos and reports with a high emotion score. This makes it possible to select the most appropriate photos and reports based on the sender's emotions.

[0050] The memory attachment unit can automatically analyze the sender's travel route and organize and attach memories for each place visited. For example, the memory attachment unit can automatically analyze the sender's travel route using GPS data and organize photos and reports for each place visited. For example, the photos and reports for each place visited are arranged in chronological order. The memory attachment unit can also analyze the travel route and build a system that organizes memories for each place visited. For example, the photos and reports for each place visited are displayed on a map. The memory attachment unit can also analyze the sender's travel route and automatically organize and attach memories for each place visited. For example, the photos and reports for each place visited are automatically classified. This allows memories to be organized and attached based on the sender's travel route.

[0051] The memory attachment unit can generate a customized message based on the emotions of the sender and send it along with the memories. The memory attachment unit, for example, uses an emotion estimation function to generate a customized message based on the emotions of the sender. For example, if the sender is moved, a message reflecting that emotion is generated. The memory attachment unit also analyzes the emotions of the sender in real time and builds a system that generates a customized message based on that emotion. For example, if the sender is feeling happy, a message that matches that emotion is generated. The memory attachment unit also uses the emotion estimation function to automatically generate a customized message that is most suitable for the emotions of the sender. For example, messages with a high emotion score are generated preferentially. This allows a customized message to be generated based on the emotions of the sender and sent along with the memories.

[0052] The memory attachment unit can attach an audio message recounting travel memories in the sender's voice. The memory attachment unit, for example, adds a function to attach an audio message to a souvenir, recounting travel memories in the sender's voice. For example, the sender records an episode from the trip and attaches it to the souvenir. The memory attachment unit also builds a system that automatically generates an audio message recounting travel memories in the sender's voice. For example, it converts text entered by the sender into audio and attaches it to the souvenir. The memory attachment unit also adds a function to attach an audio message, recounting travel memories in the sender's voice. For example, the sender records an episode from the trip and automatically attaches it to the souvenir. This makes it possible to attach an audio message recounting travel memories in the sender's voice.

[0053] The memory attachment unit can automatically generate and attach a short video of the sender's travel memories. The memory attachment unit, for example, uses generation AI to build a system that automatically generates a short video of the sender's travel memories. For example, it generates a short video by combining photos and videos. The memory attachment unit also automatically generates a short video of the sender's travel memories and attaches it to a souvenir. For example, it generates a short video that summarizes the highlights of the trip. The memory attachment unit also uses generation AI to automatically generate a short video of the sender's travel memories. For example, it analyzes photos and videos and selects the best scenes to generate a short video. This allows the sender's travel memories to be automatically generated and attached as a short video.

[0054] The memory attachment unit can select music based on the sender's emotions and send it along with the memories. The memory attachment unit, for example, uses an emotion estimation function to build a system that selects music based on the sender's emotions. For example, if the sender is moved, music that matches that emotion is selected. The memory attachment unit also analyzes the sender's emotions in real time and selects music based on those emotions. For example, if the sender is feeling happy, music that matches that emotion is selected. The memory attachment unit also uses the emotion estimation function to automatically select music that best suits the sender's emotions. For example, music with a high emotion score is preferentially selected. This allows music to be selected based on the sender's emotions and sent along with the memories.

[0055] The report generation unit can refer to the sender's past travel records to create a more detailed and personalized report. For example, the report generation unit uses a generation AI to refer to the sender's past travel records to create a detailed report. For example, the report is generated based on past travel photos and notes. The report generation unit also analyzes the sender's past travel records to build a system that automatically generates personalized reports. For example, the report is generated based on past travel data. The report generation unit also uses a generation AI to refer to the sender's past travel records to create a detailed report. For example, the report is generated based on past travel episodes. This allows the sender to refer to their past travel records to create a detailed and personalized report.

[0056] The report generation unit can analyze the sender's photo and automatically generate a detailed description based on the photo. The report generation unit, for example, builds a system that analyzes the sender's photo and automatically generates a detailed description based on the photo. For example, it analyzes the content of the photo and generates a description. The report generation unit also uses a generation AI to analyze the sender's photo and automatically generate a detailed description. For example, it includes information about the background and subject of the photo in the description. The report generation unit also analyzes the sender's photo and automatically generates a description based on the photo. For example, it creates a story based on the content of the photo and provides it as a description. This makes it possible to analyze the sender's photo and automatically generate a detailed description.

[0057] The report generation unit uses the emotion estimation function to generate a report that reflects the emotions of the sender, thereby eliciting emotional empathy. The report generation unit, for example, uses the emotion estimation function to build a system that generates a report that reflects the emotions of the sender. For example, if the sender is moved, a report that reflects that emotion is generated. The report generation unit also analyzes the emotions of the sender in real time and generates a report that reflects that emotion. For example, if the sender is feeling happy, a report that matches that emotion is generated. The report generation unit also uses the emotion estimation function to automatically generate a report that is most suitable for the emotions of the sender. For example, reports with high emotion scores are generated preferentially. This allows the report that reflects the emotions of the sender to be generated, thereby eliciting emotional empathy.

[0058] The report generation unit can add a function to turn the sender's travel memories into a manga-style report. The report generation unit, for example, uses generation AI to add a function to turn the sender's travel memories into a manga-style report. For example, travel episodes are drawn as manga. The report generation unit also adds a function to turn the sender's travel memories into a manga-style report. For example, travel events are expressed as manga. The report generation unit also uses generation AI to turn the sender's travel memories into a manga-style report. For example, travel episodes are drawn as manga. This can add a function to turn the sender's travel memories into a manga-style report.

[0059] The report generation unit can use the emotion estimation function to generate an illustration based on the emotion of the sender and attach it to the report. The report generation unit, for example, uses the emotion estimation function to build a system that generates an illustration based on the emotion of the sender. For example, if the sender is moved, an illustration that reflects that emotion is generated. The report generation unit also analyzes the emotion of the sender in real time and generates an illustration based on that emotion. For example, if the sender is feeling happy, an illustration that matches that emotion is generated. The report generation unit also uses the emotion estimation function to automatically generate an illustration that best suits the emotion of the sender. For example, illustrations with a high emotion score are generated preferentially. This allows an illustration based on the emotion of the sender to be generated and attached to the report.

[0060] The LINE integration unit uses generation AI to automatically generate messages to be displayed on the LINE chat screen, and can reflect the emotions of the sender. The LINE integration unit, for example, uses generation AI to build a system that automatically generates messages to be displayed on the LINE chat screen. For example, it generates a message that reflects the emotions of the sender. The LINE integration unit also analyzes the emotions of the sender in real time and automatically generates a message that reflects those emotions. For example, if the sender is feeling happy, it generates a message that matches that emotion. The LINE integration unit also uses generation AI to automatically generate a message that is most suitable for the emotions of the sender. For example, it prioritizes the generation of messages with a high emotion score. This allows a message that reflects the emotions of the sender to be automatically generated and displayed on the LINE chat screen.

[0061] The LINE integration unit can add a function to the LINE chat screen that displays the sender's travel route on a map and displays memories for each place visited. For example, the LINE integration unit adds a function to the LINE chat screen that displays the sender's travel route on a map. For example, it displays photos and reports for each place visited. The LINE integration unit also builds a system that displays the sender's travel route on a map and displays memories for each place visited. For example, it maps photos and reports of each place visited on a map. The LINE integration unit also builds a system that displays the sender's travel route on a map and displays memories for each place visited. For example, it displays photos and reports of each place visited on a map. This makes it possible to add a function to display the sender's travel route on a map and display memories for each place visited.

[0062] The LINE integration unit can use the emotion estimation function to automatically add emotional emojis and stamps to messages displayed on the LINE chat screen. The LINE integration unit, for example, uses the emotion estimation function to build a system that automatically adds emotional emojis and stamps to messages displayed on the LINE chat screen. For example, it selects emojis and stamps that match the sender's emotions. The LINE integration unit also analyzes the sender's emotions in real time and automatically adds emojis and stamps based on those emotions. For example, if the sender is feeling happy, it adds emojis and stamps that match that emotion. The LINE integration unit also uses the emotion estimation function to automatically add emojis and stamps that are most suitable for the sender's emotions. For example, it prioritizes adding emojis and stamps with a high emotion score. This makes it possible to automatically add emotional emojis and stamps to messages displayed on the LINE chat screen using the emotion estimation function.

[0063] The LINE integration unit can add a function to display the sender's travel memories in AR on the LINE chat screen. The LINE integration unit, for example, adds a function to display the sender's travel memories in AR on the LINE chat screen. For example, photos and videos of places visited are displayed in AR. The LINE integration unit also builds a system to display the sender's travel memories in AR. For example, it displays AR content on the LINE chat screen. The LINE integration unit also displays the sender's travel memories in AR on the LINE chat screen. For example, it displays photos and videos of places visited in AR. This allows the addition of a function to display the sender's travel memories in AR.

[0064] The LINE integration unit can add a function to display the sender's travel memories as a 3D model on the LINE chat screen. The LINE integration unit, for example, adds a function to display the sender's travel memories as a 3D model on the LINE chat screen. For example, it displays a 3D model of the places visited. The LINE integration unit also builds a system to display the sender's travel memories as a 3D model. For example, it displays a 3D model on the LINE chat screen. The LINE integration unit also builds a system to display the sender's travel memories as a 3D model on the LINE chat screen. For example, it displays a 3D model of the places visited. This allows the addition of a function to display the sender's travel memories as a 3D model.

[0065] The LINE integration unit can use the emotion estimation function to automatically add emotional background images to messages displayed on the LINE chat screen. The LINE integration unit, for example, uses the emotion estimation function to build a system that automatically adds emotional background images to messages displayed on the LINE chat screen. For example, it selects a background image that matches the emotion of the sender. The LINE integration unit also analyzes the emotion of the sender in real time and automatically adds a background image based on that emotion. For example, if the sender is feeling happy, it adds a background image that matches that emotion. The LINE integration unit also uses the emotion estimation function to automatically add a background image that is most suitable for the emotion of the sender. For example, it prioritizes adding background images with a high emotion score. In this way, the emotion estimation function can be used to automatically add emotional background images to messages displayed on the LINE chat screen.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The LINE-linked ChatGPT service can also be equipped with a speech recognition module. This module converts what the sender says into text and generates a report based on that text. For example, when the sender talks about a travel episode, the content is automatically converted into text, and the report generation module uses that text to create a detailed report. The speech recognition module can also analyze the tone and speed of the sender's voice and infer their emotions based on that information. This allows the report to be generated based on what the sender said, providing a more personalized experience.

[0068] The LINE-linked ChatGPT service can also include a translation unit. This can translate text entered by the sender and generated reports into multiple languages. For example, text entered in Japanese can be translated into English or Chinese and sent to friends or relatives overseas. The translation unit can also reflect the sender's emotions. For example, it can translate a moving episode with rich emotion. This allows travel memories to be shared with more people, transcending language barriers.

[0069] The LINE-linked ChatGPT service can also be equipped with a feedback collection module. This module collects feedback from the recipient and uses that information to improve the service. For example, when the recipient comments or rates a report or photo, that information is automatically collected and reflected in the next report. The feedback collection module can also analyze the recipient's emotions and provide feedback based on those emotions. This facilitates smoother communication between the sender and recipient and improves the quality of the service.

[0070] The ChatGPT service, which is linked to LINE, can also be equipped with a calendar integration section. This section can obtain the sender's calendar information and generate reports based on the travel schedule. For example, it can automatically generate reports for each day based on the travel dates and plans registered in the calendar. The calendar integration section can also analyze the sender's emotions and suggest schedules based on those emotions. This allows for consistent support from travel planning to report generation.

[0071] The LINE-linked ChatGPT service can also be equipped with a weather information linking module. This module can obtain weather information for the sender's travel destination and generate a report based on that information. For example, by including information about the weather during the trip in the report, a more detailed travel record can be provided. The weather information linking module can also analyze the sender's emotions and provide weather information based on those emotions. This will enrich travel memories and enable the provision of information that is in line with the sender's emotions.

[0072] The ChatGPT service, which is linked to LINE, can also incorporate game elements. For example, it can generate quizzes or mini-games based on the sender's visited places and experiences, allowing the recipient to enjoy them. Incorporating game elements also encourages the recipient to participate more actively and share the sender's memories. This allows for an interactive experience that goes beyond simply sending reports and photos.

[0073] The ChatGPT service, which is linked to LINE, can also be equipped with a health information linking module. This module monitors the sender's health status and can provide travel advice based on that information. For example, it can suggest appropriate rest times and meal recommendations based on the number of steps taken and heart rate. The health information linking module can also generate reports based on the sender's health status. This supports health-conscious travel planning, allowing users to enjoy their trip with peace of mind.

[0074] The ChatGPT service, which is linked to LINE, can also be equipped with an eco-information linking module. This module records the eco-activities of the sender during their trip and generates reports based on that information. For example, if the sender uses eco-friendly accommodations or transportation, that information will be included in the report. The eco-information linking module can also provide advice based on the sender's eco-activities. This will support environmentally conscious travel and raise eco-consciousness.

[0075] The LINE-linked ChatGPT service can also be equipped with a cultural information linking module. This module provides information about the culture and history of the places visited by the sender and can generate reports based on that information. For example, the report can include the historical background and cultural characteristics of the places visited. The cultural information linking module can also provide cultural information based on the sender's interests. This allows for a deeper understanding of the travel experience and provides cultural learning.

[0076] The ChatGPT service, which is linked to LINE, can also include a food information linking module. This module provides information about the food eaten at the places the sender visited and can generate a report based on that information. For example, the report can include local specialties and recipes. The food information linking module can also provide food information based on the sender's food preferences. This can enrich the food experience during travel and deepen knowledge about food.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: In the Memories section, you can attach photos and travel reports. For example, you can attach photos taken at Tokyo Tower or a report on the delicious food you ate in Asakusa. You can also attach photos taken during your trip or travel journals you wrote yourself. Step 2: The report generation unit uses a generation AI to generate a report based on the photos and reports attached by the memory attachment unit. For example, a text generation AI (e.g., LLM) can be used to create a report about the experience at Tokyo Tower. A multimodal generation AI can also be used to generate a report based on the content of the photos and reports. Furthermore, a report can be generated based on prompts entered by the user. Step 3: The LINE integration unit sends the reports and photos generated by the report generation unit via LINE. For example, the photos and reports will be displayed on the LINE chat screen, allowing the recipient to enjoy their souvenirs while looking at them. The generated reports and photos can also be sent in real time.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 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.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 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 attachment section for attaching photos or travel reports; a report generating unit that generates a report based on the photo or report attached by the memory attaching unit; a line linking unit that transmits the report or photo generated by the report generating unit via line; A system characterized by:

2. The memory attachment unit includes: Analyzing the emotions of the sender and automatically selecting the most appropriate photo or report based on those emotions 2. The system of claim 1.

3. The memory attachment unit includes: Automatically generate a short video of the sender's travel memories and attach the short video to the message. The system of claim 1 .

4. The report generation unit Referencing the sender's past travel history to create a more detailed and personalized report The system of claim 1 .

5. The report generation unit To generate the report reflecting the feelings of the sender and to elicit emotional empathy. The system of claim 1 .

6. The line linking unit is Using the generation AI, messages displayed on the LINE chat screen are automatically generated to reflect the sender's emotions. The system of claim 1 .

7. The line linking unit is Add a function to display the sender's travel route on a map on the Line chat screen and display the memories for each place visited. The system of claim 1 .

8. The line linking unit is Automatically add emotional emojis and stamps to messages displayed on the LINE chat screen The system of claim 1 .

Images