System

Abstract

An object of a system according to an embodiment is to simply share a situation in real time.SOLUTION: A system according to an embodiment includes a post collection unit, an information aggregation unit, an explanation generation unit, a sharing unit, and a question generation unit. The post collection unit collects posts of users. The information aggregation unit aggregates the information collected by the post collection unit. The explanation generation unit generates a brief explanation based on the information aggregated by the information aggregation unit. The sharing unit shares the explanation generated by the explanation generation unit with other users. The question generation unit generates a question for requesting additional information when the explanation generation unit determines that the information is insufficient.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] With conventional technology, when sharing information in real time, the information becomes scattered, making it difficult to summarize it concisely.

[0005] The system according to the embodiment aims to share the situation succinctly in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a post collection unit, an information aggregation unit, an explanation generation unit, a sharing unit, and a question generation unit. The post collection unit collects user posts. The information aggregation unit aggregates the information collected by the post collection unit. The explanation generation unit generates a concise explanation based on the information aggregated by the information aggregation unit. The sharing unit shares the explanation generated by the explanation generation unit with other users. The question generation unit generates a question requesting additional information when the explanation generation unit determines that there is insufficient information. [Effects of the Invention]

[0007] The system according to the embodiment allows for easy sharing of status in real time. [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 information sharing system according to an embodiment of the present invention uses a generation AI to aggregate text and image information posted by users, generate concise explanations, and share them with other users. This allows the information sharing system to provide information in a concise format to users who want to obtain information tailored to their own situation in real time.

[0029] An information sharing system according to an embodiment includes a post collection unit, an information aggregation unit, an explanation generation unit, a question generation unit, and a sharing unit. The post collection unit collects user posts. For example, users post text and images to share their current situation. These posts occur in a variety of situations, from events such as music festivals and fireworks displays to disasters such as earthquakes and fires. For example, a user may post text such as "I'm at the fireworks display now. It's really crowded" or a photo of fireworks. This information is collected by a generation AI. The information aggregation unit aggregates the information collected by the post collection unit. For example, if multiple users post about the same event, the generation AI aggregates the information and generates a concise explanation such as "The fireworks display is currently underway, and many people have gathered." The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an explanation based on the prompt. The explanation generation unit generates a concise explanation based on the information aggregated by the information aggregation unit. For example, the generation AI analyzes the text and image information of the collected user posts to generate a concise explanation. The question generation unit generates a question requesting additional information when the explanation generation unit determines that there is insufficient information. For example, if the generation AI does not have enough information to generate an explanation, it poses a question to the poster requesting additional information. For example, the generation AI automatically generates a question such as, "Please tell me more about the crowd situation at the fireworks festival." and sends it to the poster. The sharing unit shares the explanation generated by the explanation generation unit with other users. For example, a concise explanation generated by the generation AI is shared with other users in real time. For example, a user can understand the local situation by receiving information such as, "A fireworks festival is currently being held, and many people have gathered." In this way, the information sharing system according to the embodiment collects user posts, aggregates the information, generates concise explanations, and shares them with other users, thereby understanding the situation in real time.

[0030] The post collection unit can automatically acquire location information of posters and classify the posted content based on the location information. For example, the post collection unit automatically acquires location information of users at the time of posting and classifies the posted content based on that information. For example, posts related to specific event venues or disaster-stricken areas are preferentially collected. In this way, by classifying the posted content based on location information, information for each region can be efficiently collected.

[0031] The post collection unit has a function for posting using voice input, and can analyze the voice data using a generation AI to convert it into text. The post collection unit, for example, adds a voice input function, allowing users to post by voice. For example, voice can be recorded using a smartphone microphone, and the voice data can be analyzed and converted into text by a generation AI. This allows users to easily post using voice input.

[0032] The post collection unit can implement an automatic tagging function for post content, and automatically assign relevant tags using generation AI. The post collection unit, for example, builds a system that analyzes post content and automatically generates relevant tags. For example, tags such as event names, locations, and emotions can be automatically assigned using generation AI. This makes it easier to categorize post content using the automatic tagging function.

[0033] The information aggregator can analyze the time-series data of the posted content and generate an explanation that reflects changes in the situation over time. The information aggregator, for example, builds a system that analyzes the time-series data of the posted content and generates an explanation that reflects changes in the situation over time. For example, the progress of an event or the occurrence of a disaster is explained in chronological order. In this way, by analyzing the time-series data, an explanation that reflects changes in the situation can be generated.

[0034] The information aggregator can develop an algorithm that evaluates the reliability of posted content and prioritizes the aggregation of highly reliable information. The information aggregator, for example, develops an algorithm that evaluates the reliability of posted content and builds a system that prioritizes the aggregation of highly reliable information. For example, the reliability is evaluated based on the poster's reliability score and past posting history. This makes it possible to provide accurate information by prioritizing the aggregation of highly reliable information.

[0035] The information aggregation unit can automatically translate content posted in different languages ​​and aggregate information from an international perspective. The information aggregation unit, for example, builds a system that automatically translates content posted in different languages ​​and aggregates information from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. This allows information to be aggregated from an international perspective by automatically translating content posted in different languages.

[0036] The information aggregator can visualize the posted content and generate a concise explanation as an infographic. The information aggregator, for example, builds a system that visualizes the posted content and generates a concise explanation as an infographic. For example, important information is shown using diagrams and icons. In this way, by visualizing the posted content, it is possible to generate an explanation that is visually easy to understand.

[0037] The question generation unit can automatically generate question content using a generation AI and select the most appropriate question based on the poster's past posts. The question generation unit, for example, builds a system that automatically generates question content using a generation AI and selects the most appropriate question based on the poster's past posts. For example, it analyzes the past posts and generates related questions. This makes it possible to compensate for lack of information by generating the most appropriate question based on the past posts.

[0038] The question generation unit can optimize the timing of questions and send questions at the timing when it is easiest for the poster to answer. The question generation unit, for example, builds a system that optimizes the timing of questions and sends questions at the timing when it is easiest for the poster to answer. For example, the question generation unit analyzes the poster's activity pattern to identify the optimal timing. By sending questions at the optimal timing, the poster's response rate is improved.

[0039] The question generation unit can diversify the question format, allowing questions to be asked not only in text but also in images and audio. The question generation unit, for example, builds a system that diversifies the question format, allowing questions to be asked not only in text but also in images and audio. For example, the system sends questions using images or audio messages. By diversifying the question format, this makes it easier for posters to answer.

[0040] The question generation unit can customize the content of the question and individually optimize it based on the user's profile. The question generation unit, for example, builds a system that customizes the content of the question and individually optimizes it based on the user's profile. For example, the question generation unit generates questions according to the user's interests and concerns. This allows the question to be more appropriate by optimizing the content of the question based on the user's profile.

[0041] The sharing unit can dynamically change the priority of information shared in real time based on the user's level of interest. The sharing unit, for example, builds a system that dynamically changes the priority of information shared in real time based on the user's level of interest. For example, the level of interest is evaluated based on the user's past browsing history and search history. In this way, by dynamically changing the priority of information based on the user's level of interest, information that is important to the user can be provided preferentially.

[0042] The sharing unit can develop an algorithm that evaluates the reliability of shared information and preferentially displays highly reliable information. For example, the sharing unit develops an algorithm that evaluates the reliability of shared information and builds a system that preferentially displays highly reliable information. For example, the reliability is evaluated based on the poster's reliability score and past posting history. This allows highly reliable information to be preferentially displayed, thereby providing accurate information to users.

[0043] The sharing unit can visualize the shared information and provide it in infographic or video format. The sharing unit, for example, builds a system that visualizes the shared information and provides it in infographic or video format. For example, important information can be displayed using diagrams or icons. By visualizing the shared information, it becomes possible to provide information that is visually easy to understand.

[0044] The sharing unit can seamlessly share information between different devices and provide consistent information across smartphones, tablets, PCs, etc. The sharing unit, for example, builds a system that seamlessly shares information between different devices. For example, it provides consistent information across smartphones, tablets, PCs, etc. This allows seamless information sharing between different devices, allowing users to receive consistent information on any device.

[0045] The explanation generation unit can analyze the content posted during a disaster and generate an explanation for understanding the extent of damage and evacuation status in detail. The explanation generation unit, for example, builds a system that analyzes the content posted during a disaster and generates an explanation for understanding the extent of damage and evacuation status in detail. For example, the explanation generation unit evaluates the extent of damage based on the content posted. In this way, by analyzing the content posted during a disaster, the extent of damage and evacuation status can be understood in detail.

[0046] The explanation generation unit can develop an algorithm that updates disaster information in real time and always reflects the latest situation. The explanation generation unit can develop an algorithm that updates disaster information in real time and always reflects the latest situation. For example, the explanation generation unit can dynamically change the information in response to updates to posted content. This allows disaster information to be updated in real time and always reflect the latest situation.

[0047] The explanation generation unit can visualize disaster information on a map, allowing users to intuitively understand the damage situation and the locations of evacuation shelters. The explanation generation unit, for example, builds a system that visualizes disaster information on a map, allowing users to intuitively understand the damage situation and the locations of evacuation shelters. For example, the damage situation is displayed in different colors. In this way, by visualizing disaster information on a map, users can intuitively understand the damage situation and the locations of evacuation shelters.

[0048] The explanation generation unit can provide disaster information in different languages, making it easier to receive international support. The explanation generation unit, for example, builds a system that provides disaster information in different languages, making it easier to receive international support. For example, the explanation generation unit translates information into multiple languages, such as English, French, and Chinese. By providing disaster information in different languages, it makes it easier to receive international support.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The information sharing system can also be equipped with a health monitoring unit that monitors the user's health status and provides information based on the health data. For example, it can collect the user's heart rate, number of steps, and sleep data and provide information according to the user's health status. This can support health management by providing appropriate information based on the user's health status. It is also possible to add an alert function based on the health data and notify the user if an abnormality is detected. Furthermore, by sharing health data with other users, health awareness can be raised throughout the community.

[0051] The post collection unit can provide information about nearby tourist spots and restaurants based on the user's location information. For example, if the user is in a specific area, it collects reviews of tourist spots and restaurants in that area and provides them to the user. This allows the user to obtain local information in real time and enjoy their trip or outing even more. It is also possible to recommend spots that suit the user's preferences based on the location information. Furthermore, it is possible to provide event information based on the user's location information and suggest local activities.

[0052] The post collection unit has a function for posting using voice input, and can analyze the voice data with a generation AI and convert it into text. For example, a user can record voice using a smartphone microphone, and the voice data can be analyzed and converted into text with a generation AI. This allows users to easily post using voice input. Voice input also makes it easy for visually impaired or hand-challenged users to post. Furthermore, it is possible to analyze voice data to estimate emotions and provide feedback based on those emotions.

[0053] The information aggregation unit can develop an algorithm that evaluates the reliability of posted content and prioritizes the aggregation of highly reliable information. For example, it evaluates reliability based on the poster's reliability score and past posting history. This allows for the aggregation of highly reliable information on a priority basis, making it possible to provide accurate information. It can also filter out unreliable information to prevent misinformation from reaching users. Furthermore, by continuously improving the reliability evaluation algorithm and reflecting the latest information, it is possible to maintain a high level of reliability at all times.

[0054] The information aggregation unit can automatically translate content posted in different languages ​​and aggregate information from an international perspective. For example, it can translate into multiple languages, such as English, French, and Chinese. This allows for automatic translation of content posted in different languages, enabling information aggregation from an international perspective. It is also possible to continuously improve the translation algorithm using generative AI to improve translation accuracy. Furthermore, it can integrate content posted in different languages ​​and generate explanations that reflect international trends and opinions.

[0055] The information aggregation unit can visualize the posted content and generate a concise explanation as an infographic. For example, important information can be displayed using diagrams and icons. By visualizing the posted content, it is possible to generate an explanation that is easy to understand visually. Furthermore, by sharing visualized information, it is possible to promote information sharing between users. Furthermore, it is possible to provide interactive content based on visualized information, allowing users to gain a deeper understanding of the information.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The post collection unit collects user posts. For example, users post text and images to share their current situation. These posts are made in a variety of situations, from events such as music festivals and fireworks displays to disasters such as earthquakes and fires. For example, a user might post text such as "I'm at the fireworks display now. It's really crowded," or a photo of the fireworks. This information is collected by the generation AI. Step 2: The information aggregation unit aggregates the information collected by the post collection unit. For example, if multiple users post about the same event, the generation AI aggregates that information and generates a concise explanation such as, "A fireworks display is currently being held, and many people have gathered." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an explanation based on that prompt. Step 3: The explanation generator generates a concise explanation based on the information collected by the information collector. For example, the AI ​​analyzes the collected text and image information from user posts to generate a concise explanation. Step 4: The sharing unit shares the explanation generated by the explanation generation unit with other users. For example, the concise explanation generated by the generation AI is shared with other users in real time. For example, a user can receive information such as "A fireworks display is currently being held and many people have gathered," and understand the local situation. Step 5: If the explanation generation unit determines that there is insufficient information, the question generation unit generates a question requesting additional information. For example, if the generation AI does not have enough information to generate an explanation, it will ask the poster a question requesting additional information. For example, the generation AI automatically generates a question such as, "Please tell me more about the crowd situation at the fireworks display," and sends it to the poster.

[0058] (Example 2) The information sharing system according to an embodiment of the present invention uses a generation AI to aggregate text and image information posted by users, generate concise explanations, and share them with other users. This allows the information sharing system to provide information in a concise format to users who want to obtain information tailored to their own situation in real time.

[0059] An information sharing system according to an embodiment includes a post collection unit, an information aggregation unit, an explanation generation unit, a question generation unit, and a sharing unit. The post collection unit collects user posts. For example, users post text and images to share their current situation. These posts occur in a variety of situations, from events such as music festivals and fireworks displays to disasters such as earthquakes and fires. For example, a user may post text such as "I'm at the fireworks display now. It's really crowded" or a photo of fireworks. This information is collected by a generation AI. The information aggregation unit aggregates the information collected by the post collection unit. For example, if multiple users post about the same event, the generation AI aggregates the information and generates a concise explanation such as "The fireworks display is currently underway, and many people have gathered." The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an explanation based on the prompt. The explanation generation unit generates a concise explanation based on the information aggregated by the information aggregation unit. For example, the generation AI analyzes the text and image information of the collected user posts to generate a concise explanation. The question generation unit generates a question requesting additional information when the explanation generation unit determines that there is insufficient information. For example, if the generation AI does not have enough information to generate an explanation, it poses a question to the poster requesting additional information. For example, the generation AI automatically generates a question such as, "Please tell me more about the crowd situation at the fireworks festival." and sends it to the poster. The sharing unit shares the explanation generated by the explanation generation unit with other users. For example, a concise explanation generated by the generation AI is shared with other users in real time. For example, a user can understand the local situation by receiving information such as, "A fireworks festival is currently being held, and many people have gathered." In this way, the information sharing system according to the embodiment collects user posts, aggregates the information, generates concise explanations, and shares them with other users, thereby understanding the situation in real time.

[0060] The post collection unit uses generative AI to perform sentiment analysis on the content of posts and can determine the priority of posts based on the intensity and type of emotion. For example, the post collection unit performs sentiment analysis on each post and quantifies the intensity and type of emotion based on the sentiment score. For example, emotions such as joy, sadness, and surprise are expressed numerically, and posts with high specific sentiment scores are preferentially collected. This allows important posts to be preferentially collected by performing sentiment analysis.

[0061] The post collection unit can automatically acquire location information of posters and classify the posted content based on the location information. For example, the post collection unit automatically acquires location information of users at the time of posting and classifies the posted content based on that information. For example, posts related to specific event venues or disaster-stricken areas are preferentially collected. In this way, by classifying the posted content based on location information, information for each region can be efficiently collected.

[0062] The post collection unit can use the emotion estimation function to analyze the emotions of posters in real time and provide feedback according to the emotions. The post collection unit, for example, builds a system that analyzes the user's emotions in real time when posting and provides feedback based on the results. For example, an encouraging message is displayed for a post containing a positive emotion. In this way, feedback according to the emotion is provided, thereby improving user satisfaction.

[0063] The post collection unit has a function for posting using voice input, and can analyze the voice data using a generation AI to convert it into text. The post collection unit, for example, adds a voice input function, allowing users to post by voice. For example, voice can be recorded using a smartphone microphone, and the voice data can be analyzed and converted into text by a generation AI. This allows users to easily post using voice input.

[0064] The post collection unit can implement an automatic tagging function for post content, and automatically assign relevant tags using generation AI. The post collection unit, for example, builds a system that analyzes post content and automatically generates relevant tags. For example, tags such as event names, locations, and emotions can be automatically assigned using generation AI. This makes it easier to categorize post content using the automatic tagging function.

[0065] The post collection unit can use the emotion estimation function to provide an interface that encourages posters to post in a way that inspires positive emotions. The post collection unit, for example, uses the emotion estimation function to build a system that provides an interface that encourages posters to post in a way that inspires positive emotions. For example, the post collection unit displays encouraging messages or success stories that elicit positive emotions. This encourages posts that inspire positive emotions, thereby improving user satisfaction.

[0066] The information aggregator can analyze the time-series data of the posted content and generate an explanation that reflects changes in the situation over time. The information aggregator, for example, builds a system that analyzes the time-series data of the posted content and generates an explanation that reflects changes in the situation over time. For example, the progress of an event or the occurrence of a disaster is explained in chronological order. In this way, by analyzing the time-series data, an explanation that reflects changes in the situation can be generated.

[0067] The information aggregator can develop an algorithm that evaluates the reliability of posted content and prioritizes the aggregation of highly reliable information. The information aggregator, for example, develops an algorithm that evaluates the reliability of posted content and builds a system that prioritizes the aggregation of highly reliable information. For example, the reliability is evaluated based on the poster's reliability score and past posting history. This makes it possible to provide accurate information by prioritizing the aggregation of highly reliable information.

[0068] The information aggregator can use the emotion estimation function to preferentially aggregate posts with positive emotions and generate positive explanations. The information aggregator, for example, uses the emotion estimation function to build a system that preferentially aggregates posts with positive emotions. For example, posts with emotions of joy or surprise are preferentially aggregated. In this way, posts with positive emotions are preferentially aggregated, thereby improving user satisfaction.

[0069] The information aggregation unit can automatically translate content posted in different languages ​​and aggregate information from an international perspective. The information aggregation unit, for example, builds a system that automatically translates content posted in different languages ​​and aggregates information from an international perspective. For example, it translates into multiple languages ​​such as English, French, and Chinese. This allows information to be aggregated from an international perspective by automatically translating content posted in different languages.

[0070] The information aggregator can visualize the posted content and generate a concise explanation as an infographic. The information aggregator, for example, builds a system that visualizes the posted content and generates a concise explanation as an infographic. For example, important information is shown using diagrams and icons. In this way, by visualizing the posted content, it is possible to generate an explanation that is visually easy to understand.

[0071] The information aggregating unit can use the emotion estimation function to prioritize aggregating information that the user is most interested in and generate an explanation based on the interest. The information aggregating unit, for example, uses the emotion estimation function to build a system that prioritizes aggregating information that the user is most interested in. For example, it prioritizes aggregating information with a high emotion score. This prioritizes aggregating information based on the user's interest, thereby improving user satisfaction.

[0072] The question generation unit can automatically generate question content using a generation AI and select the most appropriate question based on the poster's past posts. The question generation unit, for example, builds a system that automatically generates question content using a generation AI and selects the most appropriate question based on the poster's past posts. For example, it analyzes the past posts and generates related questions. This makes it possible to compensate for lack of information by generating the most appropriate question based on the past posts.

[0073] The question generation unit can optimize the timing of questions and send questions at the timing when it is easiest for the poster to answer. The question generation unit, for example, builds a system that optimizes the timing of questions and sends questions at the timing when it is easiest for the poster to answer. For example, the question generation unit analyzes the poster's activity pattern to identify the optimal timing. By sending questions at the optimal timing, the poster's response rate is improved.

[0074] The question generation unit uses the emotion estimation function to generate questions that take into consideration the poster's emotions and can ask questions that elicit positive emotions. The question generation unit, for example, uses the emotion estimation function to build a system that generates questions that take into consideration the poster's emotions. For example, gentle questions are asked to posters who have negative emotions. In this way, questions that take into consideration the poster's emotions elicit positive emotions.

[0075] The question generation unit can diversify the question format, allowing questions to be asked not only in text but also in images and audio. The question generation unit, for example, builds a system that diversifies the question format, allowing questions to be asked not only in text but also in images and audio. For example, the system sends questions using images or audio messages. By diversifying the question format, this makes it easier for posters to answer.

[0076] The question generation unit can customize the content of the question and individually optimize it based on the user's profile. The question generation unit, for example, builds a system that customizes the content of the question and individually optimizes it based on the user's profile. For example, the question generation unit generates questions according to the user's interests and concerns. This allows the question to be more appropriate by optimizing the content of the question based on the user's profile.

[0077] The question generation unit uses the emotion estimation function to monitor the user's emotional response to questions in real time and continuously generate optimal questions. The question generation unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to questions in real time. For example, the question generation unit calculates an emotion score by analyzing the user's facial expression and voice. This allows the system to continuously generate optimal questions by monitoring the user's emotional response.

[0078] The sharing unit can dynamically change the priority of information shared in real time based on the user's level of interest. The sharing unit, for example, builds a system that dynamically changes the priority of information shared in real time based on the user's level of interest. For example, the level of interest is evaluated based on the user's past browsing history and search history. In this way, by dynamically changing the priority of information based on the user's level of interest, information that is important to the user can be provided preferentially.

[0079] The sharing unit can develop an algorithm that evaluates the reliability of shared information and preferentially displays highly reliable information. For example, the sharing unit develops an algorithm that evaluates the reliability of shared information and builds a system that preferentially displays highly reliable information. For example, the reliability is evaluated based on the poster's reliability score and past posting history. This allows highly reliable information to be preferentially displayed, thereby providing accurate information to users.

[0080] The sharing unit can use the emotion estimation function to preferentially share information that carries positive emotions, thereby improving user satisfaction. The sharing unit, for example, uses the emotion estimation function to build a system that preferentially shares information that carries positive emotions. For example, information that carries emotions such as joy and surprise is preferentially displayed. In this way, user satisfaction is improved by preferentially sharing information that carries positive emotions.

[0081] The sharing unit can visualize the shared information and provide it in infographic or video format. The sharing unit, for example, builds a system that visualizes the shared information and provides it in infographic or video format. For example, important information can be displayed using diagrams or icons. By visualizing the shared information, it becomes possible to provide information that is visually easy to understand.

[0082] The sharing unit can seamlessly share information between different devices and provide consistent information across smartphones, tablets, PCs, etc. The sharing unit, for example, builds a system that seamlessly shares information between different devices. For example, it provides consistent information across smartphones, tablets, PCs, etc. This allows seamless information sharing between different devices, allowing users to receive consistent information on any device.

[0083] The sharing unit uses the emotion estimation function to preferentially share information that the user is most interested in, and can provide information based on the user's interests. For example, the sharing unit uses the emotion estimation function to build a system that preferentially shares information that the user is most interested in. For example, information with a high emotion score is preferentially displayed. This allows information based on the user's interests to be preferentially shared, thereby improving user satisfaction.

[0084] The explanation generation unit can analyze the content posted during a disaster and generate an explanation for understanding the extent of damage and evacuation status in detail. The explanation generation unit, for example, builds a system that analyzes the content posted during a disaster and generates an explanation for understanding the extent of damage and evacuation status in detail. For example, the explanation generation unit evaluates the extent of damage based on the content posted. In this way, by analyzing the content posted during a disaster, the extent of damage and evacuation status can be understood in detail.

[0085] The explanation generation unit can develop an algorithm that updates disaster information in real time and always reflects the latest situation. The explanation generation unit can develop an algorithm that updates disaster information in real time and always reflects the latest situation. For example, the explanation generation unit can dynamically change the information in response to updates to posted content. This allows disaster information to be updated in real time and always reflect the latest situation.

[0086] The explanation generation unit can use the emotion estimation function to analyze the emotions of posters during a disaster and prioritize sharing of information that gives a sense of security. The explanation generation unit, for example, uses the emotion estimation function to build a system that analyzes the emotions of posters during a disaster and prioritizes sharing of information that gives a sense of security. For example, information with positive emotions is displayed preferentially. In this way, by analyzing the emotions of posters during a disaster, information that gives a sense of security can be prioritized and shared.

[0087] The explanation generation unit can visualize disaster information on a map, allowing users to intuitively understand the damage situation and the locations of evacuation shelters. The explanation generation unit, for example, builds a system that visualizes disaster information on a map, allowing users to intuitively understand the damage situation and the locations of evacuation shelters. For example, the damage situation is displayed in different colors. In this way, by visualizing disaster information on a map, users can intuitively understand the damage situation and the locations of evacuation shelters.

[0088] The explanation generation unit can provide disaster information in different languages, making it easier to receive international support. The explanation generation unit, for example, builds a system that provides disaster information in different languages, making it easier to receive international support. For example, the explanation generation unit translates information into multiple languages, such as English, French, and Chinese. By providing disaster information in different languages, it makes it easier to receive international support.

[0089] The explanation generation unit uses the emotion estimation function to monitor the user's emotional response to disaster information in real time, and can continuously provide optimal information. For example, the explanation generation unit uses the emotion estimation function to build a system that monitors the user's emotional response to disaster information in real time. For example, the explanation generation unit calculates an emotion score by analyzing the user's facial expression and voice. In this way, by monitoring the user's emotional response to disaster information, optimal information can be continuously provided.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The information sharing system can also be equipped with a health monitoring unit that monitors the user's health status and provides information based on the health data. For example, it can collect the user's heart rate, number of steps, and sleep data and provide information according to the user's health status. This can support health management by providing appropriate information based on the user's health status. It is also possible to add an alert function based on the health data and notify the user if an abnormality is detected. Furthermore, by sharing health data with other users, health awareness can be raised throughout the community.

[0092] The post collection unit can use the emotion estimation function to recommend appropriate music based on the user's emotions. For example, if the user is feeling stressed, it can recommend relaxing music, and if the user is feeling happy, it can recommend upbeat music. This can support the user's emotional regulation by providing music that matches the user's emotions. The emotion estimation function can also be used to automatically generate playlists based on the user's emotions. Furthermore, it can analyze the user's emotional data and optimize music recommendations based on long-term changes in emotions.

[0093] The post collection unit can provide information about nearby tourist spots and restaurants based on the user's location information. For example, if the user is in a specific area, it collects reviews of tourist spots and restaurants in that area and provides them to the user. This allows the user to obtain local information in real time and enjoy their trip or outing even more. It is also possible to recommend spots that suit the user's preferences based on the location information. Furthermore, it is possible to provide event information based on the user's location information and suggest local activities.

[0094] The post collection unit can use the emotion estimation function to provide mental health support based on the user's emotions. For example, if the user is feeling negative, it can introduce counseling services or relaxation techniques. This can support the user's mental health. The emotion estimation function can also be used to monitor changes in the user's emotions over the long term and suggest professional support as needed. Furthermore, it can provide self-care advice based on the emotion data to help the user manage their own emotions.

[0095] The post collection unit has a function for posting using voice input, and can analyze the voice data with a generation AI and convert it into text. For example, a user can record voice using a smartphone microphone, and the voice data can be analyzed and converted into text with a generation AI. This allows users to easily post using voice input. Voice input also makes it easy for visually impaired or hand-challenged users to post. Furthermore, it is possible to analyze voice data to estimate emotions and provide feedback based on those emotions.

[0096] The information aggregation unit can use the emotion estimation function to recommend appropriate news articles based on the user's emotions. For example, if the user is feeling anxious, it can recommend news articles that provide a sense of security. This can support the user's emotional regulation by providing news articles that correspond to the user's emotions. The emotion estimation function can also be used to automatically generate a news feed based on the user's emotions. Furthermore, it can analyze the user's emotional data and optimize news recommendations based on long-term changes in emotions.

[0097] The information aggregation unit can develop an algorithm that evaluates the reliability of posted content and prioritizes the aggregation of highly reliable information. For example, it evaluates reliability based on the poster's reliability score and past posting history. This allows for the aggregation of highly reliable information on a priority basis, making it possible to provide accurate information. It can also filter out unreliable information to prevent misinformation from reaching users. Furthermore, by continuously improving the reliability evaluation algorithm and reflecting the latest information, it is possible to maintain a high level of reliability at all times.

[0098] The information aggregation unit can use the emotion estimation function to prioritize aggregation of information that the user is most interested in and generate explanations based on the user's interests. For example, it prioritizes aggregation of information with a high emotion score. This prioritizes aggregation of information based on the user's interests, thereby improving user satisfaction. The emotion estimation function can also be used to monitor changes in the user's interests in real time and continuously provide information based on the user's interests. Furthermore, it can analyze the user's interest data and optimize the aggregation of information based on long-term changes in interests.

[0099] The information aggregation unit can automatically translate content posted in different languages ​​and aggregate information from an international perspective. For example, it can translate into multiple languages, such as English, French, and Chinese. This allows for automatic translation of content posted in different languages, enabling information aggregation from an international perspective. It is also possible to continuously improve the translation algorithm using generative AI to improve translation accuracy. Furthermore, it can integrate content posted in different languages ​​and generate explanations that reflect international trends and opinions.

[0100] The information aggregation unit can visualize the posted content and generate a concise explanation as an infographic. For example, important information can be displayed using diagrams and icons. By visualizing the posted content, it is possible to generate an explanation that is easy to understand visually. Furthermore, by sharing visualized information, it is possible to promote information sharing between users. Furthermore, it is possible to provide interactive content based on visualized information, allowing users to gain a deeper understanding of the information.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The post collection unit collects user posts. For example, users post text and images to share their current situation. These posts are made in a variety of situations, from events such as music festivals and fireworks displays to disasters such as earthquakes and fires. For example, a user might post text such as "I'm at the fireworks display now. It's really crowded," or a photo of the fireworks. This information is collected by the generation AI. Step 2: The information aggregation unit aggregates the information collected by the post collection unit. For example, if multiple users post about the same event, the generation AI aggregates that information and generates a concise explanation such as, "A fireworks display is currently being held, and many people have gathered." The input to the generation AI is a prompt containing instructions on what the user wants the generation AI to do, and the generation AI generates an explanation based on that prompt. Step 3: The explanation generator generates a concise explanation based on the information collected by the information collector. For example, the AI ​​analyzes the collected text and image information from user posts to generate a concise explanation. Step 4: The sharing unit shares the explanation generated by the explanation generation unit with other users. For example, the concise explanation generated by the generation AI is shared with other users in real time. For example, a user can receive information such as "A fireworks display is currently being held and many people have gathered," and understand the local situation. Step 5: If the explanation generation unit determines that there is insufficient information, the question generation unit generates a question requesting additional information. For example, if the generation AI does not have enough information to generate an explanation, it will ask the poster a question requesting additional information. For example, the generation AI automatically generates a question such as, "Please tell me more about the crowd situation at the fireworks display," and sends it to the poster.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 post collection unit that collects user posts; an information aggregation unit that aggregates the information collected by the post collection unit; an explanation generation unit that generates a concise explanation based on the information aggregated by the information aggregation unit; a sharing unit that shares the explanation generated by the explanation generating unit with other users; a question generation unit that generates a question requesting additional information when the explanation generation unit determines that the information is insufficient; A system characterized by:

2. The post collection unit It has a function to post using voice input, and the voice data is analyzed and converted into text by the generation AI.

2. The system of claim 1.

3. The information aggregation unit Analyzes the time series data of posted content and generates the explanation that reflects changes in the situation over time.

2. The system of claim 1.

4. The question generation unit The content of the question is automatically generated by the generation AI, and the most appropriate question is selected based on the poster's past posts.

2. The system of claim 1.

5. The common part is Dynamically changing the priority of the information shared in real time based on the user's interest level.

2. The system of claim 1.

6. The post collection unit Analyze the poster's emotions in real time and provide feedback according to those emotions 2. The system of claim 1.

7. The information aggregation unit The posts with positive sentiment are preferentially aggregated to generate the positive description.

2. The system of claim 1.

8. The explanation generation unit Analyze the poster's emotions during a disaster and prioritize sharing information that gives a sense of security 2. The system of claim 1.

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