System

Abstract

An object of a system according to an embodiment is to efficiently generate and sell information on a place where a user is currently present.SOLUTION: A system includes a photograph acquisition unit, a position information acquisition unit, an analysis unit, and a sales unit. The photograph acquisition unit acquires a photograph of a place where the user is currently present. The position information acquisition unit acquires position information of the photograph acquired by the photograph acquisition unit. The analysis unit analyzes the information acquired by the photograph acquisition unit and the position information acquisition unit and generates valuable information. The sales unit sells the information generated by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, when a user provides information about their current location to others, unless appropriate keywords are entered, the information is difficult to find and it is difficult to receive information fees.

[0005] The system according to the embodiment aims to efficiently generate and sell information about a user's current location. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, a location information acquisition unit, an analysis unit, and a sales unit. The photo acquisition unit acquires a photo of the user's current location. The location information acquisition unit acquires location information of the photo acquired by the photo acquisition unit. The analysis unit analyzes the information acquired by the photo acquisition unit and the location information acquisition unit to generate valuable information. The sales unit sells the information generated by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate and sell information about a user's current location. [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 generation system according to an embodiment of the present invention is a system in which a user simply takes a photo of their current location and uploads the photo and location information to the system, and a generation AI generates valuable information from the photo and location information, and sells the information. This allows the information generation system to generate valuable information using the photo and location information of the user's current location and sell the information.

[0029] An information generation system according to an embodiment includes a photo acquisition unit, a location information acquisition unit, an analysis unit, and a sales unit. The photo acquisition unit acquires a photo of a user's current location. For example, the photo is taken using a smartphone camera and uploaded to the system. The photo acquisition unit can also import photos taken with a digital camera. The photo acquisition unit can also acquire photos from cloud storage. For example, photos taken with a smartphone camera are uploaded to the system through an application. Photos taken with a digital camera are imported into the system using a USB cable. Photos stored in cloud storage are acquired by the system through an API. The location information acquisition unit acquires location information of the photo acquired by the photo acquisition unit. For example, the location information acquisition unit acquires location information using GPS. The location information acquisition unit can also acquire location information using Wi-Fi location information. The location information acquisition unit can also acquire location information using cell tower information. For example, location information acquired using GPS is provided to the system as latitude and longitude. The Wi-Fi location information identifies the location based on information about surrounding Wi-Fi access points. The cell tower information identifies the location based on the signal strength of surrounding cell towers. The analysis unit analyzes the information acquired by the photo acquisition unit and the location information acquisition unit to generate valuable information. For example, the generation AI analyzes photos and location information to generate road closure information. The generation AI can also generate traffic congestion information. Furthermore, the generation AI can generate accident information. For example, the generation AI recognizes road signs in photos and generates road closure information. Traffic congestion information is generated by analyzing the number and locations of cars in photos. Accident information is generated by analyzing the damage to vehicles in photos. The sales unit sells the information generated by the analysis unit. For example, the generated information is sold through an online marketplace. The generated information can also be provided through a subscription service. Furthermore, the generated information can be provided to other systems via APIs.For example, an online marketplace allows users to purchase generated information. A subscription service provides updated information on a regular basis. Information provided through an API is used in other systems. As a result, the information generation system according to the embodiment can generate valuable information using a photo and location information of a user's current location and sell the information.

[0030] The location information acquisition unit can automatically extract metadata from photos and combine it with location information to improve analysis accuracy. The location information acquisition unit, for example, builds a system that automatically extracts metadata from photos and combines it with location information for analysis. For example, it analyzes road conditions more accurately based on the time of shooting and weather information. The location information acquisition unit also analyzes photo metadata and combines it with location information to improve the analysis accuracy of the generation AI. For example, it distinguishes between photos taken at night and photos taken during the day and performs appropriate analysis. The location information acquisition unit also improves the accuracy of the generation AI by automatically extracting photo metadata and analyzing it in combination with location information. For example, it takes weather information into account when analyzing road conditions during rainy weather. This makes it possible to use photo metadata to improve analysis accuracy.

[0031] The photo acquisition unit allows the user to provide additional information using voice input, and the generation AI can also use that voice information for analysis. For example, the photo acquisition unit builds a system that allows the user to provide additional information using voice input when uploading photos and location information. For example, the user may say, "This road is closed," using voice input. The photo acquisition unit also uses voice input to allow the user to provide additional information about the photos and location information, and the generation AI uses that voice information for analysis. For example, the user may say, "There is a traffic jam at this location." The photo acquisition unit also develops a system that allows the user to provide additional information using voice input when uploading photos and location information, and the generation AI uses that voice information for analysis. For example, the user may say, "This road is under construction," using voice input. This allows additional information to be provided using voice input and used for analysis.

[0032] The photo acquisition unit will also allow video data to be uploaded, allowing the generation AI to extract information from the video. For example, the photo acquisition unit will build a system that allows video data to be uploaded in addition to photos and location information. For example, a user can take a video of the road conditions and upload it. The photo acquisition unit will also allow video data to be uploaded, allowing the generation AI to extract information from the video. For example, it will analyze the status of road closures from the video. The photo acquisition unit will also allow video data to be uploaded in addition to photos and location information, allowing the generation AI to extract information from the video. For example, it will analyze the status of traffic congestion from the video. This allows video data to be uploaded and used for analysis.

[0033] The photo acquisition unit can add a crowdsourcing function for integrating photos taken by different users at the same location and generating more detailed information. The photo acquisition unit adds a crowdsourcing function for integrating photos taken by different users at the same location and generating more detailed information. For example, the photo acquisition unit performs a detailed analysis of road conditions based on photos taken by multiple users. The photo acquisition unit also uses the crowdsourcing function to integrate photos taken by different users at the same location, allowing the generation AI to generate more detailed information. For example, it analyzes photos taken from multiple perspectives. The photo acquisition unit also develops a crowdsourcing function for integrating photos taken by different users at the same location and allowing the generation AI to generate more detailed information. For example, it analyzes photos taken at the same location in chronological order. This makes it possible to integrate photos taken by different users at the same location and generate more detailed information.

[0034] The analysis unit can detect anomalies by comparing with past data and generate an alert if an abnormality occurs. For example, when the generation AI analyzes photos and location information, the analysis unit builds a system that compares with past data to detect anomalies and generates an alert if an abnormality occurs. For example, an alert is generated if an abnormal traffic condition is detected. The analysis unit also develops a generation AI system that compares with past data to detect anomalies and generates an alert if an abnormality occurs. For example, an alert is generated if an abnormal road condition is detected. The analysis unit also develops a generation AI system that compares with past data when the generation AI analyzes photos and location information and detects anomalies and generates an alert if an abnormality occurs. For example, an alert is generated if an abnormal weather condition is detected. This makes it possible to detect anomalies by comparing with past data and generate an alert.

[0035] The analysis unit can analyze multiple photos in chronological order and generate information that reflects changes over time. For example, the analysis unit constructs a system in which a generation AI analyzes multiple photos in chronological order and generates information that reflects changes over time. For example, it analyzes how road conditions have changed over time. The analysis unit also develops a generation AI system that performs analysis in chronological order and generates information that reflects changes over time. For example, it analyzes how traffic congestion conditions have changed over time. The analysis unit also analyzes multiple photos in chronological order and generates information that reflects changes over time. For example, it analyzes how weather conditions have changed over time. This makes it possible to generate information that reflects changes over time.

[0036] The analysis unit can integrate different data sources to generate more accurate information. For example, when the generation AI analyzes photos and location information, the analysis unit integrates it with different data sources, such as traffic camera and weather data, to build a system that generates more accurate information. For example, traffic camera footage is used for analysis. The analysis unit also integrates different data sources to develop a system in which the generation AI generates more accurate information. For example, weather data is used for analysis to more accurately grasp road conditions. The analysis unit also integrates it with different data sources to generate more accurate information when the generation AI analyzes photos and location information. For example, traffic camera footage is combined with weather data for analysis. This makes it possible to integrate different data sources to generate more accurate information.

[0037] The analysis unit can improve the accuracy of the information by taking into account additional text information provided by the user. For example, when the generation AI analyzes photos and location information, the analysis unit builds a system that improves the accuracy of the information by taking into account additional text information provided by the user. For example, a user adds a comment such as "This road is under construction." The analysis unit also develops a system that uses the additional text information provided by the user in the analysis, allowing the generation AI to generate more accurate information. For example, a user adds a comment such as "There is a traffic jam at this location." The analysis unit also improves the accuracy of the information by taking into account additional text information provided by the user when the generation AI analyzes photos and location information. For example, a user adds a comment such as "This road is closed." In this way, the accuracy of the information can be improved by taking into account the additional text information provided by the user.

[0038] The sales department can analyze a buyer's past purchase history and automatically suggest related information. For example, when selling information, the sales department builds a system that analyzes a buyer's past purchase history and automatically suggests related information. For example, it recommends new information related to information purchased in the past. The sales department also develops a generative AI system that analyzes a buyer's past purchase history and automatically suggests related information. For example, it recommends new information based on the topic of information purchased in the past. The sales department also analyzes a buyer's past purchase history and automatically suggests related information when selling information. For example, it recommends new information based on the content of information purchased in the past. This makes it possible to analyze a buyer's past purchase history and suggest related information.

[0039] The sales department adds a function that allows buyers to rate information, and can improve the quality of the information based on those ratings. For example, the sales department adds a function that allows buyers to rate information when selling information, and builds a system that improves the quality of the information based on those ratings. For example, buyers rate information by stars. The sales department also collects buyer ratings and develops a generative AI system that improves the quality of information based on that data. For example, it introduces an algorithm to improve information with low ratings. The sales department also adds a function that allows buyers to rate information when selling information, and improves the quality of the information based on those ratings. For example, it updates the content of the information based on buyer feedback. This makes it possible to improve the quality of the information based on buyer ratings.

[0040] The sales department can introduce a subscription model to the information sales platform and add a service that provides the latest information on a regular basis. For example, the sales department can introduce a subscription model to the information sales platform and build a system that adds a service that provides the latest information on a regular basis. For example, the sales department can provide the latest traffic information for a monthly fee. The sales department can also introduce a subscription model and develop a generative AI system that provides the latest information on a regular basis. For example, the sales department can provide the latest road conditions on a weekly basis. The sales department can also introduce a subscription model to the information sales platform and add a service that provides the latest information on a regular basis. For example, the sales department can provide the latest weather information for an annual contract. This allows the sales department to introduce a subscription model and provide the latest information on a regular basis.

[0041] The sales department provides information at different price ranges, thereby increasing the number of options available to users according to their needs. For example, the sales department builds a system that provides information at different price ranges to an information sales platform, thereby increasing the number of options available to users according to their needs. For example, basic information is provided free of charge, and detailed information is provided for a fee. The sales department also develops a generative AI system that provides information at different price ranges, thereby increasing the number of options available to users according to their needs. For example, simple information is provided at a low price, and detailed information is provided at a high price. The sales department also provides information at different price ranges to an information sales platform, thereby increasing the number of options available to users according to their needs. For example, real-time information is provided at a high price, and past information is provided at a low price. This makes it possible to provide information at different price ranges, thereby increasing the number of options available to users according to their needs.

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

[0043] The photo acquisition unit can record the surrounding sound environment when a user takes a photo and use the sound information for analysis. For example, the photo acquisition unit can record the sound of a busy road and generate traffic congestion information. The photo acquisition unit can also record the sound environment and use the sound information for analysis, allowing the generation AI to generate more detailed information. For example, the photo acquisition unit can record the sound of a construction site and generate information about roads under construction. The photo acquisition unit can also record the sound environment and use the sound information for analysis, allowing the generation AI to generate more accurate information. For example, the photo acquisition unit can record the sound of rain and generate weather information. This allows the analysis accuracy to be improved by using the sound environment.

[0044] When a user takes a photo, the photo acquisition unit acquires environmental data such as the ambient temperature and humidity, and can use that data for analysis. For example, if the temperature is high, it generates information about the risk of heatstroke. The photo acquisition unit also acquires environmental data and uses that data for analysis, allowing the generation AI to generate more detailed information. For example, if the humidity is high, it generates information about the risk of mold growth. The photo acquisition unit also acquires environmental data and uses that data for analysis, allowing the generation AI to generate more accurate information. For example, if the air pressure is low, it generates information about changes in the weather. This allows the use of environmental data to improve the accuracy of analysis.

[0045] When a user takes a photo, the photo acquisition unit can detect surrounding odors and use the odor information for analysis. For example, it can detect the odor of gasoline and generate information about a gasoline leak. The photo acquisition unit can also detect odor information and use it for analysis, allowing the generation AI to generate more detailed information. For example, it can detect the odor of flowers and generate information about flower blooming. The photo acquisition unit can also detect odor information and use it for analysis, allowing the generation AI to generate more accurate information. For example, it can detect the odor of smoke and generate information about a fire. This allows the use of odor information to improve the accuracy of analysis.

[0046] The photo acquisition unit can record the ambient light environment when a user takes a photo and use that light information for analysis. For example, it can record the intensity of sunlight and generate information on the risk of sunburn. The photo acquisition unit can also record the light environment and use that light information for analysis, allowing the generation AI to generate more detailed information. For example, it can record the brightness of street lights and generate nighttime safety information. The photo acquisition unit can also record the light environment and use that light information for analysis, allowing the generation AI to generate more accurate information. For example, it can record the intensity of light on a cloudy day and generate weather information. This makes it possible to use the light environment to improve the accuracy of analysis.

[0047] The photo acquisition unit can detect surrounding vibrations when a user takes a photo and use the vibration information for analysis. For example, it can detect earthquake vibrations and generate earthquake information. The photo acquisition unit can also detect vibration information and use the vibration information for analysis, allowing the generation AI to generate more detailed information. For example, it can detect vibrations at a construction site and generate information about roads under construction. The photo acquisition unit can also detect vibration information and use the vibration information for analysis, allowing the generation AI to generate more accurate information. For example, it can detect vibrations on busy roads and generate information about traffic congestion. This makes it possible to use vibration information to improve the accuracy of analysis.

[0048] The analysis unit can detect anomalies by comparing with past data and generate an alert if an abnormality occurs. For example, when the generation AI analyzes photos and location information, it compares with past data to detect anomalies and builds a system that generates an alert if an abnormality occurs. For example, it generates an alert if it detects unusual traffic conditions. In addition, the analysis unit develops a generation AI system that detects anomalies by comparing with past data and generates an alert if an abnormality occurs. For example, it generates an alert if it detects unusual road conditions. In addition, when the generation AI analyzes photos and location information, the analysis unit compares with past data to detect anomalies and generate an alert if an abnormality occurs. For example, it generates an alert if it detects unusual weather conditions. In this way, it is possible to detect anomalies by comparing with past data and generate an alert.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The photo acquisition unit acquires a photo of the user's current location. For example, the user can take a photo using a smartphone camera and upload it to the system. It is also possible to import photos taken with a digital camera or acquire photos from cloud storage. Step 2: The location information acquisition unit acquires location information of the photo acquired by the photo acquisition unit. For example, the location information can be acquired using GPS, Wi-Fi location information, or cell tower information. Step 3: The analysis unit analyzes the information acquired by the photo acquisition unit and location information acquisition unit to generate valuable information. For example, using generation AI, it generates information on road closures, traffic congestion, accidents, etc. Step 4: The sales department sells the information generated by the analytics department, for example, through an online marketplace, subscription service, or API.

[0051] (Example 2) The information generation system according to an embodiment of the present invention is a system in which a user simply takes a photo of their current location and uploads the photo and location information to the system, and a generation AI generates valuable information from the photo and location information, and sells the information. This allows the information generation system to generate valuable information using the photo and location information of the user's current location and sell the information.

[0052] An information generation system according to an embodiment includes a photo acquisition unit, a location information acquisition unit, an analysis unit, and a sales unit. The photo acquisition unit acquires a photo of a user's current location. For example, the photo is taken using a smartphone camera and uploaded to the system. The photo acquisition unit can also import photos taken with a digital camera. The photo acquisition unit can also acquire photos from cloud storage. For example, photos taken with a smartphone camera are uploaded to the system through an application. Photos taken with a digital camera are imported into the system using a USB cable. Photos stored in cloud storage are acquired by the system through an API. The location information acquisition unit acquires location information of the photo acquired by the photo acquisition unit. For example, the location information acquisition unit acquires location information using GPS. The location information acquisition unit can also acquire location information using Wi-Fi location information. The location information acquisition unit can also acquire location information using cell tower information. For example, location information acquired using GPS is provided to the system as latitude and longitude. The Wi-Fi location information identifies the location based on information about surrounding Wi-Fi access points. The cell tower information identifies the location based on the signal strength of surrounding cell towers. The analysis unit analyzes the information acquired by the photo acquisition unit and the location information acquisition unit to generate valuable information. For example, the generation AI analyzes photos and location information to generate road closure information. The generation AI can also generate traffic congestion information. Furthermore, the generation AI can generate accident information. For example, the generation AI recognizes road signs in photos and generates road closure information. Traffic congestion information is generated by analyzing the number and locations of cars in photos. Accident information is generated by analyzing the damage to vehicles in photos. The sales unit sells the information generated by the analysis unit. For example, the generated information is sold through an online marketplace. The generated information can also be provided through a subscription service. Furthermore, the generated information can be provided to other systems via APIs.For example, an online marketplace allows users to purchase generated information. A subscription service provides updated information on a regular basis. Information provided through an API is used in other systems. As a result, the information generation system according to the embodiment can generate valuable information using a photo and location information of a user's current location and sell the information.

[0053] The photo acquisition unit can estimate the user's emotions and provide feedback to elicit positive emotions. For example, when uploading photos and location information, the photo acquisition unit uses the generation AI to analyze the user's facial expressions and voice to estimate emotions. For example, if the user is smiling while taking a photo, the generation AI determines that emotion to be positive and displays an encouraging message. When the user uploads photos and location information, the photo acquisition unit also uses the generation AI to analyze emotions in real time and provide feedback to elicit positive emotions. For example, if the user is tired, advice on how to relax is displayed. When uploading photos and location information, the photo acquisition unit also provides an interface for the generation AI to estimate the user's emotions and elicit positive emotions. For example, if the user is feeling negative, positive music or images are displayed. This makes it possible to estimate the user's emotions and elicit positive emotions.

[0054] The location information acquisition unit can automatically extract metadata from photos and combine it with location information to improve analysis accuracy. The location information acquisition unit, for example, builds a system that automatically extracts metadata from photos and combines it with location information for analysis. For example, it analyzes road conditions more accurately based on the time of shooting and weather information. The location information acquisition unit also analyzes photo metadata and combines it with location information to improve the analysis accuracy of the generation AI. For example, it distinguishes between photos taken at night and photos taken during the day and performs appropriate analysis. The location information acquisition unit also improves the accuracy of the generation AI by automatically extracting photo metadata and analyzing it in combination with location information. For example, it takes weather information into account when analyzing road conditions during rainy weather. This makes it possible to use photo metadata to improve analysis accuracy.

[0055] The photo acquisition unit allows the user to provide additional information using voice input, and the generation AI can also use that voice information for analysis. For example, the photo acquisition unit builds a system that allows the user to provide additional information using voice input when uploading photos and location information. For example, the user may say, "This road is closed," using voice input. The photo acquisition unit also uses voice input to allow the user to provide additional information about the photos and location information, and the generation AI uses that voice information for analysis. For example, the user may say, "There is a traffic jam at this location." The photo acquisition unit also develops a system that allows the user to provide additional information using voice input when uploading photos and location information, and the generation AI uses that voice information for analysis. For example, the user may say, "This road is under construction," using voice input. This allows additional information to be provided using voice input and used for analysis.

[0056] The photo acquisition unit will also allow video data to be uploaded, allowing the generation AI to extract information from the video. For example, the photo acquisition unit will build a system that allows video data to be uploaded in addition to photos and location information. For example, a user can take a video of the road conditions and upload it. The photo acquisition unit will also allow video data to be uploaded, allowing the generation AI to extract information from the video. For example, it will analyze the status of road closures from the video. The photo acquisition unit will also allow video data to be uploaded in addition to photos and location information, allowing the generation AI to extract information from the video. For example, it will analyze the status of traffic congestion from the video. This allows video data to be uploaded and used for analysis.

[0057] The photo acquisition unit can add a crowdsourcing function for integrating photos taken by different users at the same location and generating more detailed information. The photo acquisition unit adds a crowdsourcing function for integrating photos taken by different users at the same location and generating more detailed information. For example, the photo acquisition unit performs a detailed analysis of road conditions based on photos taken by multiple users. The photo acquisition unit also uses the crowdsourcing function to integrate photos taken by different users at the same location, allowing the generation AI to generate more detailed information. For example, it analyzes photos taken from multiple perspectives. The photo acquisition unit also develops a crowdsourcing function for integrating photos taken by different users at the same location and allowing the generation AI to generate more detailed information. For example, it analyzes photos taken at the same location in chronological order. This makes it possible to integrate photos taken by different users at the same location and generate more detailed information.

[0058] The photo acquisition unit can use the emotion estimation function to analyze the emotion of the user when uploading in real time and provide guidance for reducing negative emotions. For example, the photo acquisition unit can use the emotion estimation function to analyze the emotion of the user when uploading photos and location information in real time and provide guidance for reducing negative emotions. For example, if the user is feeling stressed, advice to relax is displayed. The photo acquisition unit can also use the emotion estimation function to analyze the emotion of the user when uploading photos and location information in real time and provide guidance for reducing negative emotions. For example, if the user is feeling anxious, a message to reassure the user is displayed. The photo acquisition unit can also use the emotion estimation function to analyze the emotion of the user when uploading photos and location information in real time and provide an interface for reducing negative emotions. For example, if the user is feeling angry, advice to stay calm is displayed. In this way, the user's emotions can be analyzed in real time and negative emotions can be reduced.

[0059] The analysis unit can estimate the user's emotions and generate information based on the emotions. For example, when the generation AI analyzes photos and location information, the analysis unit estimates the user's emotions and generates information based on the emotions. For example, if the user has positive emotions, the analysis unit generates information that reflects those emotions. Furthermore, the analysis unit constructs a system in which the generation AI estimates the user's emotions and generates information based on the emotions when analyzing photos and location information. For example, if the user has negative emotions, the analysis unit generates information to alleviate those emotions. Furthermore, when the generation AI analyzes photos and location information, the analysis unit estimates the user's emotions and generates information based on the emotions. For example, if the user is excited, the analysis unit generates information that reflects those emotions. This makes it possible to generate information based on the user's emotions.

[0060] The analysis unit can detect anomalies by comparing with past data and generate an alert if an abnormality occurs. For example, when the generation AI analyzes photos and location information, the analysis unit builds a system that compares with past data to detect anomalies and generates an alert if an abnormality occurs. For example, an alert is generated if an abnormal traffic condition is detected. The analysis unit also develops a generation AI system that compares with past data to detect anomalies and generates an alert if an abnormality occurs. For example, an alert is generated if an abnormal road condition is detected. The analysis unit also develops a generation AI system that compares with past data when the generation AI analyzes photos and location information and detects anomalies and generates an alert if an abnormality occurs. For example, an alert is generated if an abnormal weather condition is detected. This makes it possible to detect anomalies by comparing with past data and generate an alert.

[0061] The analysis unit can analyze multiple photos in chronological order and generate information that reflects changes over time. For example, the analysis unit constructs a system in which a generation AI analyzes multiple photos in chronological order and generates information that reflects changes over time. For example, it analyzes how road conditions have changed over time. The analysis unit also develops a generation AI system that performs analysis in chronological order and generates information that reflects changes over time. For example, it analyzes how traffic congestion conditions have changed over time. The analysis unit also analyzes multiple photos in chronological order and generates information that reflects changes over time. For example, it analyzes how weather conditions have changed over time. This makes it possible to generate information that reflects changes over time.

[0062] The analysis unit can integrate different data sources to generate more accurate information. For example, when the generation AI analyzes photos and location information, the analysis unit integrates it with different data sources, such as traffic camera and weather data, to build a system that generates more accurate information. For example, traffic camera footage is used for analysis. The analysis unit also integrates different data sources to develop a system in which the generation AI generates more accurate information. For example, weather data is used for analysis to more accurately grasp road conditions. The analysis unit also integrates it with different data sources to generate more accurate information when the generation AI analyzes photos and location information. For example, traffic camera footage is combined with weather data for analysis. This makes it possible to integrate different data sources to generate more accurate information.

[0063] The analysis unit can improve the accuracy of the information by taking into account additional text information provided by the user. For example, when the generation AI analyzes photos and location information, the analysis unit builds a system that improves the accuracy of the information by taking into account additional text information provided by the user. For example, a user adds a comment such as "This road is under construction." The analysis unit also develops a system that uses the additional text information provided by the user in the analysis, allowing the generation AI to generate more accurate information. For example, a user adds a comment such as "There is a traffic jam at this location." The analysis unit also improves the accuracy of the information by taking into account additional text information provided by the user when the generation AI analyzes photos and location information. For example, a user adds a comment such as "This road is closed." In this way, the accuracy of the information can be improved by taking into account the additional text information provided by the user.

[0064] The analysis unit can use the emotion estimation function to collect users' emotional reactions to the generated information and improve the information generation algorithm based on that feedback. The analysis unit, for example, uses the emotion estimation function to collect users' emotional reactions to the generated information and builds a system that improves the information generation algorithm based on that feedback. For example, it prioritizes generating information that the user feels positive about. The analysis unit also collects users' emotional reactions and develops a system that improves the information generation algorithm of the generation AI based on that data. For example, it introduces an algorithm to reduce information that the user feels negative about. The analysis unit also uses the emotion estimation function to collect users' emotional reactions to the generated information and improves the information generation algorithm based on that feedback. For example, it adjusts the algorithm for generating information that the user feels surprise or joy about. In this way, it is possible to collect users' emotional reactions and improve the information generation algorithm.

[0065] The sales department can estimate the buyer's emotions when selling information and provide recommendations based on those emotions. For example, the sales department builds a system in which a generation AI estimates the buyer's emotions when selling information and provides recommendations based on those emotions. For example, if the buyer is excited, information that matches that emotion is recommended. The sales department also develops a generation AI system that analyzes the buyer's emotions in real time and provides recommendations based on those emotions. For example, if the buyer wants to relax, information that matches that emotion is recommended. The sales department also develops a generation AI system in which a generation AI estimates the buyer's emotions when selling information and provides recommendations based on those emotions. For example, if the buyer has positive emotions, information that will further enhance those emotions is recommended. This makes it possible to provide recommendations based on the buyer's emotions.

[0066] The sales department can analyze a buyer's past purchase history and automatically suggest related information. For example, when selling information, the sales department builds a system that analyzes a buyer's past purchase history and automatically suggests related information. For example, it recommends new information related to information purchased in the past. The sales department also develops a generative AI system that analyzes a buyer's past purchase history and automatically suggests related information. For example, it recommends new information based on the topic of information purchased in the past. The sales department also analyzes a buyer's past purchase history and automatically suggests related information when selling information. For example, it recommends new information based on the content of information purchased in the past. This makes it possible to analyze a buyer's past purchase history and suggest related information.

[0067] The sales department adds a function that allows buyers to rate information, and can improve the quality of the information based on those ratings. For example, the sales department adds a function that allows buyers to rate information when selling information, and builds a system that improves the quality of the information based on those ratings. For example, buyers rate information by stars. The sales department also collects buyer ratings and develops a generative AI system that improves the quality of information based on that data. For example, it introduces an algorithm to improve information with low ratings. The sales department also adds a function that allows buyers to rate information when selling information, and improves the quality of the information based on those ratings. For example, it updates the content of the information based on buyer feedback. This makes it possible to improve the quality of the information based on buyer ratings.

[0068] The sales department can introduce a subscription model to the information sales platform and add a service that provides the latest information on a regular basis. For example, the sales department can introduce a subscription model to the information sales platform and build a system that adds a service that provides the latest information on a regular basis. For example, the sales department can provide the latest traffic information for a monthly fee. The sales department can also introduce a subscription model and develop a generative AI system that provides the latest information on a regular basis. For example, the sales department can provide the latest road conditions on a weekly basis. The sales department can also introduce a subscription model to the information sales platform and add a service that provides the latest information on a regular basis. For example, the sales department can provide the latest weather information for an annual contract. This allows the sales department to introduce a subscription model and provide the latest information on a regular basis.

[0069] The sales department provides information at different price ranges, thereby increasing the number of options available to users according to their needs. For example, the sales department builds a system that provides information at different price ranges to an information sales platform, thereby increasing the number of options available to users according to their needs. For example, basic information is provided free of charge, and detailed information is provided for a fee. The sales department also develops a generative AI system that provides information at different price ranges, thereby increasing the number of options available to users according to their needs. For example, simple information is provided at a low price, and detailed information is provided at a high price. The sales department also provides information at different price ranges to an information sales platform, thereby increasing the number of options available to users according to their needs. For example, real-time information is provided at a high price, and past information is provided at a low price. This makes it possible to provide information at different price ranges, thereby increasing the number of options available to users according to their needs.

[0070] The sales department can use the emotion estimation function to analyze the emotions of buyers when they purchase information in real time and provide incentives to increase their willingness to purchase. For example, the sales department uses the emotion estimation function to build a system that analyzes the emotions of buyers when they purchase information in real time and provides incentives to increase their willingness to purchase. For example, if the buyer is excited, a discount coupon is provided. The sales department also develops a generative AI system that analyzes the emotions of buyers in real time and provides incentives to increase their willingness to purchase. For example, if the buyer has positive emotions, a bonus is provided. The sales department also uses the emotion estimation function to analyze the emotions of buyers when they purchase information in real time and provides incentives to increase their willingness to purchase. For example, if the buyer is relaxed, additional information is provided free of charge. In this way, it is possible to analyze the emotions of buyers and provide incentives to increase their willingness to purchase.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The photo acquisition unit can record the surrounding sound environment when a user takes a photo and use the sound information for analysis. For example, the photo acquisition unit can record the sound of a busy road and generate traffic congestion information. The photo acquisition unit can also record the sound environment and use the sound information for analysis, allowing the generation AI to generate more detailed information. For example, the photo acquisition unit can record the sound of a construction site and generate information about roads under construction. The photo acquisition unit can also record the sound environment and use the sound information for analysis, allowing the generation AI to generate more accurate information. For example, the photo acquisition unit can record the sound of rain and generate weather information. This allows the analysis accuracy to be improved by using the sound environment.

[0073] When a user takes a photo, the photo acquisition unit acquires environmental data such as the ambient temperature and humidity, and can use that data for analysis. For example, if the temperature is high, it generates information about the risk of heatstroke. The photo acquisition unit also acquires environmental data and uses that data for analysis, allowing the generation AI to generate more detailed information. For example, if the humidity is high, it generates information about the risk of mold growth. The photo acquisition unit also acquires environmental data and uses that data for analysis, allowing the generation AI to generate more accurate information. For example, if the air pressure is low, it generates information about changes in the weather. This allows the use of environmental data to improve the accuracy of analysis.

[0074] When a user takes a photo, the photo acquisition unit can detect surrounding odors and use the odor information for analysis. For example, it can detect the odor of gasoline and generate information about a gasoline leak. The photo acquisition unit can also detect odor information and use it for analysis, allowing the generation AI to generate more detailed information. For example, it can detect the odor of flowers and generate information about flower blooming. The photo acquisition unit can also detect odor information and use it for analysis, allowing the generation AI to generate more accurate information. For example, it can detect the odor of smoke and generate information about a fire. This allows the use of odor information to improve the accuracy of analysis.

[0075] The photo acquisition unit can record the ambient light environment when a user takes a photo and use that light information for analysis. For example, it can record the intensity of sunlight and generate information on the risk of sunburn. The photo acquisition unit can also record the light environment and use that light information for analysis, allowing the generation AI to generate more detailed information. For example, it can record the brightness of street lights and generate nighttime safety information. The photo acquisition unit can also record the light environment and use that light information for analysis, allowing the generation AI to generate more accurate information. For example, it can record the intensity of light on a cloudy day and generate weather information. This makes it possible to use the light environment to improve the accuracy of analysis.

[0076] The photo acquisition unit can detect surrounding vibrations when a user takes a photo and use the vibration information for analysis. For example, it can detect earthquake vibrations and generate earthquake information. The photo acquisition unit can also detect vibration information and use the vibration information for analysis, allowing the generation AI to generate more detailed information. For example, it can detect vibrations at a construction site and generate information about roads under construction. The photo acquisition unit can also detect vibration information and use the vibration information for analysis, allowing the generation AI to generate more accurate information. For example, it can detect vibrations on busy roads and generate information about traffic congestion. This makes it possible to use vibration information to improve the accuracy of analysis.

[0077] The photo acquisition unit can use the emotion estimation function to analyze the user's emotion in real time when the user takes a photo and provide feedback to elicit positive emotions. For example, if the user is smiling while taking a photo, the emotion can be determined to be positive and an encouraging message can be displayed. The photo acquisition unit can also use the emotion estimation function to analyze the user's emotion in real time when the user takes a photo and provide feedback to elicit positive emotions. For example, if the user is tired, advice on how to relax can be displayed. The photo acquisition unit can also use the emotion estimation function to analyze the user's emotion in real time when taking a photo and provide an interface to elicit positive emotions. For example, if the user is feeling negative emotions, positive music or images can be displayed. This makes it possible to analyze the user's emotion in real time and elicit positive emotions.

[0078] The analysis unit can estimate the user's emotions and generate information based on the emotions. For example, when the generation AI analyzes photos and location information, it estimates the user's emotions and generates information based on the emotions. For example, if the user has positive emotions, it generates information that reflects those emotions. The analysis unit also builds a system in which the generation AI estimates the user's emotions and generates information based on the emotions when analyzing photos and location information. For example, if the user has negative emotions, it generates information to alleviate those emotions. The analysis unit also estimates the user's emotions and generates information based on the emotions when the generation AI analyzes photos and location information. For example, if the user is excited, it generates information that reflects those emotions. This makes it possible to generate information based on the user's emotions.

[0079] The sales department can estimate the buyer's emotions when selling information and provide recommendations based on those emotions. For example, a system is built in which a generation AI estimates the buyer's emotions when selling information and provides recommendations based on those emotions. For example, if the buyer is excited, information that matches that emotion is recommended. The sales department also develops a generation AI system that analyzes the buyer's emotions in real time and provides recommendations based on those emotions. For example, if the buyer wants to relax, information that matches that emotion is recommended. The sales department also develops a generation AI system in which a generation AI estimates the buyer's emotions when selling information and provides recommendations based on those emotions. For example, if the buyer has positive emotions, information that will further enhance those emotions is recommended. This makes it possible to provide recommendations based on the buyer's emotions.

[0080] The sales department can use the emotion estimation function to analyze the emotions of buyers when they purchase information in real time and provide incentives to increase their willingness to purchase. For example, the emotion estimation function can be used to build a system that uses the emotion estimation function to analyze the emotions of buyers when they purchase information in real time and provide incentives to increase their willingness to purchase. For example, if the buyer is excited, a discount coupon can be provided. The sales department can also develop a generative AI system that analyzes the emotions of buyers in real time and provides incentives to increase their willingness to purchase. For example, if the buyer has positive emotions, a bonus can be provided. The sales department can also use the emotion estimation function to analyze the emotions of buyers when they purchase information in real time and provide incentives to increase their willingness to purchase. For example, if the buyer is relaxed, additional information can be provided for free. This makes it possible to analyze the emotions of buyers and provide incentives to increase their willingness to purchase.

[0081] The analysis unit can detect anomalies by comparing with past data and generate an alert if an abnormality occurs. For example, when the generation AI analyzes photos and location information, it compares with past data to detect anomalies and builds a system that generates an alert if an abnormality occurs. For example, it generates an alert if it detects unusual traffic conditions. In addition, the analysis unit develops a generation AI system that detects anomalies by comparing with past data and generates an alert if an abnormality occurs. For example, it generates an alert if it detects unusual road conditions. In addition, when the generation AI analyzes photos and location information, the analysis unit compares with past data to detect anomalies and generate an alert if an abnormality occurs. For example, it generates an alert if it detects unusual weather conditions. In this way, it is possible to detect anomalies by comparing with past data and generate an alert.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The photo acquisition unit acquires a photo of the user's current location. For example, the user can take a photo using a smartphone camera and upload it to the system. It is also possible to import photos taken with a digital camera or acquire photos from cloud storage. Step 2: The location information acquisition unit acquires location information of the photo acquired by the photo acquisition unit. For example, the location information can be acquired using GPS, Wi-Fi location information, or cell tower information. Step 3: The analysis unit analyzes the information acquired by the photo acquisition unit and location information acquisition unit to generate valuable information. For example, using generation AI, it generates information on road closures, traffic congestion, accidents, etc. Step 4: The sales department sells the information generated by the analytics department, for example, through an online marketplace, subscription service, or API.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0120] The 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.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 photo acquisition unit that acquires a photo of the user's current location; a location information acquisition unit that acquires location information of the photo acquired by the photo acquisition unit; an analysis unit that analyzes the information acquired by the photo acquisition unit and the location information acquisition unit and generates valuable information; a sales department that sells the information generated by the analysis department. A system characterized by:

2. The photo acquisition unit Estimate user emotions and provide feedback to elicit positive emotions 2. The system of claim 1.

3. The location information acquisition unit Automatically extracting metadata from photos and combining it with the location information to improve the analysis accuracy of the analysis unit.

2. The system of claim 1.

4. The photo acquisition unit Add a crowdsourcing feature to integrate photos taken by different users at the same location and generate more detailed information.

2. The system of claim 1.

5. The analysis unit Estimating user emotions and generating information based on those emotions 2. The system of claim 1.

6. The analysis unit Integrating different data sources to generate more accurate information 2. The system of claim 1.

7. The sales department Inferring buyer sentiment when selling information and providing sentiment-based recommendations 2. The system of claim 1.

8. The sales department Analyzing buyers' emotions in real time when purchasing information and providing incentives to increase their purchasing motivation 2. The system of claim 1.

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