system

Abstract

The system according to this embodiment aims to generate useful information from photographs provided by users and to pay appropriate compensation for that information. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and a payment unit. The reception unit receives photos from users. The analysis unit analyzes the photos received by the reception unit and generates useful information. The provision unit provides the information generated by the analysis unit. The payment unit pays an information fee based on the information provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it does not have a sufficient system for efficiently generating useful information from photos provided by users and for paying appropriate compensation for that information.

[0005] The system according to this embodiment aims to generate useful information from photographs provided by users and to pay appropriate compensation for that information. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a payment unit. The reception unit receives photos from a user. The analysis unit analyzes the photos received by the reception unit and generates useful information. The provision unit provides the information generated by the analysis unit. The payment unit pays an information fee based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate useful information from photos provided by users and provide appropriate compensation for that information. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information valuation system according to an embodiment of the present invention is a system that analyzes photographs taken by users on-site, generates valuable information, and provides it to other users. This information valuation system begins when a user takes a photograph on-site and uploads it to the service. The uploaded photograph is analyzed by AI, and valuable information is generated. The generated information is provided to other users, and the user who provided the photograph is paid an information fee. For example, a user takes a photograph showing a road closure sign or barricade and uploads it through the service's application. This photograph is analyzed by AI, and road closure information is extracted. This information is provided to other users, allowing them to obtain accurate information in real time. Furthermore, the user who provided the photograph is paid an information fee according to the value of the information provided. For example, if the road closure information is used by many users, the user who provided the information will be paid a high information fee. This mechanism provides users with an incentive to provide information on-site. In this way, the present invention realizes a mechanism that provides accurate information in real time by valuing on-site information and sharing information among users. As a result, the information valuation system can analyze photographs taken by users on-site, generate valuable information, and provide it to other users.

[0029] The information value-adding system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a payment unit. The reception unit accepts photos from users. For example, users can take photos of the location using a smartphone camera and upload the photos through the service application. The reception unit receives the uploaded photos and transmits them to the analysis unit. The analysis unit uses AI to analyze the photos and generate valuable information. For example, the analysis unit uses image recognition technology to recognize road closure signs or barricades in the photos and extract information about them. The analysis unit transmits the generated information to the provision unit. The provision unit provides the generated information to other users. For example, the provision unit can provide information to other users in real time. Users can refer to this information when they want to know the traffic conditions on a specific road. The payment unit determines an information fee based on the value of the provided information and pays it to the user who provided the photo. For example, if the road closure information is used by many users, the payment unit pays a higher information fee to the user who provided the information. As a result, the information value-adding system according to the embodiment can analyze photos taken by users on-site, generate valuable information, and provide it to other users. Also, users can have an incentive to provide information on-site.

[0030] The analysis unit can analyze the photo using image recognition technology and recognize road closure signs or barricades. Image recognition technology includes, but is not limited to, deep learning, pattern recognition, and object detection. For example, the analysis unit can recognize road closure signs in the photo using deep learning. The analysis unit can also recognize barricades using pattern recognition. Furthermore, the analysis unit can recognize road closure signs and barricades using object detection technology. This allows the analysis unit to accurately recognize road closure signs and barricades by using image recognition technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photo and perform analysis using an AI model that recognizes road closure signs and barricades.

[0031] The providing unit can provide information to other users immediately. Specific time ranges of "immediately" include, but are not limited to, within a few seconds, in real time, and without delay. The providing unit can provide information to other users within a few seconds, for example. The providing unit can also provide information in real time. Furthermore, the providing unit can provide information without delay. This allows the providing unit to provide information in real time, allowing users to obtain the latest information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that uses generated information as input and provides information to other users immediately.

[0032] The payment unit can determine an information fee based on evaluation criteria for the provided information and pay the fee to the user who provided the photo. Evaluation criteria include, but are not limited to, the accuracy, reliability, and utility value of the information. For example, the payment unit can determine the information fee based on the accuracy of the information. The payment unit can also determine the information fee based on the reliability of the information. Furthermore, the payment unit can determine the information fee based on the utility value of the information. This allows the payment unit to provide an incentive to the user by paying an information fee according to the value of the information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can determine the information fee using an AI model that uses the evaluation criteria for the provided information as input and determines the information fee.

[0033] The reception unit can accept a user's request to upload a photo through a designated application. Examples of the designated application include, but are not limited to, a smartphone application, a web application, and a specific platform. The reception unit can accept a user's request to upload a photo through, for example, a smartphone application. The reception unit can also accept a user's request to upload a photo through a web application. The reception unit can also accept a user's request to upload a photo through a specific platform. This allows the reception unit to easily upload photos. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept a photo using an AI model that accepts photos, using a photo uploaded through the designated application as input.

[0034] The analysis unit can analyze the information contained in a photograph and extract useful information. Useful information includes, but is not limited to, traffic information, weather information, and tourist information. For example, the analysis unit can extract traffic information. It can also extract weather information. Furthermore, it can extract tourist information. In this way, the analysis unit can provide useful information to other users by extracting valuable information from photographs. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can take a photograph as input and extract information using an AI model that extracts useful information.

[0035] The reception desk can analyze the user's past upload history and select an appropriate upload method. An appropriate upload method includes, but is not limited to, file format, resolution, and upload procedure. The reception desk can, for example, prioritize suggesting upload methods that the user has frequently used in the past (e.g., voice input or text input). It can also analyze the content of photos previously uploaded by the user and suggest the best upload method for similar situations. Furthermore, the reception desk can suggest the best upload method for a specific time period based on the user's past upload history. In this way, the reception desk can suggest the best upload method for the user by analyzing past upload history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can take the user's past upload history as input and select an appropriate upload method using an AI model.

[0036] The reception unit may filter photos based on the user's current location information and areas of interest when uploading photos. Examples of location information include, but are not limited to, GPS data, location-based services, and privacy protection. Examples of areas of interest include, but are not limited to, traffic information and tourist information. For example, if the user is in a specific area, the reception unit may filter photos so that only photos related to that area are uploaded. The reception unit may also filter photos so that only related photos are uploaded based on the user's areas of interest (e.g., traffic information and tourist information). Furthermore, the reception unit may prioritize uploading photos containing important nearby information based on the user's current location information. This allows the reception unit to upload highly relevant photos by filtering based on the user's location information and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may perform filtering using an AI model that uses the user's location information and areas of interest as input.

[0037] When uploading photos, the reception unit can prioritize accepting relevant photos by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, location-based services, and privacy protection. For example, if the user is in a specific area, the reception unit can prioritize accepting photos related to that area. The reception unit can also prioritize accepting photos of locations close to the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize accepting photos related to the event. In this way, the reception unit can prioritize accepting highly relevant photos by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that receives the user's geographical location information as input and prioritizes accepting relevant photos.

[0038] The reception unit can analyze the user's social media usage status when uploading a photo and accept related photos. Social media usage status includes, but is not limited to, posting frequency, number of followers, and number of likes. For example, the reception unit can accept related photos based on photos shared by the user on social media. The reception unit can also analyze the user's social media activity and preferentially accept related photos. Furthermore, the reception unit can accept related photos based on the content posted by accounts the user follows on social media. In this way, the reception unit can accept related photos by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that receives the user's social media usage status as input and accepts related photos.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on evaluation criteria for the photo. Evaluation criteria include, but are not limited to, accuracy, reliability, and utility of the information. For example, the analysis unit can perform a detailed analysis for a photo containing important information. The analysis unit can also perform a simplified analysis for a photo containing general information. Furthermore, the analysis unit can quickly analyze a photo containing highly urgent information. Thus, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of the photo. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the level of detail using an AI model that uses the evaluation criteria for the photo as input and adjusts the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the photo. Examples of classification include, but are not limited to, categorization, tagging, and clustering. For example, the analysis unit can apply a traffic analysis algorithm to a photo containing traffic information. Furthermore, the analysis unit can apply a tourism analysis algorithm to a photo containing tourist information. Furthermore, the analysis unit can apply a weather analysis algorithm to a photo containing weather information. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of the photo. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that uses the classification of the photo as input and applies different analysis algorithms.

[0041] During analysis, the analysis unit can determine the analysis priority based on the date and time the photo was taken. The date and time of the photo may include, but is not limited to, metadata, a timestamp, calendar information, etc. The analysis unit can, for example, prioritize the analysis of the most recent photo. The analysis unit can also prioritize the analysis of photos taken during a specific time period. Furthermore, the analysis unit can prioritize the analysis of photos containing highly urgent information. This allows the analysis unit to prioritize the analysis of the most recent information by determining the analysis priority based on the date and time the photo was taken. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority using an AI model that uses the date and time the photo was taken as input and determines the analysis priority.

[0042] During analysis, the analysis unit can adjust the order of analysis based on an evaluation of the relevance of the photos. Evaluations of relevance include, but are not limited to, co-occurrence frequency, relevance score, and user feedback. For example, the analysis unit can prioritize analyzing photos related to the user's field of interest. The analysis unit can also prioritize analyzing photos related to the user's current location information. Furthermore, the analysis unit can prioritize analyzing photos related to the user's past upload history. This allows the analysis unit to prioritize analyzing highly relevant information by adjusting the order of analysis based on the relevance of the photos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the order using an AI model that uses the evaluation of the relevance of the photos as input and adjusts the order of analysis.

[0043] The providing unit can adjust the level of detail of the information provided based on evaluation criteria for the information when providing the information. Evaluation criteria include, but are not limited to, accuracy, reliability, and utility of the information. For example, the providing unit can provide detailed information when the information includes important information. Furthermore, the providing unit can provide simplified information when the information includes general information. Furthermore, the providing unit can provide information quickly when the information includes highly urgent information. Thus, the providing unit can provide important information in detail by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail using an AI model that uses the evaluation criteria for the information as input and adjusts the level of detail of the information provided.

[0044] The providing unit can apply different provision algorithms depending on the classification of information when providing the information. Examples of classification include, but are not limited to, categorization, tagging, and clustering. For example, the providing unit can apply a traffic provision algorithm when providing traffic information. Furthermore, the providing unit can apply a tourism provision algorithm when providing tourist information. Furthermore, the providing unit can apply a weather provision algorithm when providing weather information. In this way, the providing unit can provide more accurate information by applying different provision algorithms depending on the category of information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives the classification of information and applies different provision algorithms.

[0045] The providing unit can determine the priority of provision based on the submission date and time of the information when providing the information. The submission date and time may include, but is not limited to, metadata, timestamps, calendar information, etc. The providing unit can, for example, prioritize providing the latest information. The providing unit can also prioritize providing information submitted within a specific time period. Furthermore, the providing unit can prioritize providing information with high urgency. In this way, the providing unit can prioritize providing the latest information by determining the priority of provision based on the submission date and time of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the priority using an AI model that inputs the submission date and time of the information and determines the priority of provision.

[0046] The providing unit can adjust the order of providing information based on an evaluation of the relevance of the information when providing the information. Examples of the evaluation of the relevance include, but are not limited to, co-occurrence frequency, relevance score, and user feedback. For example, the providing unit can prioritize providing information related to a user's field of interest. The providing unit can also prioritize providing information related to the user's current location information. Furthermore, the providing unit can prioritize providing information related to the user's past upload history. In this way, the providing unit can prioritize providing highly relevant information by adjusting the order of providing the information based on the relevance of the information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the order using an AI model that uses the evaluation of the relevance of the information as input and adjusts the order of providing the information.

[0047] At the time of payment, the payment unit can adjust the payment amount based on the evaluation criteria of the information. The evaluation criteria include, but are not limited to, the accuracy, reliability, and usefulness of the information. For example, the payment unit can set a high payment amount if important information is provided. The payment unit can also set a standard payment amount if general information is provided. Furthermore, the payment unit can quickly set a high payment amount if highly urgent information is provided. This allows the payment unit to pay an appropriate reward based on the value of the information provided. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit can adjust the payment amount using an AI model that uses the evaluation criteria of the information as input and adjusts the payment amount.

[0048] The payment unit can vary the payment amount depending on the frequency of use of the information at the time of payment. Examples of frequency of use include, but are not limited to, the number of accesses, the duration of use, and the number of users. For example, the payment unit can set a high payment amount for information used by many users. The payment unit can also set a standard payment amount for information with a general frequency of use. Furthermore, the payment unit can vary the payment amount depending on the frequency of use for information frequently used by specific users. This allows the payment unit to pay appropriate compensation for frequently used information by varying the payment amount depending on the frequency of use of the information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can vary the payment amount using an AI model that uses the frequency of use of the information as an input and varies the payment amount.

[0049] At the time of payment, the payment unit can adjust the payment amount based on the date and time of information provision. Examples of the date and time of provision include, but are not limited to, metadata, timestamps, calendar information, etc. The payment unit can, for example, set a higher payment amount if the latest information is provided. The payment unit can also set a standard payment amount for information provided within a specific time period. Furthermore, the payment unit can quickly set a higher payment amount if the information provided is highly urgent. This allows the payment unit to pay an appropriate reward for the latest information by adjusting the payment amount based on the time of information provision. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can adjust the payment amount using an AI model that uses the date and time of information provision as input and adjusts the payment amount.

[0050] The payment unit may vary the payment amount based on an evaluation of the relevance of the information at the time of payment. Examples of evaluation of relevance include, but are not limited to, co-occurrence frequency, relevance score, and user feedback. For example, the payment unit may set a high payment amount when the user provides information related to the user's field of interest. The payment unit may also set a standard payment amount when the user provides information related to the user's current location information. Furthermore, the payment unit may vary the payment amount according to the frequency of use when the user provides information related to the user's past upload history. This allows the payment unit to pay appropriate compensation for highly relevant information by varying the payment amount based on the relevance of the information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit may vary the payment amount using an AI model that uses the evaluation of the relevance of the information as an input and varies the payment amount.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The analysis unit can also analyze audio data included in photos and extract audio information. For example, traffic announcements can be extracted from the audio data included in photos and provided as real-time traffic information. Guide audio at tourist spots can also be analyzed and provided as tourist information. Event announcements can also be analyzed and provided as event information. This allows the analysis unit to provide a wider variety of information by analyzing audio data included in photos.

[0053] The providing unit can analyze the user's past search history and provide related information preferentially. For example, if the user has frequently searched for traffic information in a specific area in the past, the latest traffic information for that area can be provided preferentially. Also, if the user has frequently searched for tourist information in the past, tourist information can be provided preferentially. Furthermore, if the user has frequently searched for weather information in the past, weather information can be provided preferentially. In this way, the providing unit can provide more relevant information by analyzing the user's past search history.

[0054] The payment unit can adjust the reward based on the user's level of contribution. For example, if the user provides a lot of useful information, a high reward can be set. Also, if the user continues to provide information for a long period of time, the reward can be increased. Furthermore, if the user provides specialized information in a particular field, a reward according to the expertise can be set. In this way, the payment unit can increase the user's motivation by adjusting the reward based on the user's level of contribution.

[0055] The reception unit can adjust the upload method taking into account the remaining battery power of the user's device. For example, if the remaining battery power is low, low-resolution photos can be uploaded preferentially. Also, if the remaining battery power is insufficient, the upload procedure can be simplified. Furthermore, if the remaining battery power is sufficient, high-resolution photos can be uploaded. In this way, the reception unit can provide an efficient upload method by taking into account the remaining battery power of the user's device.

[0056] The analysis unit can analyze text data included in a photo and extract text information. For example, the text on a sign in a photo can be analyzed and provided as traffic information. The text on a menu in a photo can also be analyzed and provided as restaurant information. Furthermore, the text on an event poster in a photo can be analyzed and provided as event information. In this way, the analysis unit can provide a wider variety of information by analyzing the text data included in a photo.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit accepts photos from users. Users can take photos of the location using their smartphone camera and upload them through the service's application. The reception unit receives the uploaded photos and sends them to the analysis unit. Step 2: The analysis unit analyzes the photos received by the reception unit and generates valuable information. The analysis unit uses AI to analyze the photos, for example, using image recognition technology to recognize road closure signs and barricades in the photos and extract that information. The analysis unit sends the generated information to the provision unit. Step 3: The providing unit provides the information generated by the analysis unit. The providing unit provides the generated information to other users, for example, in real time. When a user wants to know the traffic conditions of a specific road, the user can refer to this information. Step 4: The payment unit pays an information fee based on the information provided by the providing unit. The payment unit determines an information fee according to the value of the provided information and pays it to the user who provided the photo. For example, if information about a road closure is used by many users, the payment unit will pay a high information fee to the user who provided the information.

[0059] (Example 2) An information valuation system according to an embodiment of the present invention is a system that analyzes photographs taken by users on-site, generates valuable information, and provides it to other users. This information valuation system begins when a user takes a photograph on-site and uploads it to the service. The uploaded photograph is analyzed by AI, and valuable information is generated. The generated information is provided to other users, and the user who provided the photograph is paid an information fee. For example, a user takes a photograph showing a road closure sign or barricade and uploads it through the service's application. This photograph is analyzed by AI, and road closure information is extracted. This information is provided to other users, allowing them to obtain accurate information in real time. Furthermore, the user who provided the photograph is paid an information fee according to the value of the information provided. For example, if the road closure information is used by many users, the user who provided the information will be paid a high information fee. This mechanism provides users with an incentive to provide information on-site. In this way, the present invention realizes a mechanism that provides accurate information in real time by valuing on-site information and sharing information among users. As a result, the information valuation system can analyze photographs taken by users on-site, generate valuable information, and provide it to other users.

[0060] The information value-adding system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a payment unit. The reception unit accepts photos from users. For example, users can take photos of the location using a smartphone camera and upload the photos through the service application. The reception unit receives the uploaded photos and transmits them to the analysis unit. The analysis unit uses AI to analyze the photos and generate valuable information. For example, the analysis unit uses image recognition technology to recognize road closure signs or barricades in the photos and extract information about them. The analysis unit transmits the generated information to the provision unit. The provision unit provides the generated information to other users. For example, the provision unit can provide information to other users in real time. Users can refer to this information when they want to know the traffic conditions on a specific road. The payment unit determines an information fee based on the value of the provided information and pays it to the user who provided the photo. For example, if the road closure information is used by many users, the payment unit pays a higher information fee to the user who provided the information. As a result, the information value-adding system according to the embodiment can analyze photos taken by users on-site, generate valuable information, and provide it to other users. Also, users can have an incentive to provide information on-site.

[0061] The analysis unit can analyze the photo using image recognition technology and recognize road closure signs or barricades. Image recognition technology includes, but is not limited to, deep learning, pattern recognition, and object detection. For example, the analysis unit can recognize road closure signs in the photo using deep learning. The analysis unit can also recognize barricades using pattern recognition. Furthermore, the analysis unit can recognize road closure signs and barricades using object detection technology. This allows the analysis unit to accurately recognize road closure signs and barricades by using image recognition technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input a photo and perform analysis using an AI model that recognizes road closure signs and barricades.

[0062] The providing unit can provide information to other users immediately. Specific time ranges of "immediately" include, but are not limited to, within a few seconds, in real time, and without delay. The providing unit can provide information to other users within a few seconds, for example. The providing unit can also provide information in real time. Furthermore, the providing unit can provide information without delay. This allows the providing unit to provide information in real time, allowing users to obtain the latest information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that uses generated information as input and provides information to other users immediately.

[0063] The payment unit can determine an information fee based on evaluation criteria for the provided information and pay the fee to the user who provided the photo. Evaluation criteria include, but are not limited to, the accuracy, reliability, and utility value of the information. For example, the payment unit can determine the information fee based on the accuracy of the information. The payment unit can also determine the information fee based on the reliability of the information. Furthermore, the payment unit can determine the information fee based on the utility value of the information. This allows the payment unit to provide an incentive to the user by paying an information fee according to the value of the information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can determine the information fee using an AI model that uses the evaluation criteria for the provided information as input and determines the information fee.

[0064] The reception unit can accept a user's request to upload a photo through a designated application. Examples of the designated application include, but are not limited to, a smartphone application, a web application, and a specific platform. The reception unit can accept a user's request to upload a photo through, for example, a smartphone application. The reception unit can also accept a user's request to upload a photo through a web application. The reception unit can also accept a user's request to upload a photo through a specific platform. This allows the reception unit to easily upload photos. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept a photo using an AI model that accepts photos, using a photo uploaded through the designated application as input.

[0065] The analysis unit can analyze the information contained in a photograph and extract useful information. Useful information includes, but is not limited to, traffic information, weather information, and tourist information. For example, the analysis unit can extract traffic information. It can also extract weather information. Furthermore, it can extract tourist information. In this way, the analysis unit can provide useful information to other users by extracting valuable information from photographs. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can take a photograph as input and extract information using an AI model that extracts useful information.

[0066] The reception desk can analyze the user's emotions and adjust the timing of photo uploads based on the analyzed emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is excited, the reception desk can simplify the interface to allow for immediate photo uploads. If the user is tired, the reception desk can minimize the upload process and allow for one-click uploads. Furthermore, if the user is relaxed, the reception desk can provide detailed upload options and suggest customizable upload methods. This allows the reception desk to upload photos at a more appropriate time by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can take user emotion data as input and adjust the timing using an AI model that adjusts the upload timing.

[0067] The reception desk can analyze the user's past upload history and select an appropriate upload method. An appropriate upload method includes, but is not limited to, file format, resolution, and upload procedure. The reception desk can, for example, prioritize suggesting upload methods that the user has frequently used in the past (e.g., voice input or text input). It can also analyze the content of photos previously uploaded by the user and suggest the best upload method for similar situations. Furthermore, the reception desk can suggest the best upload method for a specific time period based on the user's past upload history. In this way, the reception desk can suggest the best upload method for the user by analyzing past upload history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can take the user's past upload history as input and select an appropriate upload method using an AI model.

[0068] The reception unit may filter photos based on the user's current location information and areas of interest when uploading photos. Examples of location information include, but are not limited to, GPS data, location-based services, and privacy protection. Examples of areas of interest include, but are not limited to, traffic information and tourist information. For example, if the user is in a specific area, the reception unit may filter photos so that only photos related to that area are uploaded. The reception unit may also filter photos so that only related photos are uploaded based on the user's areas of interest (e.g., traffic information and tourist information). Furthermore, the reception unit may prioritize uploading photos containing important nearby information based on the user's current location information. This allows the reception unit to upload highly relevant photos by filtering based on the user's location information and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may perform filtering using an AI model that uses the user's location information and areas of interest as input.

[0069] The reception unit can analyze the user's emotions and determine the priority of photos to be uploaded based on the analyzed user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, if the user is excited, the reception unit can prioritize uploading the most recent photos. Furthermore, if the user is relaxed, the reception unit can also upload photos taken in the past. Furthermore, if the user is in a hurry, the reception unit can prioritize uploading photos containing important information. Thus, the reception unit can prioritize uploading important photos by determining the priority of photos according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or without AI. For example, the reception unit can determine the priority using an AI model that receives the user's emotion data and determines the priority of photos.

[0070] When uploading photos, the reception unit can prioritize accepting relevant photos by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, location-based services, and privacy protection. For example, if the user is in a specific area, the reception unit can prioritize accepting photos related to that area. The reception unit can also prioritize accepting photos of locations close to the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize accepting photos related to the event. In this way, the reception unit can prioritize accepting highly relevant photos by taking the user's geographical location information into account. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that receives the user's geographical location information as input and prioritizes accepting relevant photos.

[0071] The reception unit can analyze the user's social media usage status when uploading a photo and accept related photos. Social media usage status includes, but is not limited to, posting frequency, number of followers, and number of likes. For example, the reception unit can accept related photos based on photos shared by the user on social media. The reception unit can also analyze the user's social media activity and preferentially accept related photos. Furthermore, the reception unit can accept related photos based on the content posted by accounts the user follows on social media. In this way, the reception unit can accept related photos by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can accept photos using an AI model that receives the user's social media usage status as input and accepts related photos.

[0072] The analysis unit can analyze the user's emotions and adjust the presentation of the analysis based on the analyzed emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can take user emotion data as input and adjust the presentation using an AI model that adjusts the presentation of the analysis.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on evaluation criteria for the photo. Evaluation criteria include, but are not limited to, accuracy, reliability, and utility of the information. For example, the analysis unit can perform a detailed analysis for a photo containing important information. The analysis unit can also perform a simplified analysis for a photo containing general information. Furthermore, the analysis unit can quickly analyze a photo containing highly urgent information. Thus, the analysis unit can analyze important information in detail by adjusting the level of detail of the analysis based on the importance of the photo. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the level of detail using an AI model that uses the evaluation criteria for the photo as input and adjusts the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the photo. Examples of classification include, but are not limited to, categorization, tagging, and clustering. For example, the analysis unit can apply a traffic analysis algorithm to a photo containing traffic information. Furthermore, the analysis unit can apply a tourism analysis algorithm to a photo containing tourist information. Furthermore, the analysis unit can apply a weather analysis algorithm to a photo containing weather information. In this way, the analysis unit can provide more accurate analysis results by applying different analysis algorithms depending on the category of the photo. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can perform analysis using an AI model that uses the classification of the photo as input and applies different analysis algorithms.

[0075] The analysis unit can analyze the user's emotions and adjust the length of the analysis based on the analyzed user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. This allows the analysis unit to adjust the length of the analysis according to the user's emotions and provide more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can adjust the length using an AI model that inputs the user's emotion data and adjusts the length of the analysis.

[0076] During analysis, the analysis unit can determine the analysis priority based on the date and time the photo was taken. The date and time of the photo may include, but is not limited to, metadata, a timestamp, calendar information, etc. The analysis unit can, for example, prioritize the analysis of the most recent photo. The analysis unit can also prioritize the analysis of photos taken during a specific time period. Furthermore, the analysis unit can prioritize the analysis of photos containing highly urgent information. This allows the analysis unit to prioritize the analysis of the most recent information by determining the analysis priority based on the date and time the photo was taken. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can determine the priority using an AI model that uses the date and time the photo was taken as input and determines the analysis priority.

[0077] During analysis, the analysis unit can adjust the order of analysis based on an evaluation of the relevance of the photos. Evaluations of relevance include, but are not limited to, co-occurrence frequency, relevance score, and user feedback. For example, the analysis unit can prioritize analyzing photos related to the user's field of interest. The analysis unit can also prioritize analyzing photos related to the user's current location information. Furthermore, the analysis unit can prioritize analyzing photos related to the user's past upload history. This allows the analysis unit to prioritize analyzing highly relevant information by adjusting the order of analysis based on the relevance of the photos. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can adjust the order using an AI model that uses the evaluation of the relevance of the photos as input and adjusts the order of analysis.

[0078] The providing unit can analyze the user's emotions and adjust the information display method based on the analyzed user's emotions. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, when the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, when the user is relaxed, the providing unit can provide a display method that includes detailed information. Furthermore, when the user is in a hurry, the providing unit can provide a display method that focuses on the main points. This allows the providing unit to adjust the information display method according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can adjust the display method using an AI model that receives user emotion data as input and adjusts the information display method.

[0079] The providing unit can adjust the level of detail of the information provided based on evaluation criteria for the information when providing the information. Evaluation criteria include, but are not limited to, accuracy, reliability, and utility of the information. For example, the providing unit can provide detailed information when the information includes important information. Furthermore, the providing unit can provide simplified information when the information includes general information. Furthermore, the providing unit can provide information quickly when the information includes highly urgent information. Thus, the providing unit can provide important information in detail by adjusting the level of detail of the information provided based on the importance of the information. Some or all of the above-described processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can adjust the level of detail using an AI model that uses the evaluation criteria for the information as input and adjusts the level of detail of the information provided.

[0080] The providing unit can apply different provision algorithms depending on the classification of information when providing the information. Examples of classification include, but are not limited to, categorization, tagging, and clustering. For example, the providing unit can apply a traffic provision algorithm when providing traffic information. Furthermore, the providing unit can apply a tourism provision algorithm when providing tourist information. Furthermore, the providing unit can apply a weather provision algorithm when providing weather information. In this way, the providing unit can provide more accurate information by applying different provision algorithms depending on the category of information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can provide information using an AI model that receives the classification of information and applies different provision algorithms.

[0081] The information provider can analyze the user's emotions and adjust the display order of information based on the analyzed emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is in a hurry, the information provider can prioritize displaying important information. If the user is relaxed, the information provider can also display information in an order that includes detailed information. Furthermore, if the user is excited, the information provider can prioritize displaying visually stimulating information. In this way, the information provider can provide more appropriate information by adjusting the display order of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can take user emotion data as input and adjust the order using an AI model that adjusts the display order of information.

[0082] The providing unit can determine the priority of provision based on the submission date and time of the information when providing the information. The submission date and time may include, but is not limited to, metadata, timestamps, calendar information, etc. The providing unit can, for example, prioritize providing the latest information. The providing unit can also prioritize providing information submitted within a specific time period. Furthermore, the providing unit can prioritize providing information with high urgency. In this way, the providing unit can prioritize providing the latest information by determining the priority of provision based on the submission date and time of the information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can determine the priority using an AI model that inputs the submission date and time of the information and determines the priority of provision.

[0083] The providing unit can adjust the order of providing information based on an evaluation of the relevance of the information when providing the information. Examples of the evaluation of the relevance include, but are not limited to, co-occurrence frequency, relevance score, and user feedback. For example, the providing unit can prioritize providing information related to a user's field of interest. The providing unit can also prioritize providing information related to the user's current location information. Furthermore, the providing unit can prioritize providing information related to the user's past upload history. In this way, the providing unit can prioritize providing highly relevant information by adjusting the order of providing the information based on the relevance of the information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can adjust the order using an AI model that uses the evaluation of the relevance of the information as input and adjusts the order of providing the information.

[0084] The payment unit can analyze the user's emotions and adjust the payment method based on those emotions. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is nervous, the payment unit can provide a simple and quick payment method. If the user is relaxed, it can also provide more detailed payment options. Furthermore, if the user is in a hurry, it can provide a way to complete the payment with a single click. In this way, the payment unit can provide a more appropriate payment method by adjusting it according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the payment unit may be performed using AI or not. For example, the payment unit can take user emotion data as input and adjust the payment method using an AI model.

[0085] At the time of payment, the payment unit can adjust the payment amount based on the evaluation criteria of the information. The evaluation criteria include, but are not limited to, the accuracy, reliability, and usefulness of the information. For example, the payment unit can set a high payment amount if important information is provided. The payment unit can also set a standard payment amount if general information is provided. Furthermore, the payment unit can quickly set a high payment amount if highly urgent information is provided. This allows the payment unit to pay an appropriate reward based on the value of the information provided. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit can adjust the payment amount using an AI model that uses the evaluation criteria of the information as input and adjusts the payment amount.

[0086] The payment unit can vary the payment amount depending on the frequency of use of the information at the time of payment. Examples of frequency of use include, but are not limited to, the number of accesses, the duration of use, and the number of users. For example, the payment unit can set a high payment amount for information used by many users. The payment unit can also set a standard payment amount for information with a general frequency of use. Furthermore, the payment unit can vary the payment amount depending on the frequency of use for information frequently used by specific users. This allows the payment unit to pay appropriate compensation for frequently used information by varying the payment amount depending on the frequency of use of the information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can vary the payment amount using an AI model that uses the frequency of use of the information as an input and varies the payment amount.

[0087] The payment unit can analyze the user's emotions and determine payment priorities based on the analyzed user emotions. Emotions include, but are not limited to, joy, sadness, anger, and the like. For example, the payment unit can quickly make payments when the user is in a hurry. The payment unit can also provide detailed payment options when the user is relaxed. Furthermore, the payment unit can provide visually stimulating payment methods when the user is excited. This allows the payment unit to determine payment priorities based on the user's emotions, thereby enabling payments to be made at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the payment unit can be performed using, for example, AI, or without AI. For example, the payment unit can determine the priorities using an AI model that uses the user's emotion data as input and determines payment priorities.

[0088] At the time of payment, the payment unit can adjust the payment amount based on the date and time of information provision. Examples of the date and time of provision include, but are not limited to, metadata, timestamps, calendar information, etc. The payment unit can, for example, set a higher payment amount if the latest information is provided. The payment unit can also set a standard payment amount for information provided within a specific time period. Furthermore, the payment unit can quickly set a higher payment amount if the information provided is highly urgent. This allows the payment unit to pay an appropriate reward for the latest information by adjusting the payment amount based on the time of information provision. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can adjust the payment amount using an AI model that uses the date and time of information provision as input and adjusts the payment amount.

[0089] The payment unit may vary the payment amount based on an evaluation of the relevance of the information at the time of payment. Examples of evaluation of relevance include, but are not limited to, co-occurrence frequency, relevance score, and user feedback. For example, the payment unit may set a high payment amount when the user provides information related to the user's field of interest. The payment unit may also set a standard payment amount when the user provides information related to the user's current location information. Furthermore, the payment unit may vary the payment amount according to the frequency of use when the user provides information related to the user's past upload history. This allows the payment unit to pay appropriate compensation for highly relevant information by varying the payment amount based on the relevance of the information. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit may vary the payment amount using an AI model that uses the evaluation of the relevance of the information as an input and varies the payment amount. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and payment unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives photos taken by a user using a smartphone camera. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photos using AI to generate valuable information. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated information to other users. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines an information fee according to the value of the provided information and pays it to the user who provided the photo. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, provision unit, and payment unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives a photo taken by a user using the camera of the smart glasses. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to generate valuable information. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the generated information to other users. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines an information fee according to the value of the provided information and pays it to the user who provided the photo. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and payment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives a photo taken by a user using the camera of the headset type terminal. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photo using AI to generate valuable information. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the generated information to other users. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines an information fee according to the value of the provided information and pays it to the user who provided the photo. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, and payment unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives photos taken by a user using the robot's camera. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the photos using AI to generate valuable information. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated information to other users. The payment unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines an information fee according to the value of the provided information and pays it to the user who provided the photo.

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

[0091] The analysis unit can also analyze audio data included in photos and extract audio information. For example, traffic announcements can be extracted from the audio data included in photos and provided as real-time traffic information. Guide audio at tourist spots can also be analyzed and provided as tourist information. Event announcements can also be analyzed and provided as event information. This allows the analysis unit to provide a wider variety of information by analyzing audio data included in photos.

[0092] The providing unit can analyze the user's past search history and provide related information preferentially. For example, if the user has frequently searched for traffic information in a specific area in the past, the latest traffic information for that area can be provided preferentially. Also, if the user has frequently searched for tourist information in the past, tourist information can be provided preferentially. Furthermore, if the user has frequently searched for weather information in the past, weather information can be provided preferentially. In this way, the providing unit can provide more relevant information by analyzing the user's past search history.

[0093] The payment unit can adjust the reward based on the user's level of contribution. For example, if the user provides a lot of useful information, a high reward can be set. Also, if the user continues to provide information for a long period of time, the reward can be increased. Furthermore, if the user provides specialized information in a particular field, a reward according to the expertise can be set. In this way, the payment unit can increase the user's motivation by adjusting the reward based on the user's level of contribution.

[0094] The reception unit can adjust the upload method taking into account the remaining battery power of the user's device. For example, if the remaining battery power is low, low-resolution photos can be uploaded preferentially. Also, if the remaining battery power is insufficient, the upload procedure can be simplified. Furthermore, if the remaining battery power is sufficient, high-resolution photos can be uploaded. In this way, the reception unit can provide an efficient upload method by taking into account the remaining battery power of the user's device.

[0095] The analysis unit can analyze text data included in a photo and extract text information. For example, the text on a sign in a photo can be analyzed and provided as traffic information. The text on a menu in a photo can also be analyzed and provided as restaurant information. Furthermore, the text on an event poster in a photo can be analyzed and provided as event information. In this way, the analysis unit can provide a wider variety of information by analyzing the text data included in a photo.

[0096] The providing unit can analyze the user's emotions and adjust the information display method based on the analyzed user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the providing unit can provide more appropriate information by adjusting the information display method according to the user's emotions.

[0097] The analysis unit can analyze the user's emotions and adjust the way the analysis is presented based on the analyzed user's emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. If the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that focus on the main points. In this way, the analysis unit can provide more appropriate analysis results by adjusting the way the analysis is presented according to the user's emotions.

[0098] The payment unit can analyze the user's emotions and adjust the payment method based on the analyzed user's emotions. For example, if the user is nervous, a simple and quick payment method can be provided. If the user is relaxed, detailed payment options can be provided. Furthermore, if the user is in a hurry, a method that allows payment to be completed with one click can be provided. In this way, the payment unit can provide a more appropriate payment method by adjusting the payment method according to the user's emotions.

[0099] The reception unit can analyze the user's emotions and adjust the timing of photo uploads based on the analyzed user's emotions. For example, if the user is excited, the interface can be simplified to allow the user to upload photos instantly. Also, if the user is tired, the upload procedure can be minimized to allow one-click uploads. Furthermore, if the user is relaxed, detailed upload options can be provided and a customizable upload method can be suggested. In this way, the reception unit can adjust the upload timing according to the user's emotions, allowing the user to upload photos at more appropriate times.

[0100] The providing unit can analyze the user's emotions and adjust the display order of information based on the analyzed user's emotions. For example, if the user is in a hurry, important information can be displayed preferentially. Also, if the user is relaxed, detailed information can be displayed in an order that includes detailed information. Furthermore, if the user is excited, visually stimulating information can be displayed preferentially. In this way, the providing unit can provide more appropriate information by adjusting the display order of information according to the user's emotions.

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

[0102] Step 1: The reception unit accepts photos from users. Users can take photos of the location using their smartphone camera and upload them through the service's application. The reception unit receives the uploaded photos and sends them to the analysis unit. Step 2: The analysis unit analyzes the photos received by the reception unit and generates valuable information. The analysis unit uses AI to analyze the photos, for example, using image recognition technology to recognize road closure signs and barricades in the photos and extract that information. The analysis unit sends the generated information to the provision unit. Step 3: The providing unit provides the information generated by the analysis unit. The providing unit provides the generated information to other users, for example, in real time. When a user wants to know the traffic conditions of a specific road, the user can refer to this information. Step 4: The payment unit pays an information fee based on the information provided by the providing unit. The payment unit determines an information fee according to the value of the provided information and pays it to the user who provided the photo. For example, if information about a road closure is used by many users, the payment unit will pay a high information fee to the user who provided the information.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a photo from a user; an analysis unit that analyzes the photographs received by the reception unit and generates useful information; a providing unit that provides the information generated by the analysis unit; a payment unit that pays an information fee based on the information provided by the providing unit; A system characterized by:

2. The analysis unit Image recognition technology is used to analyze photos and identify road closure signs or barricades 2. The system of claim 1.

3. The providing unit Provide instant information to other users 2. The system of claim 1.

4. The payment unit An information fee is determined based on the evaluation criteria of the information provided, and paid to the user who provided the photo.

2. The system of claim 1.

5. The reception unit Accepts user photo uploads through a specified application 2. The system of claim 1.

6. The analysis unit Analyzing the information in a photo and extracting useful information 2. The system of claim 1.

7. The reception unit Analyze user emotions and adjust the timing of photo uploads based on the analyzed user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's upload history and select the appropriate upload method 2. The system of claim 1.

Images