System

Abstract

An object of the system according to the embodiment is to determine an efficient and consistent annotation by utilizing image authentication data of a user.SOLUTION: A system according to an embodiment includes a collection unit, a determination unit, and a storage unit. The collection unit collects image authentication data from a user. The determination unit determines the annotation based on the data collected by the collection unit. The storage unit stores the annotation data determined by the determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are prone to bias in AI training data, posing challenges to the efficiency and consistency of annotation work.

[0005] The system according to the embodiment aims to determine annotations efficiently and consistently by utilizing image authentication data of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, and a storage unit. The collection unit collects image authentication data from a user. The determination unit determines annotations based on the data collected by the collection unit. The storage unit stores the annotation data determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize the image authentication data of the user to determine the annotation efficiently and consistently. [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 image annotation system according to an embodiment of the present invention collects image authentication data from users, confirms annotations, and stores them. The image annotation system uses CAPTCHAs linked to users' daily login behavior to perform image annotations. When a certain number of responses are received, the system uses the responses as annotations based on the user's recognition rate. For example, when a user logs in, a CAPTCHA is displayed for image authentication. The user is then prompted to select a specific image. For example, instructions such as "Please select an image that shows a traffic light" are displayed. When the user selects the correct image, the information is recorded as annotation data. Next, if a certain number of users select the same image, the annotation for that image is confirmed. For example, if 100 users recognize the same image as a "traffic light," the image is annotated as a "traffic light." This mechanism ensures consistency in annotations. Furthermore, the system can collect annotations from a large number of users in a short period of time. For example, if 100 million users log in and complete a 10-second CAPTCHA test, 30 years' worth of annotation data can be collected in a short period of time. This overcomes the challenges of traditional human annotation, such as high outsourcing costs, worker bias, and lack of consistency. Image annotation systems effectively address the bias issues of generative AI, enabling more accurate image generation. For example, if the data used by generative AI contains bias toward a particular race or gender, this system can annotate more diverse data and mitigate bias. This system can also be applied to other fields. For example, in the medical field, annotating image data used by doctors during diagnoses can improve the accuracy of AI diagnoses. Furthermore, in the transportation field, annotating images of road signs and traffic lights can improve the recognition accuracy of self-driving cars. Thus, image annotation systems are a groundbreaking solution to the bias issues of generative AI, and are expected to be applied in a variety of fields.

[0029] An image annotation system according to an embodiment includes a collection unit, a determination unit, and a storage unit. The collection unit collects image authentication data from a user. For example, when a user logs in, the collection unit displays a CAPTCHA prompting the user to select a specific image. For example, an instruction such as "Please select an image that shows a traffic light" is displayed. When the user selects the correct image, the collection unit records the information. For example, the collection unit can also store the image data selected by the user in cloud storage. For example, the collection unit uploads the image data selected by the user to cloud storage to facilitate data management. The determination unit determines the annotation based on the data collected by the collection unit. For example, the determination unit includes an algorithm that determines the annotation when a certain number of users select the same image. For example, if 100 users recognize the same image as a "traffic light," the image is annotated as a "traffic light." The determination unit can also set criteria for accepting annotations based on user recognition rates. For example, the annotation is determined when 80% or more users select the same image. The storage unit stores the annotation data confirmed by the confirmation unit. The storage unit can, for example, store the confirmed annotation data in cloud storage. For example, the storage unit can upload the confirmed annotation data to the cloud storage to facilitate data management. This allows the image annotation system according to the embodiment to efficiently collect, confirm, and store image authentication data from users.

[0030] The image annotation system includes a collection unit that stores image data selected by a user in cloud storage. The collection unit stores the image data selected by the user in the cloud storage. Examples of cloud storage include, but are not limited to, AWS (registered trademark) S3 and Google (registered trademark) Cloud Storage. The collection unit, for example, uploads the image data selected by the user to the cloud storage to facilitate data management. For example, the collection unit can also store the image data selected by the user in the cloud storage in real time. This facilitates data management by storing the image data selected by the user in the cloud storage.

[0031] The image annotation system includes a confirmation unit including an algorithm that confirms an annotation when a certain number of users or more select the same image. The confirmation unit includes an algorithm that confirms an annotation when a certain number of users or more select the same image. Criteria for a certain number or more include, but are not limited to, 10 or more users, 50% or more, etc. For example, if 100 users recognize the same image as a "traffic light," the confirmation unit annotates the image as a "traffic light." The confirmation unit can also confirm the annotation using, for example, a machine learning algorithm or a rule-based algorithm. This ensures consistency of the annotation by confirming the annotation when a certain number of users or more select the same image.

[0032] The image annotation system includes a storage unit that stores the confirmed annotation data in cloud storage. The storage unit stores the confirmed annotation data in the cloud storage. Examples of cloud storage include, but are not limited to, AWS S3 and Google Cloud Storage. The storage unit, for example, uploads the confirmed annotation data to the cloud storage to facilitate data management. For example, the storage unit can store the confirmed annotation data in the cloud storage in real time. This facilitates data management by storing the confirmed annotation data in the cloud storage.

[0033] The image annotation system includes a determination unit that sets an annotation adoption criterion based on a user's recognition ratio. The determination unit sets the annotation adoption criterion based on the user's recognition ratio. The recognition ratio includes, but is not limited to, a recognition rate of 80% or more. The determination unit determines the annotation when, for example, 80% or more users select the same image. The determination unit can also calculate the recognition ratio using, for example, a machine learning algorithm or a rule-based algorithm and set the annotation adoption criterion. By setting the annotation adoption criterion based on the user's recognition ratio, the accuracy of the annotation is improved.

[0034] The image annotation system includes a collection unit that collects image data in the medical field or the traffic field. The collection unit collects image data in the medical field or the traffic field. The medical field includes, for example, but is not limited to, radiological images and pathological images. The traffic field includes, for example, but is not limited to, images from traffic surveillance cameras and vehicle license plate images. The collection unit can collect image data in the medical field, for example, to improve the accuracy of AI-based diagnoses. The collection unit can also collect image data in the traffic field to improve the recognition accuracy of self-driving cars. As a result, by collecting image data in the medical field or the traffic field, the accuracy of annotation in a specific field is improved.

[0035] The image annotation system includes a determination unit that determines annotations in the medical field and the traffic field. The determination unit determines the annotations in the medical field and the traffic field. The medical field includes, for example, but is not limited to, radiological images and pathological images. The traffic field includes, for example, but is not limited to, images from traffic surveillance cameras and vehicle license plate images. The determination unit, for example, determines annotations in the medical field to improve the accuracy of AI-based diagnoses. Furthermore, the determination unit determines annotations in the traffic field to improve the recognition accuracy of self-driving vehicles. As a result, by determining annotations in the medical field and the traffic field, the accuracy of annotations in specific fields is improved.

[0036] The image annotation system includes a collection unit that analyzes a user's past login history and selects an optimal collection method. The collection unit analyzes the user's past login history and selects an optimal collection method. Examples of optimal collection methods include, but are not limited to, image capture and screenshots. The collection unit, for example, performs image authentication based on a time period in which the user frequently logged in in the past. The collection unit can also select an optimal collection method (audio, text, image, etc.) based on, for example, a device used by the user in the past. The collection unit can also concentrate collection on specific days of the week or time periods based on the user's past login history. This improves collection efficiency by selecting an optimal collection method based on the user's past login history.

[0037] The image annotation system includes a collection unit that performs filtering based on a user's current activity status and areas of interest when collecting image authentication data. The collection unit performs filtering based on a user's current activity status and areas of interest when collecting image authentication data. Examples of activity status include, but are not limited to, online status and active time. Examples of areas of interest include, but are not limited to, hobbies and career-related activities. The collection unit performs relevant image authentication based on, for example, the content of a website currently viewed by the user. The collection unit can also provide interesting image authentication based on, for example, the user's areas of interest (sports, music, travel, etc.). The collection unit can also perform image authentication at an appropriate time depending on the user's current activity status (at work, on break, etc.). This improves the relevance of the collected data by filtering based on the user's current activity status and areas of interest.

[0038] The image annotation system includes a collection unit that selects an optimal collection means according to a user's input method when collecting image authentication data. The collection unit selects an optimal collection means according to a user's input method when collecting image authentication data. Input methods include, but are not limited to, voice input, text input, and image input. For example, if a user prefers voice input, the collection unit performs image authentication using voice. For example, if a user prefers text input, the collection unit can also perform image authentication using text. Furthermore, if a user prefers image input, the collection unit can also perform image authentication in the form of selecting an image. This improves collection efficiency by selecting an optimal collection means according to the user's input method.

[0039] The image annotation system includes a collection unit that, when collecting image authentication data, prioritizes collecting highly relevant data by taking into account the user's geographical location information. When collecting image authentication data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the collection unit provides image authentication related to that area. For example, if the user is traveling, the collection unit can also provide image authentication related to the user's travel destination. Furthermore, if the user is at home, the collection unit can also provide image authentication related to the area around the user's home. This improves the accuracy of the collected data by preferentially collecting highly relevant data by taking into account the user's geographical location information.

[0040] The image annotation system includes a collection unit that analyzes a user's social media activity and collects related data when collecting image authentication data. The collection unit analyzes the user's social media activity and collects related data when collecting image authentication data. Social media activity includes, but is not limited to, post content, number of likes, and the like. The collection unit provides, for example, image authentication related to locations where the user checked in on social media. The collection unit can also analyze, for example, the user's social media post content and provide related image authentication. The collection unit can also provide related image authentication by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and collecting related data, the accuracy of the collected data is improved.

[0041] The image annotation system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting image authentication data. The collection unit customizes the collection method by reflecting a user's past feedback when collecting image authentication data. Feedback includes, but is not limited to, user surveys, reviews, and the like. The collection unit selects an optimal image authentication method, for example, based on feedback previously provided by the user. The collection unit can also preferentially provide a specific image authentication method based on, for example, the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. As a result, collection efficiency is improved by customizing the collection method by reflecting the user's past feedback.

[0042] The image annotation system includes a confirmation unit that adjusts the level of detail of the confirmation based on the importance of the image data when confirming the annotation. The confirmation unit adjusts the level of detail of the confirmation based on the importance of the image data when confirming the annotation. The importance includes, but is not limited to, for example, business importance and urgency. The confirmation unit, for example, performs detailed annotation for image data with high importance. The confirmation unit can also perform simplified annotation for image data with low importance. The confirmation unit can also gradually adjust the level of detail of the annotation depending on the importance of the image data. Thus, by adjusting the level of detail of the confirmation based on the importance of the image data, the accuracy of the annotation is improved.

[0043] The image annotation system includes a determination unit that applies different determination algorithms depending on the category of image data when determining annotations. The determination unit applies different determination algorithms depending on the category of image data when determining annotations. Categories include, but are not limited to, medical images and traffic images, for example. The determination unit applies a dedicated determination algorithm to image data of traffic signs, for example. The determination unit can also apply a specialized medical determination algorithm to medical image data, for example. The determination unit can also apply a landscape-specific determination algorithm to image data of natural landscapes. In this way, by applying different determination algorithms depending on the category of image data, the accuracy of annotations is improved.

[0044] The image annotation system includes a determination unit that, when determining an annotation, improves the accuracy of the determination by referring to the user's past determination results. The determination unit improves the accuracy of the determination by referring to the user's past determination results. Past determination results include, but are not limited to, past annotation data, user history, etc. The determination unit improves the accuracy of the determination by referring to, for example, annotation results performed by the user in the past. The determination unit can also extract specific patterns from the user's past determination results and reflect them in the determination algorithm. The determination unit can also analyze the user's past determination results and gradually improve the accuracy of the determination. This improves the accuracy of the annotation by referring to the user's past determination results.

[0045] The image annotation system includes a confirmation unit that, when confirming annotations, determines a confirmation priority based on the submission time of image data. When confirming annotations, the confirmation unit determines a confirmation priority based on the submission time of image data. The submission time includes, but is not limited to, for example, the submission date and submission time. For example, the confirmation unit prioritizes annotation confirmation for image data submitted earlier. For example, the confirmation unit can also postpone annotation confirmation for image data submitted later. The confirmation unit can also gradually adjust the confirmation priority depending on the submission time of image data. Thus, by determining the confirmation priority based on the submission time of image data, annotation efficiency is improved.

[0046] The image annotation system includes a confirmation unit that adjusts the confirmation order based on the relevance of image data when confirming annotations. The confirmation unit adjusts the confirmation order based on the relevance of image data when confirming annotations. Relevance includes, but is not limited to, similarity of content and related topics, for example. The confirmation unit, for example, prioritizes annotation confirmation for image data with high relevance. The confirmation unit can also postpone annotation confirmation for image data with low relevance, for example. The confirmation unit can also gradually adjust the confirmation order according to the relevance of image data. Thus, by adjusting the confirmation order based on the relevance of image data, annotation efficiency is improved.

[0047] The image annotation system includes a confirmation unit that adjusts confirmation criteria according to a user's level of expertise when confirming an annotation. The confirmation unit adjusts confirmation criteria according to a user's level of expertise when confirming an annotation. Expertise levels include, but are not limited to, qualifications, years of experience, and the like. For example, the confirmation unit applies strict confirmation criteria when a user has a high level of expertise. For example, the confirmation unit can apply lenient confirmation criteria when a user has a low level of expertise. The confirmation unit can also gradually adjust the confirmation criteria according to the user's level of expertise. This improves the accuracy of annotations by adjusting the confirmation criteria according to the user's level of expertise.

[0048] The image annotation system includes a storage unit that optimizes a storage algorithm by referring to previously stored data when storing data. The storage unit optimizes the storage algorithm by referring to previously stored data when storing data. Previous stored data includes, but is not limited to, past backup data, log data, etc. The storage unit, for example, refers to previously stored data and selects an optimal storage algorithm. The storage unit can also extract specific patterns from previously stored data and reflect them in the storage algorithm. The storage unit can also analyze previously stored data and gradually optimize the storage algorithm. In this way, by optimizing the storage algorithm by referring to previously stored data, storage efficiency is improved.

[0049] The image annotation system includes a storage unit that updates the stored data by reflecting user feedback when the data is saved. The storage unit updates the stored data by reflecting user feedback when the data is saved. The feedback includes, but is not limited to, user surveys, reviews, and the like. The storage unit updates the stored data, for example, based on feedback provided by the user. The storage unit can also prioritize saving specific data based on user feedback, for example. The storage unit can also analyze user feedback and update the stored data to improve its quality. In this way, updating the stored data by reflecting user feedback improves the quality of the stored data.

[0050] The image annotation system includes a storage unit that adjusts the level of detail of storage based on the importance of data when storing the data. The storage unit adjusts the level of detail of storage based on the importance of data when storing the data. The importance includes, but is not limited to, business importance, urgency, etc. For example, the storage unit stores data in detail for highly important data. For example, the storage unit can also store data in a simplified manner for less important data. The storage unit can also gradually adjust the level of detail of storage depending on the importance of the data. In this way, adjusting the level of detail of storage based on the importance of the data improves the quality of the stored data.

[0051] The image annotation system includes a storage unit that weights the stored data based on the submission time of the image data when the data is saved. The storage unit weights the stored data based on the submission time of the image data when the data is saved. The submission time includes, but is not limited to, for example, the submission date and submission time. For example, the storage unit prioritizes saving image data that was submitted earlier. For example, the storage unit can also postpone saving image data that was submitted later. The storage unit can also gradually adjust the weighting of the stored data depending on the submission time of the image data. Thus, by weighting the stored data based on the submission time of the image data, the quality of the stored data is improved.

[0052] The image annotation system includes a storage unit that integrates information from different data sources to enrich the stored data when the data is stored. The storage unit integrates information from different data sources to enrich the stored data when the data is stored. Examples of different data sources include, but are not limited to, external APIs and data from other systems. The storage unit, for example, integrates information from the different data sources to enrich the stored data. The storage unit can also analyze information from the different data sources to improve the quality of the stored data. The storage unit can also refer to information from the different data sources to update the stored data. In this way, the quality of the stored data is improved by integrating information from the different data sources.

[0053] The image annotation system includes a storage unit that adjusts the order of storage based on the relevance of data when storing the data. The storage unit adjusts the order of storage based on the relevance of data when storing the data. Relevance includes, but is not limited to, similarity of content, related topics, and the like. For example, the storage unit prioritizes storing data with high relevance. For example, the storage unit can also store data with low relevance later. The storage unit can also gradually adjust the order of storage according to the relevance of data. In this way, adjusting the order of storage based on the relevance of data improves the quality of stored data.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The image annotation system can also include a confirmation unit that analyzes a user's past annotation history and dynamically adjusts the annotation confirmation criteria. For example, if a user has previously annotated with high accuracy, the system can prioritize the adoption of that user's annotation results. Also, if a user has expertise in a particular category, the system can relax the annotation confirmation criteria for that category. Furthermore, by extracting patterns from the user's annotation history and incorporating them into the confirmation algorithm, the accuracy of annotations can be improved.

[0056] The image annotation system may also include a determination unit that adjusts annotation determination criteria taking into account the user's geographical location information. For example, if the user is in a specific area, annotations for images related to that area may be prioritized. Also, if the user is traveling, annotations for images related to the travel destination may be prioritized. Furthermore, if the user is at home, annotations for images related to the area around the user's home may be prioritized. In this way, by adjusting the annotation determination criteria taking into account the user's geographical location information, the accuracy of annotations is improved.

[0057] The image annotation system may also include a collection unit that analyzes a user's social media activity and collects related data. For example, the system may provide image authentication related to the places where the user has checked in on social media. The system may also analyze the content of the user's social media posts and provide related image authentication. The system may also provide related image authentication based on the activities of the user's friends on social media. In this way, the accuracy of the collected data is improved by analyzing the user's social media activity and collecting related data.

[0058] The image annotation system may also include a collection unit that customizes the collection method by reflecting the user's past feedback. For example, the system may select an optimal image authentication method based on the user's past feedback. A specific image authentication method may be preferentially provided based on the user's past feedback. The system may also analyze the user's past feedback and improve the collection method. This allows the collection efficiency to be improved by customizing the collection method by reflecting the user's past feedback.

[0059] The image annotation system may also include a storage unit that optimizes the storage algorithm by referencing previously stored data when storing the image. For example, the system may select an optimal storage algorithm by referencing previously stored data. Specific patterns may be extracted from previously stored data and reflected in the storage algorithm. Furthermore, the system may analyze previously stored data and gradually optimize the storage algorithm. By optimizing the storage algorithm by referencing previously stored data, storage efficiency is improved.

[0060] The image annotation system may also include a storage unit that adjusts the level of detail of storage based on the importance of the data when storing the data. For example, data with high importance may be stored in detail. Data with low importance may be stored in a simplified manner. The level of detail of storage may also be adjusted in stages depending on the importance of the data. This improves the quality of stored data by adjusting the level of detail of storage based on the importance of the data.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection unit collects image authentication data from the user. For example, when the user logs in, a CAPTCHA is displayed and the user is asked to select a specific image. When the user selects the correct image, the information is recorded by the collection unit. The collection unit can also save the image data selected by the user to cloud storage. Step 2: The confirmation unit confirms the annotation based on the data collected by the collection unit. For example, it includes an algorithm that confirms the annotation when a certain number of users select the same image. It is also possible to set criteria for accepting annotations based on user recognition rates. Step 3: The storage unit stores the annotation data confirmed by the confirmation unit, for example, in a cloud storage.

[0063] (Example 2) An image annotation system according to an embodiment of the present invention collects image authentication data from users, confirms annotations, and stores them. The image annotation system uses CAPTCHAs linked to users' daily login behavior to perform image annotations. When a certain number of responses are received, the system uses the responses as annotations based on the user's recognition rate. For example, when a user logs in, a CAPTCHA is displayed for image authentication. The user is then prompted to select a specific image. For example, instructions such as "Please select an image that shows a traffic light" are displayed. When the user selects the correct image, the information is recorded as annotation data. Next, if a certain number of users select the same image, the annotation for that image is confirmed. For example, if 100 users recognize the same image as a "traffic light," the image is annotated as a "traffic light." This mechanism ensures consistency in annotations. Furthermore, the system can collect annotations from a large number of users in a short period of time. For example, if 100 million users log in and complete a 10-second CAPTCHA test, 30 years' worth of annotation data can be collected in a short period of time. This overcomes the challenges of traditional human annotation, such as high outsourcing costs, worker bias, and lack of consistency. Image annotation systems effectively address the bias issues of generative AI, enabling more accurate image generation. For example, if the data used by generative AI contains bias toward a particular race or gender, this system can annotate more diverse data and mitigate bias. This system can also be applied to other fields. For example, in the medical field, annotating image data used by doctors during diagnoses can improve the accuracy of AI diagnoses. Furthermore, in the transportation field, annotating images of road signs and traffic lights can improve the recognition accuracy of self-driving cars. Thus, image annotation systems are a groundbreaking solution to the bias issues of generative AI, and are expected to be applied in a variety of fields.

[0064] An image annotation system according to an embodiment includes a collection unit, a determination unit, and a storage unit. The collection unit collects image authentication data from a user. For example, when a user logs in, the collection unit displays a CAPTCHA prompting the user to select a specific image. For example, an instruction such as "Please select an image that shows a traffic light" is displayed. When the user selects the correct image, the collection unit records the information. For example, the collection unit can also store the image data selected by the user in cloud storage. For example, the collection unit uploads the image data selected by the user to cloud storage to facilitate data management. The determination unit determines the annotation based on the data collected by the collection unit. For example, the determination unit includes an algorithm that determines the annotation when a certain number of users select the same image. For example, if 100 users recognize the same image as a "traffic light," the image is annotated as a "traffic light." The determination unit can also set criteria for accepting annotations based on user recognition rates. For example, the annotation is determined when 80% or more users select the same image. The storage unit stores the annotation data confirmed by the confirmation unit. The storage unit can, for example, store the confirmed annotation data in cloud storage. For example, the storage unit can upload the confirmed annotation data to the cloud storage to facilitate data management. This allows the image annotation system according to the embodiment to efficiently collect, confirm, and store image authentication data from users.

[0065] The image annotation system includes a collection unit that stores image data selected by a user in cloud storage. The collection unit stores the image data selected by the user in the cloud storage. Examples of cloud storage include, but are not limited to, AWS S3 and Google Cloud Storage. The collection unit, for example, uploads the image data selected by the user to the cloud storage, facilitating data management. For example, the collection unit can also store the image data selected by the user in the cloud storage in real time. This facilitates data management by storing the image data selected by the user in the cloud storage.

[0066] The image annotation system includes a confirmation unit including an algorithm that confirms an annotation when a certain number of users or more select the same image. The confirmation unit includes an algorithm that confirms an annotation when a certain number of users or more select the same image. Criteria for a certain number or more include, but are not limited to, 10 or more users, 50% or more, etc. For example, if 100 users recognize the same image as a "traffic light," the confirmation unit annotates the image as a "traffic light." The confirmation unit can also confirm the annotation using, for example, a machine learning algorithm or a rule-based algorithm. This ensures consistency of the annotation by confirming the annotation when a certain number of users or more select the same image.

[0067] The image annotation system includes a storage unit that stores the confirmed annotation data in cloud storage. The storage unit stores the confirmed annotation data in the cloud storage. Examples of cloud storage include, but are not limited to, AWS S3 and Google Cloud Storage. The storage unit, for example, uploads the confirmed annotation data to the cloud storage to facilitate data management. For example, the storage unit can store the confirmed annotation data in the cloud storage in real time. This facilitates data management by storing the confirmed annotation data in the cloud storage.

[0068] The image annotation system includes a determination unit that sets an annotation adoption criterion based on a user's recognition ratio. The determination unit sets the annotation adoption criterion based on the user's recognition ratio. The recognition ratio includes, but is not limited to, a recognition rate of 80% or more. The determination unit determines the annotation when, for example, 80% or more users select the same image. The determination unit can also calculate the recognition ratio using, for example, a machine learning algorithm or a rule-based algorithm and set the annotation adoption criterion. By setting the annotation adoption criterion based on the user's recognition ratio, the accuracy of the annotation is improved.

[0069] The image annotation system includes a collection unit that collects image data in the medical field or the traffic field. The collection unit collects image data in the medical field or the traffic field. The medical field includes, for example, but is not limited to, radiological images and pathological images. The traffic field includes, for example, but is not limited to, images from traffic surveillance cameras and vehicle license plate images. The collection unit can collect image data in the medical field, for example, to improve the accuracy of AI-based diagnoses. The collection unit can also collect image data in the traffic field to improve the recognition accuracy of self-driving cars. As a result, by collecting image data in the medical field or the traffic field, the accuracy of annotation in a specific field is improved.

[0070] The image annotation system includes a determination unit that determines annotations in the medical field and the traffic field. The determination unit determines the annotations in the medical field and the traffic field. The medical field includes, for example, but is not limited to, radiological images and pathological images. The traffic field includes, for example, but is not limited to, images from traffic surveillance cameras and vehicle license plate images. The determination unit, for example, determines annotations in the medical field to improve the accuracy of AI-based diagnoses. Furthermore, the determination unit determines annotations in the traffic field to improve the recognition accuracy of self-driving vehicles. As a result, by determining annotations in the medical field and the traffic field, the accuracy of annotations in specific fields is improved.

[0071] The image annotation system includes a collection unit that estimates a user's emotion and adjusts the timing of collecting image authentication data based on the estimated user emotion. The collection unit estimates the user's emotion and adjusts the timing of collecting image authentication data based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is stressed, the collection unit may not perform image authentication immediately after logging in, but may wait a certain amount of time before starting collection. For example, if the user is relaxed, the collection unit may perform image authentication immediately after logging in, allowing for smooth collection. Furthermore, if the user is in a hurry, the collection unit may simplify image authentication and complete collection in a short time. This reduces the burden on the user by adjusting the collection timing according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0072] The image annotation system includes a collection unit that analyzes a user's past login history and selects an optimal collection method. The collection unit analyzes the user's past login history and selects an optimal collection method. Examples of optimal collection methods include, but are not limited to, image capture and screenshots. The collection unit, for example, performs image authentication based on a time period in which the user frequently logged in in the past. The collection unit can also select an optimal collection method (audio, text, image, etc.) based on, for example, a device used by the user in the past. The collection unit can also concentrate collection on specific days of the week or time periods based on the user's past login history. This improves collection efficiency by selecting an optimal collection method based on the user's past login history.

[0073] The image annotation system includes a collection unit that performs filtering based on a user's current activity status and areas of interest when collecting image authentication data. The collection unit performs filtering based on a user's current activity status and areas of interest when collecting image authentication data. Examples of activity status include, but are not limited to, online status and active time. Examples of areas of interest include, but are not limited to, hobbies and career-related activities. The collection unit performs relevant image authentication based on, for example, the content of a website currently viewed by the user. The collection unit can also provide interesting image authentication based on, for example, the user's areas of interest (sports, music, travel, etc.). The collection unit can also perform image authentication at an appropriate time depending on the user's current activity status (at work, on break, etc.). This improves the relevance of the collected data by filtering based on the user's current activity status and areas of interest.

[0074] The image annotation system includes a collection unit that selects an optimal collection means according to a user's input method when collecting image authentication data. The collection unit selects an optimal collection means according to a user's input method when collecting image authentication data. Input methods include, but are not limited to, voice input, text input, and image input. For example, if a user prefers voice input, the collection unit performs image authentication using voice. For example, if a user prefers text input, the collection unit can also perform image authentication using text. Furthermore, if a user prefers image input, the collection unit can also perform image authentication in the form of selecting an image. This improves collection efficiency by selecting an optimal collection means according to the user's input method.

[0075] The image annotation system includes a collection unit that estimates a user's emotion and prioritizes image data to be collected based on the estimated user emotion. The collection unit estimates the user's emotion and prioritizes image data to be collected based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, and anger. For example, if the user is stressed, the collection unit may prioritize simple image authentication. For example, if the user is relaxed, the collection unit may also prioritize detailed image authentication. Furthermore, if the user is in a hurry, the collection unit may prioritize image authentication that can be completed in a short time. This improves collection efficiency by prioritizing image data to be collected based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The image annotation system includes a collection unit that, when collecting image authentication data, prioritizes collecting highly relevant data by taking into account the user's geographical location information. When collecting image authentication data, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, if the user is in a specific area, the collection unit provides image authentication related to that area. For example, if the user is traveling, the collection unit can also provide image authentication related to the user's travel destination. Furthermore, if the user is at home, the collection unit can also provide image authentication related to the area around the user's home. This improves the accuracy of the collected data by preferentially collecting highly relevant data by taking into account the user's geographical location information.

[0077] The image annotation system includes a collection unit that analyzes a user's social media activity and collects related data when collecting image authentication data. The collection unit analyzes the user's social media activity and collects related data when collecting image authentication data. Social media activity includes, but is not limited to, post content, number of likes, and the like. The collection unit provides, for example, image authentication related to locations where the user checked in on social media. The collection unit can also analyze, for example, the user's social media post content and provide related image authentication. The collection unit can also provide related image authentication by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity and collecting related data, the accuracy of the collected data is improved.

[0078] The image annotation system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting image authentication data. The collection unit customizes the collection method by reflecting a user's past feedback when collecting image authentication data. Feedback includes, but is not limited to, user surveys, reviews, and the like. The collection unit selects an optimal image authentication method, for example, based on feedback previously provided by the user. The collection unit can also preferentially provide a specific image authentication method based on, for example, the user's past feedback. The collection unit can also analyze the user's past feedback and improve the collection method. As a result, collection efficiency is improved by customizing the collection method by reflecting the user's past feedback.

[0079] The image annotation system includes a determination unit that estimates a user's emotion and adjusts annotation confirmation criteria based on the estimated user emotion. The determination unit estimates the user's emotion and adjusts the annotation confirmation criteria based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, and anger. For example, the determination unit relaxes the annotation confirmation criteria when the user is stressed. For example, the determination unit can tighten the annotation confirmation criteria when the user is relaxed. The determination unit can also set criteria for quickly confirming annotations when the user is in a hurry. This improves annotation accuracy by adjusting the annotation confirmation criteria according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, 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.

[0080] The image annotation system includes a confirmation unit that adjusts the level of detail of the confirmation based on the importance of the image data when confirming the annotation. The confirmation unit adjusts the level of detail of the confirmation based on the importance of the image data when confirming the annotation. The importance includes, but is not limited to, for example, business importance and urgency. The confirmation unit, for example, performs detailed annotation for image data with high importance. The confirmation unit can also perform simplified annotation for image data with low importance. The confirmation unit can also gradually adjust the level of detail of the annotation depending on the importance of the image data. Thus, by adjusting the level of detail of the confirmation based on the importance of the image data, the accuracy of the annotation is improved.

[0081] The image annotation system includes a determination unit that applies different determination algorithms depending on the category of image data when determining annotations. The determination unit applies different determination algorithms depending on the category of image data when determining annotations. Categories include, but are not limited to, medical images and traffic images, for example. The determination unit applies a dedicated determination algorithm to image data of traffic signs, for example. The determination unit can also apply a specialized medical determination algorithm to medical image data, for example. The determination unit can also apply a landscape-specific determination algorithm to image data of natural landscapes. In this way, by applying different determination algorithms depending on the category of image data, the accuracy of annotations is improved.

[0082] The image annotation system includes a determination unit that, when determining an annotation, improves the accuracy of the determination by referring to the user's past determination results. The determination unit improves the accuracy of the determination by referring to the user's past determination results. Past determination results include, but are not limited to, past annotation data, user history, etc. The determination unit improves the accuracy of the determination by referring to, for example, annotation results performed by the user in the past. The determination unit can also extract specific patterns from the user's past determination results and reflect them in the determination algorithm. The determination unit can also analyze the user's past determination results and gradually improve the accuracy of the determination. This improves the accuracy of the annotation by referring to the user's past determination results.

[0083] The image annotation system includes a determination unit that estimates a user's emotion and adjusts the timing of annotation confirmation based on the estimated user emotion. The determination unit estimates the user's emotion and adjusts the timing of annotation confirmation based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the determination unit delays the timing of annotation confirmation when the user is stressed. For example, the determination unit can also accelerate the timing of annotation confirmation when the user is relaxed. The determination unit can also quickly set the timing of annotation confirmation when the user is in a hurry. This improves annotation accuracy by adjusting the timing of annotation confirmation according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0084] The image annotation system includes a confirmation unit that, when confirming annotations, determines a confirmation priority based on the submission time of image data. When confirming annotations, the confirmation unit determines a confirmation priority based on the submission time of image data. The submission time includes, but is not limited to, for example, the submission date and submission time. For example, the confirmation unit prioritizes annotation confirmation for image data submitted earlier. For example, the confirmation unit can also postpone annotation confirmation for image data submitted later. The confirmation unit can also gradually adjust the confirmation priority depending on the submission time of image data. Thus, by determining the confirmation priority based on the submission time of image data, annotation efficiency is improved.

[0085] The image annotation system includes a confirmation unit that adjusts the confirmation order based on the relevance of image data when confirming annotations. The confirmation unit adjusts the confirmation order based on the relevance of image data when confirming annotations. Relevance includes, but is not limited to, similarity of content and related topics, for example. The confirmation unit, for example, prioritizes annotation confirmation for image data with high relevance. The confirmation unit can also postpone annotation confirmation for image data with low relevance, for example. The confirmation unit can also gradually adjust the confirmation order according to the relevance of image data. Thus, by adjusting the confirmation order based on the relevance of image data, annotation efficiency is improved.

[0086] The image annotation system includes a confirmation unit that adjusts confirmation criteria according to a user's level of expertise when confirming an annotation. The confirmation unit adjusts confirmation criteria according to a user's level of expertise when confirming an annotation. Expertise levels include, but are not limited to, qualifications, years of experience, and the like. For example, the confirmation unit applies strict confirmation criteria when a user has a high level of expertise. For example, the confirmation unit can apply lenient confirmation criteria when a user has a low level of expertise. The confirmation unit can also gradually adjust the confirmation criteria according to the user's level of expertise. This improves the accuracy of annotations by adjusting the confirmation criteria according to the user's level of expertise.

[0087] The image annotation system includes a storage unit that estimates a user's emotion and selects data to be saved based on the estimated user emotion. The storage unit estimates the user's emotion and selects data to be saved based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, and anger. For example, when the user is stressed, the storage unit prioritizes saving data of low importance. For example, when the user is relaxed, the storage unit can also prioritize saving data of high importance. Furthermore, when the user is in a hurry, the storage unit can prioritize saving data that can be saved quickly. This improves the quality of saved data by selecting data to be saved based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0088] The image annotation system includes a storage unit that optimizes a storage algorithm by referring to previously stored data when storing data. The storage unit optimizes the storage algorithm by referring to previously stored data when storing data. Previous stored data includes, but is not limited to, past backup data, log data, etc. The storage unit, for example, refers to previously stored data and selects an optimal storage algorithm. The storage unit can also extract specific patterns from previously stored data and reflect them in the storage algorithm. The storage unit can also analyze previously stored data and gradually optimize the storage algorithm. In this way, by optimizing the storage algorithm by referring to previously stored data, storage efficiency is improved.

[0089] The image annotation system includes a storage unit that updates the stored data by reflecting user feedback when the data is saved. The storage unit updates the stored data by reflecting user feedback when the data is saved. The feedback includes, but is not limited to, user surveys, reviews, and the like. The storage unit updates the stored data, for example, based on feedback provided by the user. The storage unit can also prioritize saving specific data based on user feedback, for example. The storage unit can also analyze user feedback and update the stored data to improve its quality. In this way, updating the stored data by reflecting user feedback improves the quality of the stored data.

[0090] The image annotation system includes a storage unit that adjusts the level of detail of storage based on the importance of data when storing the data. The storage unit adjusts the level of detail of storage based on the importance of data when storing the data. The importance includes, but is not limited to, business importance, urgency, etc. For example, the storage unit stores data in detail for highly important data. For example, the storage unit can also store data in a simplified manner for less important data. The storage unit can also gradually adjust the level of detail of storage depending on the importance of the data. In this way, adjusting the level of detail of storage based on the importance of the data improves the quality of the stored data.

[0091] The image annotation system includes a storage unit that estimates a user's emotion and adjusts the frequency of saving based on the estimated user emotion. The storage unit estimates the user's emotion and adjusts the frequency of saving based on the estimated user emotion. Emotions include, but are not limited to, joy, sadness, anger, etc. For example, the storage unit reduces the frequency of saving when the user is stressed. For example, the storage unit can increase the frequency of saving when the user is relaxed. The storage unit can also set a frequency of quick saving when the user is in a hurry. This improves the quality of saved data by adjusting the frequency of saving according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0092] The image annotation system includes a storage unit that weights the stored data based on the submission time of the image data when the data is saved. The storage unit weights the stored data based on the submission time of the image data when the data is saved. The submission time includes, but is not limited to, for example, the submission date and submission time. For example, the storage unit prioritizes saving image data that was submitted earlier. For example, the storage unit can also postpone saving image data that was submitted later. The storage unit can also gradually adjust the weighting of the stored data depending on the submission time of the image data. Thus, by weighting the stored data based on the submission time of the image data, the quality of the stored data is improved.

[0093] The image annotation system includes a storage unit that integrates information from different data sources to enrich the stored data when the data is stored. The storage unit integrates information from different data sources to enrich the stored data when the data is stored. Examples of different data sources include, but are not limited to, external APIs and data from other systems. The storage unit, for example, integrates information from the different data sources to enrich the stored data. The storage unit can also analyze information from the different data sources to improve the quality of the stored data. The storage unit can also refer to information from the different data sources to update the stored data. In this way, the quality of the stored data is improved by integrating information from the different data sources.

[0094] The image annotation system includes a storage unit that adjusts the order of storage based on the relevance of data when storing the data. The storage unit adjusts the order of storage based on the relevance of data when storing the data. Relevance includes, but is not limited to, similarity of content, related topics, and the like. For example, the storage unit prioritizes storing data with high relevance. For example, the storage unit can also store data with low relevance later. The storage unit can also gradually adjust the order of storage according to the relevance of data. In this way, adjusting the order of storage based on the relevance of data improves the quality of stored data. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, determination unit, and storage unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and displays a CAPTCHA when a user logs in and requests the user to select a specific image. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the annotation based on the collected data. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the determined annotation data in cloud storage. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, determination unit, and storage unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and displays a CAPTCHA when a user logs in and requests the user to select a specific image. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the annotation based on the collected data. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and stores the determined annotation data in cloud storage. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, determination unit, and storage unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314, and displays a CAPTCHA when the user logs in and requests the user to select a specific image. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the annotation based on the collected data. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the determined annotation data in cloud storage. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, determination unit, and storage unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and displays a CAPTCHA when a user logs in and requests the user to select a specific image. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the annotation based on the collected data. The storage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and stores the determined annotation data in cloud storage.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The image annotation system can also include a confirmation unit that analyzes a user's past annotation history and dynamically adjusts the annotation confirmation criteria. For example, if a user has previously annotated with high accuracy, the system can prioritize the adoption of that user's annotation results. Also, if a user has expertise in a particular category, the system can relax the annotation confirmation criteria for that category. Furthermore, by extracting patterns from the user's annotation history and incorporating them into the confirmation algorithm, the accuracy of annotations can be improved.

[0097] The image annotation system may also include a confirmation unit that estimates the user's emotions and adjusts annotation confirmation criteria based on the estimated user emotions. For example, if the user is stressed, the annotation confirmation criteria may be relaxed. If the user is relaxed, the annotation confirmation criteria may be tightened. Also, if the user is in a hurry, criteria for quickly confirming annotations may be set. In this way, by adjusting the annotation confirmation criteria according to the user's emotions, the accuracy of annotations is improved.

[0098] The image annotation system may also include a determination unit that adjusts annotation determination criteria taking into account the user's geographical location information. For example, if the user is in a specific area, annotations for images related to that area may be prioritized. Also, if the user is traveling, annotations for images related to the travel destination may be prioritized. Furthermore, if the user is at home, annotations for images related to the area around the user's home may be prioritized. In this way, by adjusting the annotation determination criteria taking into account the user's geographical location information, the accuracy of annotations is improved.

[0099] The image annotation system may also include a collection unit that analyzes a user's social media activity and collects related data. For example, the system may provide image authentication related to the places where the user has checked in on social media. The system may also analyze the content of the user's social media posts and provide related image authentication. The system may also provide related image authentication based on the activities of the user's friends on social media. In this way, the accuracy of the collected data is improved by analyzing the user's social media activity and collecting related data.

[0100] The image annotation system may also include a collection unit that estimates the user's emotions and determines the priority of image data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, simple image authentication may be provided preferentially. If the user is relaxed, detailed image authentication may be provided preferentially. Also, if the user is in a hurry, image authentication that can be completed in a short time may be provided preferentially. In this way, by determining the priority of image data to be collected according to the user's emotions, collection efficiency is improved.

[0101] The image annotation system may also include a collection unit that customizes the collection method by reflecting the user's past feedback. For example, the system may select an optimal image authentication method based on the user's past feedback. A specific image authentication method may be preferentially provided based on the user's past feedback. The system may also analyze the user's past feedback and improve the collection method. This allows the collection efficiency to be improved by customizing the collection method by reflecting the user's past feedback.

[0102] The image annotation system may also include a storage unit that estimates the user's emotions and selects data to be saved based on the estimated user emotions. For example, if the user is feeling stressed, data of low importance may be saved with priority. If the user is relaxed, data of high importance may be saved with priority. Also, if the user is in a hurry, data that can be saved quickly may be saved with priority. In this way, the quality of saved data is improved by selecting data to be saved based on the user's emotions.

[0103] The image annotation system may also include a storage unit that optimizes the storage algorithm by referencing previously stored data when storing the image. For example, the system may select an optimal storage algorithm by referencing previously stored data. Specific patterns may be extracted from previously stored data and reflected in the storage algorithm. Furthermore, the system may analyze previously stored data and gradually optimize the storage algorithm. By optimizing the storage algorithm by referencing previously stored data, storage efficiency is improved.

[0104] The image annotation system may also include a storage unit that adjusts the level of detail of storage based on the importance of the data when storing the data. For example, data with high importance may be stored in detail. Data with low importance may be stored in a simplified manner. The level of detail of storage may also be adjusted in stages depending on the importance of the data. This improves the quality of stored data by adjusting the level of detail of storage based on the importance of the data.

[0105] The image annotation system may also include a storage unit that estimates the user's emotions and adjusts the frequency of saving based on the estimated user emotions. For example, if the user is stressed, the frequency of saving may be reduced. If the user is relaxed, the frequency of saving may be increased. Also, if the user is in a hurry, the frequency of quick saving may be set. In this way, adjusting the frequency of saving according to the user's emotions improves the quality of saved data.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The collection unit collects image authentication data from the user. For example, when the user logs in, a CAPTCHA is displayed and the user is asked to select a specific image. When the user selects the correct image, the information is recorded by the collection unit. The collection unit can also save the image data selected by the user to cloud storage. Step 2: The confirmation unit confirms the annotation based on the data collected by the collection unit. For example, it includes an algorithm that confirms the annotation when a certain number of users select the same image. It is also possible to set criteria for accepting annotations based on user recognition rates. Step 3: The storage unit stores the annotation data confirmed by the confirmation unit, for example, in a cloud storage.

[0108] 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.

[0109] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 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 collection unit that collects image authentication data from users; a determination unit that determines annotations based on the data collected by the collection unit; a storage unit for storing the annotation data confirmed by the confirmation unit. A system characterized by:

2. The collecting unit Save user-selected image data in cloud storage 2. The system of claim 1.

3. The determination unit Includes an algorithm to confirm annotations when a certain number of users select the same image.

2. The system of claim 1.

4. The storage unit Save the finalized annotation data to cloud storage 2. The system of claim 1.

5. The determination unit Set annotation adoption criteria based on user recognition ratios 2. The system of claim 1.

6. The collecting unit Collecting image data in the medical or transportation fields 2. The system of claim 1.

7. The determination unit Finalizing annotations in medical and transportation domains 2. The system of claim 1.

8. The collecting unit The system estimates user emotions and adjusts the timing of image authentication data collection based on the estimated user emotions.

2. The system of claim 1.

Images