system

Abstract

The system according to the embodiment aims to provide feedback based on the user's recognition rate using CAPTCHA. [Solution] A system according to an embodiment includes a selection unit, a calculation unit, a provision unit, and a presentation unit. The selection unit selects a type of CAPTCHA. The calculation unit calculates a user's recognition ratio based on the CAPTCHA selected by the selection unit. The provision unit provides feedback based on the recognition ratio calculated by the calculation unit. The presentation unit presents the feedback provided by the provision unit to the user.

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 do not fully utilize CAPTCHA-based feedback systems, and there is room for improvement.

[0005] The system according to the embodiment aims to provide feedback based on the user's recognition rate using CAPTCHA. [Means for solving the problem]

[0006] A system according to an embodiment includes a selection unit, a calculation unit, a provision unit, and a presentation unit. The selection unit selects a type of CAPTCHA. The calculation unit calculates a user's recognition ratio based on the CAPTCHA selected by the selection unit. The provision unit provides feedback based on the recognition ratio calculated by the calculation unit. The presentation unit presents the feedback provided by the provision unit to the user. [Effects of the Invention]

[0007] An embodiment of the system can utilize CAPTCHA to provide feedback based on the user's recognition rate. [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 feedback system according to an embodiment of the present invention utilizes CAPTCHAs linked to a user's daily login behavior. When a user logs in, the system displays a CAPTCHA and allows the user to select a type, such as image authentication or character authentication. The system then calculates the user's recognition rate based on the user's accuracy rate, response time, past response history, and other factors. The feedback content may include customized information that reflects the user's behavioral patterns and preferences. For example, suggestions for products and services that may interest the user or suggestions for enhanced login security may be considered. CAPTCHA responses are automatically collected when the user logs in, and if a certain number of responses are obtained, they are adopted as feedback based on the user's recognition rate. This mechanism provides feedback based on the user's recognition rate, improving the user experience. For example, the CAPTCHA displayed when a user logs in can be selected from image authentication and character authentication. If the user selects image authentication, the system records the accuracy rate and response time of the image selected by the user and calculates the recognition rate. The system then suggests products and services that the user may be interested in based on the user's recognition rate. For example, customized information may be provided based on the user's past interest in the product or service. The system also suggests ways to strengthen security when users log in, such as introducing two-factor authentication or strengthening passwords. This allows users to receive feedback based on their behavioral patterns and preferences, improving security when logging in. This allows the image feedback system to provide feedback based on the user's recognition rate, improving the user experience.

[0029] An image feedback system according to an embodiment includes a selection unit, a calculation unit, a providing unit, and a presentation unit. The selection unit selects a type of CAPTCHA. For example, the selection unit can select a type of CAPTCHA, such as image authentication or character authentication. The selection unit selects a type of CAPTCHA to be displayed when a user logs in. For example, if a user selects image authentication, the selection unit displays image authentication. Furthermore, if a user selects character authentication, the selection unit can also display character authentication. The calculation unit calculates a user's recognition ratio. For example, the calculation unit calculates the recognition ratio based on the user's correct answer rate, response time, and past response history. The calculation unit records the correct answer rate and response time of the CAPTCHA displayed when the user logs in and calculates the recognition ratio. For example, if a user selects image authentication, the calculation unit records the correct answer rate and response time of the image authentication and calculates the recognition ratio. Furthermore, if a user selects character authentication, the calculation unit can also record the correct answer rate and response time of the character authentication and calculate the recognition ratio. The providing unit provides feedback based on the user's recognition ratio. For example, the providing unit provides customized information that reflects the user's behavioral patterns and preferences. The providing unit suggests products and services that the user may be interested in. For example, the providing unit provides customized information based on products and services in which the user has shown interest in the past. The providing unit also suggests strengthening security when the user logs in. For example, the providing unit suggests introducing two-factor authentication or strengthening passwords. The presentation unit presents the feedback provided by the providing unit to the user. For example, the presentation unit presents feedback based on responses automatically collected when the user logs in. The presentation unit collects responses to a CAPTCHA that is displayed when the user logs in and presents the feedback. For example, if the user selects image authentication, the presentation unit collects the image authentication response and presents the feedback. Also, if the user selects character authentication, the presentation unit can collect the character authentication response and present the feedback.As a result, the image feedback system according to the embodiment can provide feedback based on the user's recognition ratio, improving the user experience.

[0030] The selection unit can select at least one type of CAPTCHA from image authentication and character authentication. The selection unit can select, for example, image authentication. Image authentication is performed by having the user select a specific image from displayed images. For example, the selection unit can instruct the user to select an image of a car from displayed images. The selection unit can also select character authentication. Character authentication is performed by having the user input displayed characters. For example, the selection unit can instruct the user to input displayed characters. This allows the selection unit to select a type of CAPTCHA according to the user's preferences. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past CAPTCHA selection history into AI and select the optimal type of CAPTCHA.

[0031] The calculation unit can calculate the recognition ratio based on the user's accuracy rate, response time, and past response history. The calculation unit calculates the recognition ratio based on, for example, the user's accuracy rate. The accuracy rate is the number of correct answers to CAPTCHAs answered by the user divided by the total number of answers. For example, if a user answers 10 CAPTCHAs and answers 8 of them correctly, the calculation unit calculates the accuracy rate as 80%. The calculation unit can also calculate the recognition ratio based on the user's response time. The response time is the time it takes the user to answer the CAPTCHA. For example, if it takes the user an average of 5 seconds to answer a CAPTCHA, the calculation unit calculates the response time as 5 seconds. The calculation unit can also calculate the recognition ratio based on the user's past response history. The past response history includes the content of CAPTCHAs the user has answered in the past and the date and time of the answers. For example, the calculation unit calculates the recognition ratio based on the accuracy rate and response time of CAPTCHAs the user has answered in the past. This allows the calculation unit to accurately calculate the user's recognition ratio. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input the user's past response history into AI and have the AI ​​calculate the recognition ratio.

[0032] The providing unit can provide customized information that reflects the user's behavioral patterns and preferences. The providing unit provides customized information based on, for example, the user's behavioral patterns. The behavioral patterns include the user's operations on a website, the frequency of clicks, and page transition patterns. For example, the providing unit analyzes links and pages frequently clicked by the user and provides information that is likely to interest the user. The providing unit can also provide customized information based on the user's preferences. Preferences include products and services selected by the user in the past and survey results. For example, the providing unit provides customized information based on products and services purchased by the user in the past. This allows the providing unit to provide customized information to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's behavioral patterns and preferences into AI and have the AI ​​provide customized information.

[0033] The providing unit can suggest products or services that the user may be interested in. The providing unit makes suggestions based on, for example, products or services in which the user has shown interest in the past. Products or services that the user may be interested in include products or services the user has previously purchased, browsing history, etc. For example, the providing unit suggests products related to products the user has previously purchased. The providing unit can also make suggestions based on products or services the user has previously viewed. For example, the providing unit suggests products related to products the user has previously viewed. This allows the providing unit to suggest products or services based on the user's interests. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history or browsing history into AI and cause the AI ​​to suggest products or services that the user may be interested in.

[0034] The providing unit can make suggestions for strengthening security at the time of login. The providing unit, for example, suggests the introduction of two-factor authentication. Two-factor authentication is a method for strengthening security by using another authentication method in addition to a password when a user logs in. For example, the providing unit suggests to the user that they enter an authentication code sent to their smartphone in addition to their password. The providing unit can also suggest strengthening the password. Strengthening the password is a method for suggesting to the user that they set a more complex and strong password when logging in. For example, the providing unit suggests to the user that they set a strong password that combines alphanumeric characters and symbols. This allows the providing unit to strengthen security at the time of user login. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to execute the suggestion for strengthening security at the time of user login.

[0035] The presentation unit can present feedback based on a response automatically collected when the user logs in. The presentation unit, for example, collects a response to a CAPTCHA displayed when the user logs in and presents the feedback. The automatically collected response may be the content of the user's answer to the CAPTCHA and the time it took to answer. For example, if the user selects image authentication, the presentation unit collects a response to the image authentication and presents the feedback. Furthermore, if the user selects character authentication, the presentation unit can also collect a response to the character authentication and present the feedback. This allows the presentation unit to provide feedback based on the user's response at the time of login. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's response at the time of login into AI and have the AI ​​present the feedback.

[0036] The selection unit can analyze the user's past CAPTCHA selection history and automatically select the most appropriate type of CAPTCHA. For example, if the user has previously preferred image authentication, the selection unit can prioritize providing image authentication. The past CAPTCHA selection history includes the types of CAPTCHAs selected by the user in the past and the date and time of selection. For example, the selection unit can analyze the number of times the user has previously selected image authentication and the date and time, and prioritize providing image authentication. The selection unit can also prioritize character authentication if the user has frequently selected character authentication in the past. For example, the selection unit can analyze the number of times the user has previously selected character authentication and the date and time, and prioritize providing character authentication. This allows the selection unit to provide the most appropriate CAPTCHA based on the user's past selection history. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's past CAPTCHA selection history into AI and select the most appropriate type of CAPTCHA.

[0037] When selecting a CAPTCHA, the selection unit can select the optimal type of CAPTCHA by taking into consideration the user's device information. For example, if the user is using a smartphone, the selection unit provides image authentication suitable for touch operation. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the selection unit provides image authentication suitable for touch operation. Furthermore, if the user is using a desktop, the selection unit can also provide character authentication suitable for keyboard input. For example, if the user is using a desktop, the selection unit provides character authentication suitable for keyboard input. This allows the selection unit to provide the optimal CAPTCHA based on the user's device information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's device information into AI to select the optimal type of CAPTCHA.

[0038] When selecting a CAPTCHA, the selection unit can prioritize selecting a highly relevant CAPTCHA by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the selection unit provides image authentication related to that area. Geographical location information may be the user's IP address or GPS data. For example, if the user is in a specific area, the selection unit provides image authentication related to that area. Furthermore, if the user is traveling, the selection unit can also provide a CAPTCHA related to the user's travel destination. For example, if the user is traveling, the selection unit provides a CAPTCHA related to the user's travel destination. This allows the selection unit to provide a highly relevant CAPTCHA based on the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input the user's geographical location information into AI and cause the AI ​​to select a highly relevant CAPTCHA.

[0039] When selecting a CAPTCHA, the selection unit can analyze the user's social media activity and select a relevant CAPTCHA. For example, the selection unit can provide a CAPTCHA related to images recently shared by the user on social media. Social media activity can include the content of posts made by the user on social media and the number of likes. For example, the selection unit can provide a CAPTCHA related to images recently shared by the user on social media. The selection unit can also provide a CAPTCHA related to topics in which the user has shown interest on social media. For example, the selection unit can provide a CAPTCHA related to topics in which the user has shown interest on social media. This allows the selection unit to provide a relevant CAPTCHA based on the user's social media activity. Some or all of the above-described processing by the selection unit can be performed using, or without, AI. For example, the selection unit can input the user's social media activity into AI and have the AI ​​select a relevant CAPTCHA.

[0040] When calculating the recognition ratio, the calculation unit can optimize the calculation algorithm by referring to the user's past answer history. For example, if the user has a high accuracy rate in the past, the calculation unit calculates the recognition ratio based on that data. The past answer history includes the content of CAPTCHAs the user has answered in the past and the date and time of the answers. For example, if the user has a high accuracy rate in the past, the calculation unit calculates the recognition ratio based on that data. Furthermore, if the user has a low accuracy rate for specific CAPTCHAs in the past, the calculation unit can also calculate the recognition ratio based on that data. For example, if the user has a low accuracy rate for specific CAPTCHAs in the past, the calculation unit calculates the recognition ratio based on that data. This allows the calculation unit to optimize the recognition ratio calculation algorithm based on the user's past answer history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input the user's past answer history into AI and cause the AI ​​to optimize the recognition ratio calculation algorithm.

[0041] The calculation unit can improve the accuracy of the calculation when calculating the recognition ratio by taking into account device information of the user. For example, if the user is using a smartphone, the calculation unit calculates the recognition ratio by taking into account touch operation data. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the calculation unit calculates the recognition ratio by taking into account touch operation data. Furthermore, if the user is using a desktop computer, the calculation unit can also calculate the recognition ratio by taking into account keyboard input data. For example, if the user is using a desktop computer, the calculation unit calculates the recognition ratio by taking into account keyboard input data. This allows the calculation unit to improve the accuracy of the calculation of the recognition ratio based on the user's device information. Some or all of the above-described processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input the user's device information into AI and have the AI ​​calculate the recognition ratio.

[0042] The calculation unit can improve the accuracy of the calculation when calculating the recognition ratio by taking into account the user's geographical location information. For example, if the user is in a specific area, the calculation unit calculates the recognition ratio by taking into account data for that area. The geographical location information may be the user's IP address or GPS data. For example, if the user is in a specific area, the calculation unit calculates the recognition ratio by taking into account data for that area. Furthermore, if the user is traveling, the calculation unit can also calculate the recognition ratio by taking into account data for the travel destination. For example, if the user is traveling, the calculation unit calculates the recognition ratio by taking into account data for the travel destination. This allows the calculation unit to improve the accuracy of the calculation of the recognition ratio based on the user's geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input the user's geographical location information into AI and cause the AI ​​to calculate the recognition ratio.

[0043] The calculation unit can analyze the user's social media activity when calculating the recognition ratio to improve the accuracy of the calculation. For example, the calculation unit calculates the recognition ratio by taking into account information recently shared by the user on social media. Social media activity includes the content of posts made by the user on social media and the number of likes. For example, the calculation unit calculates the recognition ratio by taking into account information recently shared by the user on social media. The calculation unit can also calculate the recognition ratio by taking into account topics in which the user has shown interest on social media. For example, the calculation unit calculates the recognition ratio by taking into account topics in which the user has shown interest on social media. This allows the calculation unit to improve the accuracy of the recognition ratio calculation based on the user's social media activity. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's social media activity into AI and have the AI ​​calculate the recognition ratio.

[0044] When providing feedback, the providing unit can select optimal feedback by analyzing the user's past behavioral patterns. The providing unit provides feedback based on, for example, products or services in which the user has shown interest in the past. The behavioral patterns include the user's operations on a website, frequency of clicks, and page transition patterns. For example, the providing unit provides feedback based on the user's past interest in products or services. The providing unit can also provide feedback at optimal timing based on the user's past behavioral patterns. For example, if the user has tended to visit websites during a specific time period in the past, the providing unit provides feedback during that time period. This allows the providing unit to provide optimal feedback based on the user's past behavioral patterns. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past behavioral patterns into AI and have the AI ​​select optimal feedback.

[0045] When providing feedback, the providing unit can customize the content of the feedback by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides feedback tailored to the screen size. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the providing unit provides feedback tailored to the screen size. Furthermore, if the user is using a desktop, the providing unit can also provide feedback optimized for a large screen. For example, if the user is using a desktop, the providing unit provides feedback optimized for a large screen. This allows the providing unit to customize the content of the feedback based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's device information into AI and cause the AI ​​to customize the content of the feedback.

[0046] When providing feedback, the providing unit can select optimal feedback taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides feedback related to the area. The geographical location information is the user's IP address, GPS data, or the like. For example, if the user is in a specific area, the providing unit provides feedback related to the area. Furthermore, if the user is traveling, the providing unit can also provide feedback related to the travel destination. For example, if the user is traveling, the providing unit provides feedback related to the travel destination. This allows the providing unit to provide optimal feedback based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to AI and cause the AI ​​to select optimal feedback.

[0047] When providing feedback, the providing unit can analyze the user's social media activity and customize the content of the feedback. The providing unit provides feedback based on, for example, information recently shared by the user on social media. Social media activity includes the content of posts made by the user on social media and the number of likes. For example, the providing unit provides feedback based on information recently shared by the user on social media. The providing unit can also provide feedback based on topics in which the user has shown interest on social media. For example, the providing unit provides feedback based on topics in which the user has shown interest on social media. This allows the providing unit to customize the content of the feedback based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's social media activity into AI and cause the AI ​​to customize the content of the feedback.

[0048] When presenting feedback, the presentation unit can select the optimal display method by referring to the user's past operation history. The presentation unit provides feedback based on, for example, the user's preferred display method in the past. The operation history may include the user's click history and page transition history on a website. For example, the presentation unit provides feedback based on the user's preferred display method in the past. The presentation unit can also provide feedback at the optimal timing based on the user's past operation history. For example, if the user has tended to visit websites during a specific time period in the past, the presentation unit provides feedback during that time period. This allows the presentation unit to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the user's past operation history into AI and have the AI ​​select the optimal display method.

[0049] When presenting feedback, the presentation unit can select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. Furthermore, if the user is using a desktop, the presentation unit can also provide a display method optimized for a large screen. For example, if the user is using a desktop, the presentation unit provides a display method optimized for a large screen. This allows the presentation unit to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0050] When presenting feedback, the presentation unit can select the optimal display method taking into account the user's geographical location information. For example, if the user is in a specific area, the presentation unit provides feedback related to the area. The geographical location information may be the user's IP address or GPS data. For example, if the user is in a specific area, the presentation unit provides feedback related to the area. Furthermore, if the user is traveling, the presentation unit can also provide feedback related to the travel destination. For example, if the user is traveling, the presentation unit provides feedback related to the travel destination. This allows the presentation unit to provide the optimal display method based on the user's geographical location information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input the user's geographical location information to AI and cause the AI ​​to select the optimal display method.

[0051] When presenting feedback, the presentation unit can analyze the user's social media activity and select an optimal display method. The presentation unit provides feedback based on, for example, information recently shared by the user on social media. Social media activity includes the content of posts made by the user on social media and the number of likes. For example, the presentation unit provides feedback based on information recently shared by the user on social media. The presentation unit can also provide feedback based on topics in which the user has shown interest on social media. For example, the presentation unit provides feedback based on topics in which the user has shown interest on social media. This allows the presentation unit to provide an optimal display method based on the user's social media activity. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit may input the user's social media activity into AI and cause the AI ​​to select an optimal display method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The calculation unit may also take into account the user's past learning history when calculating the user's recognition ratio. For example, the calculation unit may calculate the recognition ratio based on the content and learning time of the user in the past. If the user has a high learning history in a particular field, the calculation unit may prioritize the accuracy rate of CAPTCHAs related to that field in calculating the recognition ratio. Also, if the user has a low learning history in a particular field, the calculation unit may lower the accuracy rate of CAPTCHAs related to that field. This allows the calculation unit to more accurately calculate the recognition ratio based on the user's learning history.

[0054] The notification unit can also monitor the user's device usage and provide feedback at optimal times. For example, if the user uses the device for a long time, the notification unit can provide feedback encouraging the user to take a break. Also, if the user frequently uses a specific application, the notification unit can provide feedback related to that application. Furthermore, by providing feedback during times when the user is not using the device, it is possible to attract the user's attention. In this way, the notification unit can provide feedback at optimal times based on the user's device usage.

[0055] The calculation unit may also take the user's social background into account when calculating the user's recognition ratio. For example, the calculation unit may calculate the recognition ratio based on the user's social background, such as the user's occupation, educational level, and residential area. If the user is engaged in a specific occupation, the calculation unit may prioritize the accuracy rate of CAPTCHAs related to that occupation. Also, if the user has a specific educational level, the calculation unit may prioritize the accuracy rate of CAPTCHAs related to that educational level. This allows the calculation unit to more accurately calculate the recognition ratio based on the user's social background.

[0056] The presentation unit can also analyze the user's past feedback history and select the optimal display method. For example, the presentation unit provides feedback based on a display method that the user has previously preferred. Also, if the user has previously avoided a specific display method, the presentation unit can adjust the display method to avoid that display method. Furthermore, if the user has previously preferred to receive feedback during a specific time period, the presentation unit can provide feedback during that time period. In this way, the presentation unit can provide the optimal display method based on the user's past feedback history.

[0057] The providing unit can also monitor the user's reaction to the feedback in real time and dynamically adjust the content of the feedback. For example, if the user has a positive reaction to the feedback, the providing unit can continue to provide the same feedback. Also, if the user has a negative reaction to the feedback, the providing unit can change the content of the feedback and provide it. Furthermore, if the user has no reaction to the feedback, the providing unit can reevaluate the form and content of the feedback and provide optimal feedback. In this way, the providing unit can dynamically adjust the content of the feedback based on the user's reaction.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The selection unit selects the type of CAPTCHA. For example, the selection unit can select the type of CAPTCHA, such as image authentication or character authentication. The selection unit selects the type of CAPTCHA to be displayed when the user logs in, and displays image authentication if the user selects image authentication, or character authentication if the user selects character authentication. Step 2: The calculation unit calculates the user's recognition ratio. For example, the calculation unit calculates the recognition ratio based on the user's correct answer rate, response time, and past response history. The calculation unit records the correct answer rate and response time for the CAPTCHA displayed when the user logs in and calculates the recognition ratio. The calculation unit records the correct answer rate and response time for image authentication and character authentication and calculates the recognition ratio based on that. Step 3: The provider provides feedback based on the user's recognition ratio. For example, the provider provides customized information that reflects the user's behavioral patterns and preferences. It may suggest products or services that the user may be interested in, or suggest ways to strengthen security when logging in (for example, introducing two-factor authentication or strengthening passwords). Step 4: The presentation unit presents the feedback provided by the provision unit to the user. For example, the presentation unit presents feedback based on responses automatically collected when the user logs in. The presentation unit collects responses for image authentication or character authentication and presents feedback based on the collected responses.

[0060] (Example 2) An image feedback system according to an embodiment of the present invention utilizes CAPTCHAs linked to a user's daily login behavior. When a user logs in, the system displays a CAPTCHA and allows the user to select a type, such as image authentication or character authentication. The system then calculates the user's recognition rate based on the user's accuracy rate, response time, past response history, and other factors. The feedback content may include customized information that reflects the user's behavioral patterns and preferences. For example, suggestions for products and services that may interest the user or suggestions for enhanced login security may be considered. CAPTCHA responses are automatically collected when the user logs in, and if a certain number of responses are obtained, they are adopted as feedback based on the user's recognition rate. This mechanism provides feedback based on the user's recognition rate, improving the user experience. For example, the CAPTCHA displayed when a user logs in can be selected from image authentication and character authentication. If the user selects image authentication, the system records the accuracy rate and response time of the image selected by the user and calculates the recognition rate. The system then suggests products and services that the user may be interested in based on the user's recognition rate. For example, customized information may be provided based on the user's past interest in the product or service. The system also suggests ways to strengthen security when users log in, such as introducing two-factor authentication or strengthening passwords. This allows users to receive feedback based on their behavioral patterns and preferences, improving security when logging in. This allows the image feedback system to provide feedback based on the user's recognition rate, improving the user experience.

[0061] An image feedback system according to an embodiment includes a selection unit, a calculation unit, a providing unit, and a presentation unit. The selection unit selects a type of CAPTCHA. For example, the selection unit can select a type of CAPTCHA, such as image authentication or character authentication. The selection unit selects a type of CAPTCHA to be displayed when a user logs in. For example, if a user selects image authentication, the selection unit displays image authentication. Furthermore, if a user selects character authentication, the selection unit can also display character authentication. The calculation unit calculates a user's recognition ratio. For example, the calculation unit calculates the recognition ratio based on the user's correct answer rate, response time, and past response history. The calculation unit records the correct answer rate and response time of the CAPTCHA displayed when the user logs in and calculates the recognition ratio. For example, if a user selects image authentication, the calculation unit records the correct answer rate and response time of the image authentication and calculates the recognition ratio. Furthermore, if a user selects character authentication, the calculation unit can also record the correct answer rate and response time of the character authentication and calculate the recognition ratio. The providing unit provides feedback based on the user's recognition ratio. For example, the providing unit provides customized information that reflects the user's behavioral patterns and preferences. The providing unit suggests products and services that the user may be interested in. For example, the providing unit provides customized information based on products and services in which the user has shown interest in the past. The providing unit also suggests strengthening security when the user logs in. For example, the providing unit suggests introducing two-factor authentication or strengthening passwords. The presentation unit presents the feedback provided by the providing unit to the user. For example, the presentation unit presents feedback based on responses automatically collected when the user logs in. The presentation unit collects responses to a CAPTCHA that is displayed when the user logs in and presents the feedback. For example, if the user selects image authentication, the presentation unit collects the image authentication response and presents the feedback. Also, if the user selects character authentication, the presentation unit can collect the character authentication response and present the feedback.As a result, the image feedback system according to the embodiment can provide feedback based on the user's recognition ratio, improving the user experience.

[0062] The selection unit can select at least one type of CAPTCHA from image authentication and character authentication. The selection unit can select, for example, image authentication. Image authentication is performed by having the user select a specific image from displayed images. For example, the selection unit can instruct the user to select an image of a car from displayed images. The selection unit can also select character authentication. Character authentication is performed by having the user input displayed characters. For example, the selection unit can instruct the user to input displayed characters. This allows the selection unit to select a type of CAPTCHA according to the user's preferences. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's past CAPTCHA selection history into AI and select the optimal type of CAPTCHA.

[0063] The calculation unit can calculate the recognition ratio based on the user's accuracy rate, response time, and past response history. The calculation unit calculates the recognition ratio based on, for example, the user's accuracy rate. The accuracy rate is the number of correct answers to CAPTCHAs answered by the user divided by the total number of answers. For example, if a user answers 10 CAPTCHAs and answers 8 of them correctly, the calculation unit calculates the accuracy rate as 80%. The calculation unit can also calculate the recognition ratio based on the user's response time. The response time is the time it takes the user to answer the CAPTCHA. For example, if it takes the user an average of 5 seconds to answer a CAPTCHA, the calculation unit calculates the response time as 5 seconds. The calculation unit can also calculate the recognition ratio based on the user's past response history. The past response history includes the content of CAPTCHAs the user has answered in the past and the date and time of the answers. For example, the calculation unit calculates the recognition ratio based on the accuracy rate and response time of CAPTCHAs the user has answered in the past. This allows the calculation unit to accurately calculate the user's recognition ratio. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input the user's past response history into AI and have the AI ​​calculate the recognition ratio.

[0064] The providing unit can provide customized information that reflects the user's behavioral patterns and preferences. The providing unit provides customized information based on, for example, the user's behavioral patterns. The behavioral patterns include the user's operations on a website, the frequency of clicks, and page transition patterns. For example, the providing unit analyzes links and pages frequently clicked by the user and provides information that is likely to interest the user. The providing unit can also provide customized information based on the user's preferences. Preferences include products and services selected by the user in the past and survey results. For example, the providing unit provides customized information based on products and services purchased by the user in the past. This allows the providing unit to provide customized information to the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's behavioral patterns and preferences into AI and have the AI ​​provide customized information.

[0065] The providing unit can suggest products or services that the user may be interested in. The providing unit makes suggestions based on, for example, products or services in which the user has shown interest in the past. Products or services that the user may be interested in include products or services the user has previously purchased, browsing history, etc. For example, the providing unit suggests products related to products the user has previously purchased. The providing unit can also make suggestions based on products or services the user has previously viewed. For example, the providing unit suggests products related to products the user has previously viewed. This allows the providing unit to suggest products or services based on the user's interests. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past purchase history or browsing history into AI and cause the AI ​​to suggest products or services that the user may be interested in.

[0066] The providing unit can make suggestions for strengthening security at the time of login. The providing unit, for example, suggests the introduction of two-factor authentication. Two-factor authentication is a method for strengthening security by using another authentication method in addition to a password when a user logs in. For example, the providing unit suggests to the user that they enter an authentication code sent to their smartphone in addition to their password. The providing unit can also suggest strengthening the password. Strengthening the password is a method for suggesting to the user that they set a more complex and strong password when logging in. For example, the providing unit suggests to the user that they set a strong password that combines alphanumeric characters and symbols. This allows the providing unit to strengthen security at the time of user login. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can cause AI to execute the suggestion for strengthening security at the time of user login.

[0067] The presentation unit can present feedback based on a response automatically collected when the user logs in. The presentation unit, for example, collects a response to a CAPTCHA displayed when the user logs in and presents the feedback. The automatically collected response may be the content of the user's answer to the CAPTCHA and the time it took to answer. For example, if the user selects image authentication, the presentation unit collects a response to the image authentication and presents the feedback. Furthermore, if the user selects character authentication, the presentation unit can also collect a response to the character authentication and present the feedback. This allows the presentation unit to provide feedback based on the user's response at the time of login. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's response at the time of login into AI and have the AI ​​present the feedback.

[0068] The selection unit can estimate the user's emotions and dynamically change the type of CAPTCHA based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can provide simple image authentication to reduce the user's burden. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the selection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the selection unit can analyze the tone and speed of the user's voice to estimate the emotions. This allows the selection unit to change the type of CAPTCHA depending on the user's emotions. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's emotion data into AI and have the AI ​​change the type of CAPTCHA.

[0069] The selection unit can analyze the user's past CAPTCHA selection history and automatically select the most appropriate type of CAPTCHA. For example, if the user has previously preferred image authentication, the selection unit can prioritize providing image authentication. The past CAPTCHA selection history includes the types of CAPTCHAs selected by the user in the past and the date and time of selection. For example, the selection unit can analyze the number of times the user has previously selected image authentication and the date and time, and prioritize providing image authentication. The selection unit can also prioritize character authentication if the user has frequently selected character authentication in the past. For example, the selection unit can analyze the number of times the user has previously selected character authentication and the date and time, and prioritize providing character authentication. This allows the selection unit to provide the most appropriate CAPTCHA based on the user's past selection history. Some or all of the above-described processing by the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's past CAPTCHA selection history into AI and select the most appropriate type of CAPTCHA.

[0070] When selecting a CAPTCHA, the selection unit can select the optimal type of CAPTCHA by taking into consideration the user's device information. For example, if the user is using a smartphone, the selection unit provides image authentication suitable for touch operation. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the selection unit provides image authentication suitable for touch operation. Furthermore, if the user is using a desktop, the selection unit can also provide character authentication suitable for keyboard input. For example, if the user is using a desktop, the selection unit provides character authentication suitable for keyboard input. This allows the selection unit to provide the optimal CAPTCHA based on the user's device information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's device information into AI to select the optimal type of CAPTCHA.

[0071] The selection unit can estimate the user's emotions and adjust the difficulty of the CAPTCHA based on the estimated user emotions. For example, if the user is nervous, the selection unit can provide an easy CAPTCHA to encourage a successful experience. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the selection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the selection unit can analyze the tone and speed of the user's voice to estimate the emotions. This allows the selection unit to adjust the difficulty of the CAPTCHA according to the user's emotions. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without AI. For example, the selection unit can input the user's emotion data into AI and have the AI ​​adjust the difficulty of the CAPTCHA.

[0072] When selecting a CAPTCHA, the selection unit can prioritize selecting a highly relevant CAPTCHA by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the selection unit provides image authentication related to that area. Geographical location information may be the user's IP address or GPS data. For example, if the user is in a specific area, the selection unit provides image authentication related to that area. Furthermore, if the user is traveling, the selection unit can also provide a CAPTCHA related to the user's travel destination. For example, if the user is traveling, the selection unit provides a CAPTCHA related to the user's travel destination. This allows the selection unit to provide a highly relevant CAPTCHA based on the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input the user's geographical location information into AI and cause the AI ​​to select a highly relevant CAPTCHA.

[0073] When selecting a CAPTCHA, the selection unit can analyze the user's social media activity and select a relevant CAPTCHA. For example, the selection unit can provide a CAPTCHA related to images recently shared by the user on social media. Social media activity can include the content of posts made by the user on social media and the number of likes. For example, the selection unit can provide a CAPTCHA related to images recently shared by the user on social media. The selection unit can also provide a CAPTCHA related to topics in which the user has shown interest on social media. For example, the selection unit can provide a CAPTCHA related to topics in which the user has shown interest on social media. This allows the selection unit to provide a relevant CAPTCHA based on the user's social media activity. Some or all of the above-described processing by the selection unit can be performed using, or without, AI. For example, the selection unit can input the user's social media activity into AI and have the AI ​​select a relevant CAPTCHA.

[0074] The calculation unit can estimate the user's emotions and adjust the calculation method of the recognition ratio based on the estimated user's emotions. For example, if the user is nervous, the calculation unit calculates the recognition ratio by prioritizing response time. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the calculation unit captures the user's facial expressions with a camera and estimates the emotion using an emotion estimation algorithm. The calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the calculation unit analyzes the tone and speed of the user's voice to estimate the emotion. This allows the calculation unit to adjust the calculation method of the recognition ratio according to the user's emotions. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input user's emotion data into AI and have the AI ​​adjust the calculation method of the recognition ratio.

[0075] When calculating the recognition ratio, the calculation unit can optimize the calculation algorithm by referring to the user's past answer history. For example, if the user has a high accuracy rate in the past, the calculation unit calculates the recognition ratio based on that data. The past answer history includes the content of CAPTCHAs the user has answered in the past and the date and time of the answers. For example, if the user has a high accuracy rate in the past, the calculation unit calculates the recognition ratio based on that data. Furthermore, if the user has a low accuracy rate for specific CAPTCHAs in the past, the calculation unit can also calculate the recognition ratio based on that data. For example, if the user has a low accuracy rate for specific CAPTCHAs in the past, the calculation unit calculates the recognition ratio based on that data. This allows the calculation unit to optimize the recognition ratio calculation algorithm based on the user's past answer history. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input the user's past answer history into AI and cause the AI ​​to optimize the recognition ratio calculation algorithm.

[0076] The calculation unit can improve the accuracy of the calculation when calculating the recognition ratio by taking into account device information of the user. For example, if the user is using a smartphone, the calculation unit calculates the recognition ratio by taking into account touch operation data. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the calculation unit calculates the recognition ratio by taking into account touch operation data. Furthermore, if the user is using a desktop computer, the calculation unit can also calculate the recognition ratio by taking into account keyboard input data. For example, if the user is using a desktop computer, the calculation unit calculates the recognition ratio by taking into account keyboard input data. This allows the calculation unit to improve the accuracy of the calculation of the recognition ratio based on the user's device information. Some or all of the above-described processing in the calculation unit may be performed using AI, for example, or may be performed without using AI. For example, the calculation unit can input the user's device information into AI and have the AI ​​calculate the recognition ratio.

[0077] The calculation unit can estimate the user's emotions and adjust the frequency of calculation of the recognition ratio based on the estimated user's emotions. For example, if the user is nervous, the calculation unit can set the calculation frequency of the recognition ratio low to reduce the burden on the user. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the calculation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The calculation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the calculation unit can analyze the tone and speed of the user's voice to estimate the emotions. This allows the calculation unit to adjust the calculation frequency of the recognition ratio according to the user's emotions. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input the user's emotion data into AI and have the AI ​​adjust the calculation frequency of the recognition ratio.

[0078] The calculation unit can improve the accuracy of the calculation when calculating the recognition ratio by taking into account the user's geographical location information. For example, if the user is in a specific area, the calculation unit calculates the recognition ratio by taking into account data for that area. The geographical location information may be the user's IP address or GPS data. For example, if the user is in a specific area, the calculation unit calculates the recognition ratio by taking into account data for that area. Furthermore, if the user is traveling, the calculation unit can also calculate the recognition ratio by taking into account data for the travel destination. For example, if the user is traveling, the calculation unit calculates the recognition ratio by taking into account data for the travel destination. This allows the calculation unit to improve the accuracy of the calculation of the recognition ratio based on the user's geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit may input the user's geographical location information into AI and cause the AI ​​to calculate the recognition ratio.

[0079] The calculation unit can analyze the user's social media activity when calculating the recognition ratio to improve the accuracy of the calculation. For example, the calculation unit calculates the recognition ratio by taking into account information recently shared by the user on social media. Social media activity includes the content of posts made by the user on social media and the number of likes. For example, the calculation unit calculates the recognition ratio by taking into account information recently shared by the user on social media. The calculation unit can also calculate the recognition ratio by taking into account topics in which the user has shown interest on social media. For example, the calculation unit calculates the recognition ratio by taking into account topics in which the user has shown interest on social media. This allows the calculation unit to improve the accuracy of the recognition ratio calculation based on the user's social media activity. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's social media activity into AI and have the AI ​​calculate the recognition ratio.

[0080] The providing unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide content that helps the user relax. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. This allows the providing unit to adjust the content of the feedback according to the user's emotions. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into AI and have the AI ​​adjust the content of the feedback.

[0081] When providing feedback, the providing unit can select optimal feedback by analyzing the user's past behavioral patterns. The providing unit provides feedback based on, for example, products or services in which the user has shown interest in the past. The behavioral patterns include the user's operations on a website, frequency of clicks, and page transition patterns. For example, the providing unit provides feedback based on the user's past interest in products or services. The providing unit can also provide feedback at optimal timing based on the user's past behavioral patterns. For example, if the user has tended to visit websites during a specific time period in the past, the providing unit provides feedback during that time period. This allows the providing unit to provide optimal feedback based on the user's past behavioral patterns. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's past behavioral patterns into AI and have the AI ​​select optimal feedback.

[0082] When providing feedback, the providing unit can customize the content of the feedback by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit provides feedback tailored to the screen size. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the providing unit provides feedback tailored to the screen size. Furthermore, if the user is using a desktop, the providing unit can also provide feedback optimized for a large screen. For example, if the user is using a desktop, the providing unit provides feedback optimized for a large screen. This allows the providing unit to customize the content of the feedback based on the user's device information. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input the user's device information into AI and cause the AI ​​to customize the content of the feedback.

[0083] The providing unit can estimate the user's emotions and determine the priority of feedback based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing relaxing content. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the user's voice to estimate the emotions. This allows the providing unit to determine the priority of feedback according to the user's emotions. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's emotion data into AI and have the AI ​​determine the priority of feedback.

[0084] When providing feedback, the providing unit can select optimal feedback taking into account the user's geographical location information. For example, if the user is in a specific area, the providing unit provides feedback related to the area. The geographical location information is the user's IP address, GPS data, or the like. For example, if the user is in a specific area, the providing unit provides feedback related to the area. Furthermore, if the user is traveling, the providing unit can also provide feedback related to the travel destination. For example, if the user is traveling, the providing unit provides feedback related to the travel destination. This allows the providing unit to provide optimal feedback based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to AI and cause the AI ​​to select optimal feedback.

[0085] When providing feedback, the providing unit can analyze the user's social media activity and customize the content of the feedback. The providing unit provides feedback based on, for example, information recently shared by the user on social media. Social media activity includes the content of posts made by the user on social media and the number of likes. For example, the providing unit provides feedback based on information recently shared by the user on social media. The providing unit can also provide feedback based on topics in which the user has shown interest on social media. For example, the providing unit provides feedback based on topics in which the user has shown interest on social media. This allows the providing unit to customize the content of the feedback based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the user's social media activity into AI and cause the AI ​​to customize the content of the feedback.

[0086] The presentation unit can estimate the user's emotions and adjust the feedback display method based on the estimated user emotions. For example, when the user is nervous, the presentation unit provides a simple, highly visible display method. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the presentation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The presentation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the presentation unit analyzes the tone and speed of the user's voice to estimate the emotions. This allows the presentation unit to adjust the feedback display method according to the user's emotions. Some or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without AI. For example, the presentation unit can input the user's emotion data into AI and have the AI ​​adjust the feedback display method.

[0087] When presenting feedback, the presentation unit can select the optimal display method by referring to the user's past operation history. The presentation unit provides feedback based on, for example, the user's preferred display method in the past. The operation history may include the user's click history and page transition history on a website. For example, the presentation unit provides feedback based on the user's preferred display method in the past. The presentation unit can also provide feedback at the optimal timing based on the user's past operation history. For example, if the user has tended to visit websites during a specific time period in the past, the presentation unit provides feedback during that time period. This allows the presentation unit to provide the optimal display method based on the user's past operation history. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit can input the user's past operation history into AI and have the AI ​​select the optimal display method.

[0088] When presenting feedback, the presentation unit can select the optimal display method by taking into consideration the user's device information. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. The device information includes the type of device, OS, browser, etc. used by the user. For example, if the user is using a smartphone, the presentation unit provides a display method tailored to the screen size. Furthermore, if the user is using a desktop, the presentation unit can also provide a display method optimized for a large screen. For example, if the user is using a desktop, the presentation unit provides a display method optimized for a large screen. This allows the presentation unit to provide the optimal display method based on the user's device information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0089] The presentation unit can estimate the user's emotions and adjust the display order of the feedback based on the estimated user's emotions. For example, if the user is nervous, the presentation unit can prioritize displaying relaxing content. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the presentation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The presentation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the presentation unit can analyze the tone and speed of the user's voice to estimate the emotions. This allows the presentation unit to adjust the display order of the feedback according to the user's emotions. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit can input the user's emotion data into AI and have the AI ​​adjust the display order of the feedback.

[0090] When presenting feedback, the presentation unit can select the optimal display method taking into account the user's geographical location information. For example, if the user is in a specific area, the presentation unit provides feedback related to the area. The geographical location information may be the user's IP address or GPS data. For example, if the user is in a specific area, the presentation unit provides feedback related to the area. Furthermore, if the user is traveling, the presentation unit can also provide feedback related to the travel destination. For example, if the user is traveling, the presentation unit provides feedback related to the travel destination. This allows the presentation unit to provide the optimal display method based on the user's geographical location information. Some or all of the above-described processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI. For example, the presentation unit may input the user's geographical location information to AI and cause the AI ​​to select the optimal display method.

[0091] When presenting feedback, the presentation unit can analyze the user's social media activity and select an optimal display method. The presentation unit provides feedback based on, for example, information recently shared by the user on social media. Social media activity includes the content of posts made by the user on social media and the number of likes. For example, the presentation unit provides feedback based on information recently shared by the user on social media. The presentation unit can also provide feedback based on topics in which the user has shown interest on social media. For example, the presentation unit provides feedback based on topics in which the user has shown interest on social media. This allows the presentation unit to provide an optimal display method based on the user's social media activity. Some or all of the above-described processing in the presentation unit may be performed using, or without, AI. For example, the presentation unit may input the user's social media activity into AI and cause the AI ​​to select an optimal display method. === Hard Collateral 1-1 === Each of the multiple elements, including the selection unit, calculation unit, provision unit, and presentation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the selection unit can select the type of CAPTCHA via the control unit 46A of the smart device 14. The calculation unit calculates the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The provision unit provides feedback based on the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The presentation unit presents the feedback to the user via the control unit 46A of the smart device 14. The selection unit can also estimate the user's emotion and dynamically change the type of CAPTCHA based on the estimated emotion. For example, the selection unit can analyze the user's facial expression and voice using the camera 42 and microphone 38B of the smart device 14 to estimate the emotion. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned selection unit, calculation unit, provision unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit can select the type of CAPTCHA via the control unit 46A of the smart glasses 214. The calculation unit calculates the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The provision unit provides feedback based on the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The presentation unit presents the feedback to the user via the control unit 46A of the smart glasses 214. The selection unit can also estimate the user's emotion and dynamically change the type of CAPTCHA based on the estimated emotion. For example, the selection unit analyzes the user's facial expression and voice using the camera 42 and microphone 238 of the smart glasses 214 to estimate the emotion. === Hard Collateral 1-3 === Each of the multiple elements including the selection unit, calculation unit, provision unit, and presentation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the selection unit can select the type of CAPTCHA via the control unit 46A of the headset type terminal 314. The calculation unit calculates the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The provision unit provides feedback based on the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The presentation unit presents the feedback to the user via the control unit 46A of the headset type terminal 314. The selection unit can also estimate the user's emotion and dynamically change the type of CAPTCHA based on the estimated emotion. For example, the selection unit analyzes the user's facial expression and voice using the camera 42 and microphone 238 of the headset type terminal 314 to estimate the emotion. === Hard Collateral 1-4 === Each of the multiple elements including the selection unit, calculation unit, provision unit, and presentation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the selection unit can select the type of CAPTCHA via the control unit 46A of the robot 414. The calculation unit calculates the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The provision unit provides feedback based on the user's recognition ratio via the specific processing unit 290 of the data processing device 12. The presentation unit presents the feedback to the user via the control unit 46A of the robot 414. The selection unit can also estimate the user's emotion and dynamically change the type of CAPTCHA based on the estimated emotion. For example, the selection unit can analyze the user's facial expression and voice using the camera 42 and microphone 238 of the robot 414 to estimate the emotion.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The selection unit can also acquire biometric information of the user and dynamically change the type of CAPTCHA based on the acquired biometric information. For example, the selection unit measures the user's heart rate and electrodermal response with a sensor to estimate the user's stress level. If the user indicates a high stress level, the selection unit can provide simple image authentication to reduce the user's burden. Alternatively, if the user is relaxed, the selection unit can provide more complex character authentication. This allows the selection unit to adjust the type of CAPTCHA based on the user's biometric information.

[0094] The calculation unit may also take into account the user's past learning history when calculating the user's recognition ratio. For example, the calculation unit may calculate the recognition ratio based on the content and learning time of the user in the past. If the user has a high learning history in a particular field, the calculation unit may prioritize the accuracy rate of CAPTCHAs related to that field in calculating the recognition ratio. Also, if the user has a low learning history in a particular field, the calculation unit may lower the accuracy rate of CAPTCHAs related to that field. This allows the calculation unit to more accurately calculate the recognition ratio based on the user's learning history.

[0095] The providing unit can also estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide relaxing content to encourage the user to calm down. If the user is sad, the providing unit can provide encouraging messages or positive content. Furthermore, if the user is happy, the providing unit can provide content that further enhances the user's emotions. In this way, the providing unit can provide feedback according to the user's emotions.

[0096] The notification unit can also monitor the user's device usage and provide feedback at optimal times. For example, if the user uses the device for a long time, the notification unit can provide feedback encouraging the user to take a break. Also, if the user frequently uses a specific application, the notification unit can provide feedback related to that application. Furthermore, by providing feedback during times when the user is not using the device, it is possible to attract the user's attention. In this way, the notification unit can provide feedback at optimal times based on the user's device usage.

[0097] The selection unit can also estimate the user's emotions and change the display format of the CAPTCHA based on the estimated user's emotions. For example, if the user is tired, a visually simple and easy-to-understand CAPTCHA can be provided to reduce the user's burden. Alternatively, if the user is concentrating, a more complex CAPTCHA can be provided to test the user's recognition ability. Furthermore, if the user is relaxed, a playful CAPTCHA can be provided to attract the user's interest. In this way, the selection unit can provide a display format of the CAPTCHA according to the user's emotions.

[0098] The calculation unit may also take the user's social background into account when calculating the user's recognition ratio. For example, the calculation unit may calculate the recognition ratio based on the user's social background, such as the user's occupation, educational level, and residential area. If the user is engaged in a specific occupation, the calculation unit may prioritize the accuracy rate of CAPTCHAs related to that occupation. Also, if the user has a specific educational level, the calculation unit may prioritize the accuracy rate of CAPTCHAs related to that educational level. This allows the calculation unit to more accurately calculate the recognition ratio based on the user's social background.

[0099] The providing unit can also estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing feedback that helps relieve stress. Also, if the user is relaxing, the providing unit can prioritize providing feedback related to entertainment or hobbies. Furthermore, if the user is concentrating, the providing unit can prioritize providing feedback related to study or work. In this way, the providing unit can determine the priority of feedback according to the user's emotions.

[0100] The presentation unit can also analyze the user's past feedback history and select the optimal display method. For example, the presentation unit provides feedback based on a display method that the user has previously preferred. Also, if the user has previously avoided a specific display method, the presentation unit can adjust the display method to avoid that display method. Furthermore, if the user has previously preferred to receive feedback during a specific time period, the presentation unit can provide feedback during that time period. In this way, the presentation unit can provide the optimal display method based on the user's past feedback history.

[0101] The selection unit can also estimate the user's emotions and adjust the difficulty of the CAPTCHA based on the estimated user emotions. For example, if the user is nervous, an easy CAPTCHA can be provided to encourage a successful experience. The user's emotions are estimated using technologies such as facial expression recognition and voice analysis. For example, the selection unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the selection unit analyzes the tone and speed of the user's voice to estimate the emotions. This allows the selection unit to adjust the difficulty of the CAPTCHA according to the user's emotions.

[0102] The providing unit can also monitor the user's reaction to the feedback in real time and dynamically adjust the content of the feedback. For example, if the user has a positive reaction to the feedback, the providing unit can continue to provide the same feedback. Also, if the user has a negative reaction to the feedback, the providing unit can change the content of the feedback and provide it. Furthermore, if the user has no reaction to the feedback, the providing unit can reevaluate the form and content of the feedback and provide optimal feedback. In this way, the providing unit can dynamically adjust the content of the feedback based on the user's reaction.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The selection unit selects the type of CAPTCHA. For example, the selection unit can select the type of CAPTCHA, such as image authentication or character authentication. The selection unit selects the type of CAPTCHA to be displayed when the user logs in, and displays image authentication if the user selects image authentication, or character authentication if the user selects character authentication. Step 2: The calculation unit calculates the user's recognition ratio. For example, the calculation unit calculates the recognition ratio based on the user's correct answer rate, response time, and past response history. The calculation unit records the correct answer rate and response time for the CAPTCHA displayed when the user logs in and calculates the recognition ratio. The calculation unit records the correct answer rate and response time for image authentication and character authentication and calculates the recognition ratio based on that. Step 3: The provider provides feedback based on the user's recognition ratio. For example, the provider provides customized information that reflects the user's behavioral patterns and preferences. It may suggest products or services that the user may be interested in, or suggest ways to strengthen security when logging in (for example, introducing two-factor authentication or strengthening passwords). Step 4: The presentation unit presents the feedback provided by the provision unit to the user. For example, the presentation unit presents feedback based on responses automatically collected when the user logs in. The presentation unit collects responses for image authentication or character authentication and presents feedback based on the collected responses.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 selection section for selecting the type of CAPTCHA; a calculation unit that calculates a recognition ratio of the user based on the CAPTCHA selected by the selection unit; a providing unit that provides feedback based on the recognition ratio calculated by the calculating unit; a presentation unit that presents the feedback provided by the provision unit to a user; Equipped with A system characterized by:

2. The selection unit Select at least one CAPTCHA type: image or text 2. The system of claim 1.

3. The calculation unit Calculate the recognition rate based on the user's accuracy rate, response time, and past response history.

2. The system of claim 1.

4. The providing unit Providing customized information that reflects the user's behavioral patterns and preferences 2. The system of claim 1.

5. The providing unit To make suggestions for products or services that may be of interest to you 2. The system of claim 1.

6. The providing unit Propose security enhancements when logging in 2. The system of claim 1.

7. The presentation unit Present feedback based on responses automatically collected when users log in 2. The system of claim 1.

8. The selection unit Estimate user emotions and dynamically change the type of CAPTCHA based on the estimated user emotions.

2. The system of claim 1.

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