system

Abstract

An object of the system according to the embodiment is to prevent the disclosure of erroneous personal information or a face photo on the SNS.SOLUTION: A system according to an embodiment includes a registering unit, a checking unit, and a blocking unit. The registration unit registers personal information of the user. The check unit checks specific information to be disclosed on the SNS based on the personal information registered in the registration unit. The block part blocks the disclosure of the specific information when the specific information checked by the check part coincides with the registered personal information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to prevent the incorrect publication of personal information or photographs of people's faces on social media, posing a risk of personal information being leaked.

[0005] The system according to the embodiment aims to prevent the erroneous disclosure of personal information and photographs of faces on social networking sites. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a check unit, and a blocking unit. The registration unit registers personal information of users. The check unit checks specific information to be made public on the SNS based on the personal information registered in the registration unit. The blocking unit blocks the publication of the specific information checked by the check unit if the specific information matches the registered personal information. [Effects of the Invention]

[0007] The system according to the embodiment can prevent the erroneous disclosure of personal information or photographs of faces on social networking sites. [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) A system according to an embodiment of the present invention prevents the erroneous disclosure of personal information, facial photos, and other information on social networking sites (SNSs). This system allows users to register their personal information, checks the information to be published on the SNS in advance, and blocks the information from being published if it matches the registered personal information. For example, when a user attempts to upload a facial photo to the SNS, the system compares the facial photo with pre-registered facial photos and blocks the publication if it matches. Similarly, when a user attempts to publish their name or address, the system compares the photo with pre-registered information and blocks the publication if it matches. This mechanism prevents users from accidentally disclosing personal information and prevents the leakage of personal information. It also allows even young users to use the SNS with peace of mind. This system pre-registers users' personal information and prevents the erroneous disclosure of personal information on the SNS. For example, when a user attempts to upload a facial photo to the SNS, the system compares the facial photo with pre-registered facial photos and blocks the publication if it matches. Similarly, when a user attempts to publish their name or address, the system compares the photo with pre-registered information and blocks the publication if it matches. This prevents users from accidentally disclosing personal information and prevents the leakage of personal information. In addition, even young users can use SNS with peace of mind.

[0029] An information disclosure prevention system according to an embodiment includes a registration unit, a check unit, and a block unit. The registration unit registers personal information of a user. The personal information includes, but is not limited to, for example, a name, address, telephone number, email address, and facial photograph. The registration unit, for example, stores the personal information entered by the user in a database. The registration unit can also encrypt and store the personal information of the user. For example, encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used. The check unit checks specific information to be disclosed on an SNS based on the personal information registered in the registration unit. For example, the check unit checks facial photographs to be disclosed on the SNS using facial recognition technology. The check unit can also check text information to be disclosed on the SNS using text matching technology. For example, deep learning-based facial recognition technology or keyword matching technology is used. The block unit blocks the disclosure of information if the specific information checked by the check unit matches the registered personal information. For example, the block unit notifies the user of the information whose disclosure has been blocked. For example, the information disclosure prevention system according to the embodiment uses methods such as email notification, in-app notification, SMS notification, etc. In this way, the information disclosure prevention system according to the embodiment can register the personal information of the user in advance and prevent the personal information from being erroneously disclosed on the SNS.

[0030] The check unit can check facial photos posted on SNS using a specific facial recognition technology. Specific facial recognition technologies include, but are not limited to, deep learning-based facial recognition technology and template matching technology. The check unit can check facial photos posted on SNS using, for example, deep learning-based facial recognition technology. The check unit can also check facial photos using template matching technology. For example, deep learning-based facial recognition technology learns a large amount of facial image data and performs highly accurate facial recognition. Template matching technology compares a pre-registered facial image with a publicly posted facial image and calculates the degree of match. This allows the use of facial recognition technology to accurately check the posting of facial photos.

[0031] The check unit may check text information published on the SNS using a specific text matching technology. Specific text matching technologies include, but are not limited to, keyword matching technology and natural language processing technology. For example, the check unit may check text information published on the SNS using keyword matching technology. The check unit may also check text information using natural language processing technology. For example, keyword matching technology compares pre-registered keywords with published text information and calculates the degree of match. Natural language processing technology analyzes the meaning and context of the text and checks the published text information. Thus, by using text matching technology, the publication of text information can be accurately checked.

[0032] The registration unit can encrypt and store the user's personal information using a specific encryption technology. Examples of specific encryption technologies include, but are not limited to, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and SHA (Secure Hash Algorithm). The registration unit can encrypt and store the user's personal information using, for example, AES. The registration unit can also encrypt the personal information using RSA. For example, AES is a symmetric key encryption method that provides fast and secure encryption. RSA is a public key encryption method that provides high security. SHA is used to verify the integrity of data using a hash function. As a result, encrypting and storing personal information improves the security of the information.

[0033] The check unit can check specific information published on the SNS in real time. Specific definitions and criteria for real time include, but are not limited to, the acceptable range of delay time and update frequency. The check unit, for example, checks information published on the SNS in real time. For example, the check unit instantly analyzes the published information and compares it with registered personal information. The check unit can also periodically update the published information and check it in real time. For example, the check unit updates the information every second and checks the published information. By checking information in real time, it is possible to immediately block publication.

[0034] The blocking unit can notify the user of specific information that has been blocked from publication. Specific notification methods and means include, but are not limited to, email notification, in-app notification, SMS notification, and the like. For example, the blocking unit can notify the user of the information that has been blocked from publication by email. The blocking unit can also notify the user using an in-app notification. For example, the blocking unit can immediately notify the user of the information that has been blocked from publication, allowing the user to understand the situation. Furthermore, the blocking unit can notify the user using an SMS notification. For example, the blocking unit can quickly notify the user of the information that has been blocked from publication by sending an SMS to the user. In this way, by notifying the user of the information that has been blocked from publication, the user can understand the situation.

[0035] The registration unit can analyze the user's past registration history and select an appropriate registration method. Specific criteria and selection methods for an appropriate registration method include, but are not limited to, the user's input history and frequency of use. The registration unit, for example, analyzes the user's past registration history and selects the optimal registration method. For example, if the user has preferred text input in the past, the registration unit can preferentially suggest text input. Also, if the user has used voice input in the past, the registration unit can recommend voice input. Furthermore, if the user has frequently used image upload in the past, the registration unit provides an interface that makes it easy to upload images. In this way, the optimal registration method can be provided to the user by analyzing the past registration history.

[0036] When registering personal information, the registration unit can perform specific filtering based on the user's current living situation and areas of interest. Specific criteria and methods for specific filtering include, but are not limited to, keyword filtering and category filtering. The registration unit performs filtering based on the user's current living situation and areas of interest, for example. For example, if the user is a student, school-related information can be registered with priority. Also, if the user is working, workplace-related information can be registered with priority. Furthermore, if the user is traveling, information about the travel destination can be registered with priority. In this way, appropriate information can be registered by filtering based on the user's living situation and areas of interest.

[0037] When registering personal information, the registration unit can select an appropriate registration means depending on the user's input method. Specific types and selection criteria for appropriate registration means include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the registration unit registers the personal information using voice recognition technology. Also, if the user selects text input, the registration unit can provide a text box for inputting personal information. Furthermore, if the user selects image upload, the registration unit extracts the personal information using image recognition technology. This improves registration efficiency by selecting the optimal registration means depending on the user's input method.

[0038] When registering personal information, the registration unit can prioritize registering specific information that is highly relevant by taking into account the user's geographical location information. Specific information includes, for example, geographically nearby information and related event information, but is not limited to these examples. For example, when the user is at home, the registration unit can prioritize registering information related to the home. Furthermore, when the user is at work, the registration unit can prioritize registering information related to the work. Furthermore, when the user is traveling, the registration unit prioritizes registering information related to the travel destination. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0039] When registering personal information, the registration unit can analyze the user's social media activity and register specific related information. Specific information includes, but is not limited to, past postings and related topics, for example. The registration unit can, for example, analyze the content that the user frequently posts on social media and register related personal information. The registration unit can also analyze the user's friendships on social media and register related personal information. Furthermore, the registration unit can analyze the user's social media activity time and suggest the optimal timing for registration. This allows related information to be appropriately registered by analyzing social media activity.

[0040] The registration unit can customize a specific registration method by reflecting the user's past feedback when registering personal information. Examples of specific registration methods include, but are not limited to, customizing an input form or changing the registration procedure. For example, the registration unit can simplify the registration procedure based on feedback provided by the user in the past. The registration unit can also improve a registration method that the user has previously expressed dissatisfaction with. Furthermore, the registration unit preferentially provides a registration method that the user has previously expressed favorably. In this way, by reflecting past feedback, the optimal registration method can be provided to the user.

[0041] The checking unit can improve the accuracy of the check by taking into account the interrelationships of specific information to be made public. Examples of specific information include, but are not limited to, related posts and common tags. For example, the checking unit can check whether the information to be made public is consistent with other information. The checking unit can also check whether the information to be made public is related to other information. Furthermore, the checking unit can check whether the information to be made public is consistent with other information. This improves the accuracy of the check by taking into account the interrelationships of information.

[0042] When checking, the checking unit can take into consideration the attribute information of the submitter of the specific information to be made public. The specific information includes, but is not limited to, for example, age, gender, and occupation. For example, the checking unit can conduct strict checks if the submitter is a minor. The checking unit can also conduct simplified checks if the submitter is elderly. Furthermore, if the submitter is engaged in a specific occupation, the checking unit can prioritize checking information related to that occupation. This allows for appropriate checks by taking into consideration the submitter's attribute information.

[0043] When checking, the checking unit can weight the check based on the submission frequency of specific information to be made public. The specific information includes, for example, the number of past submissions and the submission interval, but is not limited to these examples. For example, the checking unit performs a detailed check on information that is submitted frequently. The checking unit can also perform a simplified check on information that is submitted infrequently. Furthermore, the checking unit performs a balanced check on information that is submitted at a medium frequency. In this way, by weighting based on the submission frequency, appropriate checks can be performed.

[0044] When checking, the checking unit can take into account the geographical distribution of specific information to be published. Specific information includes, but is not limited to, for example, the number of posts by region and geographical relevance. For example, if the published information is concentrated in a specific region, the checking unit can prioritize checking information related to that region. Furthermore, if the published information is distributed over a wide area, the checking unit can also perform a comprehensive check. Furthermore, if the published information is related to a specific country or region, the checking unit can perform a check taking into account the regulations of that country or region. This allows for appropriate checks by taking geographical distribution into account.

[0045] During the check, the checking unit may improve the accuracy of the check by referring to related literature for specific information to be published. Specific information includes, but is not limited to, academic papers and technical reports. For example, the checking unit may check whether the published information is consistent with the specific literature. The checking unit may also check whether the published information is consistent with related literature. Furthermore, the checking unit may check whether the published information is consistent with related literature. This improves the accuracy of the check by referring to related literature.

[0046] When checking, the checking unit can perform the check taking into consideration the market value of specific information to be made public. Examples of specific information include, but are not limited to, sales forecasts and market research data. For example, the checking unit performs a detailed check of information with high market value. The checking unit can also perform a simplified check of information with low market value. Furthermore, the checking unit performs a balanced check of information with medium market value. This allows for appropriate checks by taking market value into consideration.

[0047] When blocking, the blocking unit can analyze the user's past behavioral history and select an appropriate blocking method. Examples of appropriate blocking methods include, but are not limited to, blocking specific keywords or specific users. For example, the blocking unit blocks information if the user has attempted to publish similar information in the past. The blocking unit can also analyze trends in information published by the user in the past and block related information. Furthermore, the blocking unit selects the optimal blocking method based on feedback from information published by the user in the past. This allows the optimal blocking method to be provided by analyzing the user's past behavioral history.

[0048] When blocking, the blocking unit can customize specific blocking measures based on the user's current living situation. Specific blocking measures include, but are not limited to, notification methods and blocking scopes. For example, if the user is a student, the blocking unit may prioritize blocking education-related information. Furthermore, if the user is working, the blocking unit may prioritize blocking work-related information. Furthermore, if the user is traveling, the blocking unit may prioritize blocking travel destination-related information. Thus, by customizing the blocking measures based on living situations, appropriate blocking can be performed.

[0049] When blocking, the blocking unit may improve a specific blocking method by reflecting user feedback. Specific blocking methods include, but are not limited to, methods for reflecting feedback and adjustments to blocking criteria. For example, the blocking unit may simplify the blocking procedure based on feedback previously provided by the user. The blocking unit may also improve blocking methods that users have previously expressed dissatisfaction with. Furthermore, the blocking unit may prioritize blocking methods that users have previously expressed favorable feedback. This allows the blocking method to be improved by reflecting feedback.

[0050] When blocking, the blocking unit can select an appropriate blocking method by taking into account the user's geographical location information. Suitable blocking methods include, but are not limited to, blocking specific keywords or specific users. For example, when the user is at home, the blocking unit can prioritize blocking information related to the home. Also, when the user is at work, the blocking unit can prioritize blocking information related to the work. Furthermore, when the user is traveling, the blocking unit prioritizes blocking information related to the travel destination. This makes it possible to provide an optimal blocking method by taking into account the geographical location information.

[0051] When blocking, the blocking unit can analyze the user's social media activity and suggest specific blocking measures. Specific blocking measures include, but are not limited to, notification methods and the scope of blocking. For example, the blocking unit can analyze the content frequently posted by the user on social media and block related information. The blocking unit can also analyze the user's friendships on social media and block related information. Furthermore, the blocking unit can analyze the user's social media activity time and suggest optimal blocking measures. In this way, appropriate blocking measures can be suggested by analyzing social media activity.

[0052] When blocking, the blocking unit can customize a specific blocking method by reflecting the user's past feedback. Specific blocking methods include, but are not limited to, methods for reflecting feedback and adjustments to blocking criteria. For example, the blocking unit can simplify the blocking procedure based on feedback provided by the user in the past. The blocking unit can also improve blocking methods that the user has previously expressed dissatisfaction with. Furthermore, the blocking unit prioritizes providing blocking methods that the user has previously expressed favorable feedback about. This allows the optimal blocking method to be provided to the user by reflecting past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The checking unit can analyze a user's past posting history and detect specific patterns. For example, if a user frequently used a specific keyword in the past, posts containing that keyword can be checked with priority. Also, if a user posted during a specific time period in the past, posts made during that time period can be checked with priority. Furthermore, if a user posted a lot of information in a specific category in the past, information related to that category can be checked with priority. In this way, by analyzing past posting history, the accuracy of the check can be improved.

[0055] The checking unit can take into account the reliability of the source to evaluate the reliability of information published on SNS. For example, information from highly reliable sources is checked in detail, while information from less reliable sources is checked more simply. The unit can also determine the reliability of information based on the source's past reliability evaluation. Furthermore, if the source is an official institution or a certified organization, the information is checked with priority. By taking the reliability of the source into account, the reliability of the information published can be improved.

[0056] The checking unit can analyze the content of information published on SNS and prioritize checking information related to specific topics. For example, it can prioritize checking information related to currently trending topics. It can also focus on checking information related to topics that users are interested in. It can also prioritize checking information related to specific events or campaigns. By prioritizing checking information related to topics, it is possible to prevent important information from being made public.

[0057] The checking unit can analyze the visual elements of information posted on social media and detect specific patterns. For example, it can analyze the content of images and videos to check whether they contain specific faces or objects. It can also analyze the color and composition of images and videos to determine whether they relate to a specific theme. It can also analyze the metadata of images and videos to confirm the location and time of their capture. This allows for the accuracy of posted information to be improved by analyzing visual elements.

[0058] The checking unit can analyze the language of information published on SNS and prioritize checking information related to a specific language. For example, if a user uses a specific language, it can prioritize checking information related to that language. It can also focus on checking information related to languages ​​used in a specific region or country. It can also prioritize checking languages ​​related to specific events or campaigns. By prioritizing checking information related to a language, it is possible to prevent important information from being made public.

[0059] The checker can analyze the audio of information posted on social media and detect specific patterns. For example, it can analyze the tone and speed of the audio to check whether it contains a specific emotion. It can also analyze the content of the audio to determine whether it contains specific keywords. It can also analyze the background sounds of the audio to check whether they are related to a specific environment. This allows the accuracy of the information posted to be improved by analyzing the audio.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The registration unit registers the user's personal information. This personal information includes name, address, phone number, email address, and face photo. The personal information entered by the user is stored in a database, and may be stored using encryption technology (e.g., AES or RSA). Step 2: The checking unit checks specific information published on the SNS based on the personal information registered in the registration unit. The checking unit checks facial photos and text information published on the SNS using facial recognition technology and text matching technology (for example, deep learning-based facial recognition technology and keyword matching technology). Step 3: If the specific information checked by the checking unit matches the registered personal information, the blocking unit blocks the disclosure of that information. The blocking unit notifies the user of the information that has been blocked from disclosure (for example, by email, in-app notification, SMS notification, etc.).

[0062] (Example 2) A system according to an embodiment of the present invention prevents the erroneous disclosure of personal information, facial photos, and other information on social networking sites (SNSs). This system allows users to register their personal information, checks the information to be published on the SNS in advance, and blocks the information from being published if it matches the registered personal information. For example, when a user attempts to upload a facial photo to the SNS, the system compares the facial photo with pre-registered facial photos and blocks the publication if it matches. Similarly, when a user attempts to publish their name or address, the system compares the photo with pre-registered information and blocks the publication if it matches. This mechanism prevents users from accidentally disclosing personal information and prevents the leakage of personal information. It also allows even young users to use the SNS with peace of mind. This system pre-registers users' personal information and prevents the erroneous disclosure of personal information on the SNS. For example, when a user attempts to upload a facial photo to the SNS, the system compares the facial photo with pre-registered facial photos and blocks the publication if it matches. Similarly, when a user attempts to publish their name or address, the system compares the photo with pre-registered information and blocks the publication if it matches. This prevents users from accidentally disclosing personal information and prevents the leakage of personal information. In addition, even young users can use SNS with peace of mind.

[0063] An information disclosure prevention system according to an embodiment includes a registration unit, a check unit, and a block unit. The registration unit registers personal information of a user. The personal information includes, but is not limited to, for example, a name, address, telephone number, email address, and facial photograph. The registration unit, for example, stores the personal information entered by the user in a database. The registration unit can also encrypt and store the personal information of the user. For example, encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) are used. The check unit checks specific information to be disclosed on an SNS based on the personal information registered in the registration unit. For example, the check unit checks facial photographs to be disclosed on the SNS using facial recognition technology. The check unit can also check text information to be disclosed on the SNS using text matching technology. For example, deep learning-based facial recognition technology or keyword matching technology is used. The block unit blocks the disclosure of information if the specific information checked by the check unit matches the registered personal information. For example, the block unit notifies the user of the information whose disclosure has been blocked. For example, the information disclosure prevention system according to the embodiment uses methods such as email notification, in-app notification, SMS notification, etc. In this way, the information disclosure prevention system according to the embodiment can register the personal information of the user in advance and prevent the personal information from being erroneously disclosed on the SNS.

[0064] The check unit can check facial photos posted on SNS using a specific facial recognition technology. Specific facial recognition technologies include, but are not limited to, deep learning-based facial recognition technology and template matching technology. The check unit can check facial photos posted on SNS using, for example, deep learning-based facial recognition technology. The check unit can also check facial photos using template matching technology. For example, deep learning-based facial recognition technology learns a large amount of facial image data and performs highly accurate facial recognition. Template matching technology compares a pre-registered facial image with a publicly posted facial image and calculates the degree of match. This allows the use of facial recognition technology to accurately check the posting of facial photos.

[0065] The check unit may check text information published on the SNS using a specific text matching technology. Specific text matching technologies include, but are not limited to, keyword matching technology and natural language processing technology. For example, the check unit may check text information published on the SNS using keyword matching technology. The check unit may also check text information using natural language processing technology. For example, keyword matching technology compares pre-registered keywords with published text information and calculates the degree of match. Natural language processing technology analyzes the meaning and context of the text and checks the published text information. Thus, by using text matching technology, the publication of text information can be accurately checked.

[0066] The registration unit can encrypt and store the user's personal information using a specific encryption technology. Examples of specific encryption technologies include, but are not limited to, AES (Advanced Encryption Standard), RSA (Rivest-Shamir-Adleman), and SHA (Secure Hash Algorithm). The registration unit can encrypt and store the user's personal information using, for example, AES. The registration unit can also encrypt the personal information using RSA. For example, AES is a symmetric key encryption method that provides fast and secure encryption. RSA is a public key encryption method that provides high security. SHA is used to verify the integrity of data using a hash function. As a result, encrypting and storing personal information improves the security of the information.

[0067] The check unit can check specific information published on the SNS in real time. Specific definitions and criteria for real time include, but are not limited to, the acceptable range of delay time and update frequency. The check unit, for example, checks information published on the SNS in real time. For example, the check unit instantly analyzes the published information and compares it with registered personal information. The check unit can also periodically update the published information and check it in real time. For example, the check unit updates the information every second and checks the published information. By checking information in real time, it is possible to immediately block publication.

[0068] The blocking unit can notify the user of specific information that has been blocked from publication. Specific notification methods and means include, but are not limited to, email notification, in-app notification, SMS notification, and the like. For example, the blocking unit can notify the user of the information that has been blocked from publication by email. The blocking unit can also notify the user using an in-app notification. For example, the blocking unit can immediately notify the user of the information that has been blocked from publication, allowing the user to understand the situation. Furthermore, the blocking unit can notify the user using an SMS notification. For example, the blocking unit can quickly notify the user of the information that has been blocked from publication by sending an SMS to the user. In this way, by notifying the user of the information that has been blocked from publication, the user can understand the situation.

[0069] An information disclosure prevention system according to an embodiment includes a registration unit, a check unit, and a block unit. The registration unit estimates a user's emotions using a specific method and adjusts the timing of personal information registration based on the estimated user emotions. The registration unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the registration unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The registration unit can also estimate the user's emotions using voice analysis technology. For example, the registration unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the registration unit can also estimate the user's emotions using text analysis technology. For example, the registration unit analyzes text entered by the user to estimate the emotions. The registration unit adjusts the timing of personal information registration based on the estimated user emotions. For example, if the user is relaxed, the system sends a notification prompting the user to register personal information. Furthermore, if the user is feeling stressed, the system suggests postponing registration. Furthermore, if the user is concentrating, the system simplifies and quickly completes the registration procedure. This improves registration efficiency by adjusting the registration timing according to the user's emotions.

[0070] The registration unit can analyze the user's past registration history and select an appropriate registration method. Specific criteria and selection methods for an appropriate registration method include, but are not limited to, the user's input history and frequency of use. The registration unit, for example, analyzes the user's past registration history and selects the optimal registration method. For example, if the user has preferred text input in the past, the registration unit can preferentially suggest text input. Also, if the user has used voice input in the past, the registration unit can recommend voice input. Furthermore, if the user has frequently used image upload in the past, the registration unit provides an interface that makes it easy to upload images. In this way, the optimal registration method can be provided to the user by analyzing the past registration history.

[0071] When registering personal information, the registration unit can perform specific filtering based on the user's current living situation and areas of interest. Specific criteria and methods for specific filtering include, but are not limited to, keyword filtering and category filtering. The registration unit performs filtering based on the user's current living situation and areas of interest, for example. For example, if the user is a student, school-related information can be registered with priority. Also, if the user is working, workplace-related information can be registered with priority. Furthermore, if the user is traveling, information about the travel destination can be registered with priority. In this way, appropriate information can be registered by filtering based on the user's living situation and areas of interest.

[0072] When registering personal information, the registration unit can select an appropriate registration means depending on the user's input method. Specific types and selection criteria for appropriate registration means include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the registration unit registers the personal information using voice recognition technology. Also, if the user selects text input, the registration unit can provide a text box for inputting personal information. Furthermore, if the user selects image upload, the registration unit extracts the personal information using image recognition technology. This improves registration efficiency by selecting the optimal registration means depending on the user's input method.

[0073] The registration unit may estimate the user's emotion using a specific method and prioritize the personal information to be registered based on the estimated user's emotion. Specific methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. The registration unit may estimate the user's emotion using, for example, facial expression recognition technology. For example, the registration unit may capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The registration unit may also estimate the user's emotion using voice analysis technology. For example, the registration unit may analyze the tone and speed of the user's voice to estimate the emotion. The registration unit may also estimate the user's emotion using text analysis technology. For example, the registration unit may analyze text entered by the user to estimate the emotion. The registration unit may prioritize the personal information to be registered based on the estimated user's emotion. For example, if the user is relaxed, detailed personal information may be registered. If the user is in a hurry, only minimal personal information may be registered. If the user is excited, important personal information may be registered. This allows important information to be registered with priority by determining the priority of personal information according to the user's feelings.

[0074] When registering personal information, the registration unit can prioritize registering specific information that is highly relevant by taking into account the user's geographical location information. Specific information includes, for example, geographically nearby information and related event information, but is not limited to these examples. For example, when the user is at home, the registration unit can prioritize registering information related to the home. Furthermore, when the user is at work, the registration unit can prioritize registering information related to the work. Furthermore, when the user is traveling, the registration unit prioritizes registering information related to the travel destination. In this way, highly relevant information can be prioritized by taking into account the geographical location information.

[0075] When registering personal information, the registration unit can analyze the user's social media activity and register specific related information. Specific information includes, but is not limited to, past postings and related topics, for example. The registration unit can, for example, analyze the content that the user frequently posts on social media and register related personal information. The registration unit can also analyze the user's friendships on social media and register related personal information. Furthermore, the registration unit can analyze the user's social media activity time and suggest the optimal timing for registration. This allows related information to be appropriately registered by analyzing social media activity.

[0076] The registration unit can customize a specific registration method by reflecting the user's past feedback when registering personal information. Examples of specific registration methods include, but are not limited to, customizing an input form or changing the registration procedure. For example, the registration unit can simplify the registration procedure based on feedback provided by the user in the past. The registration unit can also improve a registration method that the user has previously expressed dissatisfaction with. Furthermore, the registration unit preferentially provides a registration method that the user has previously expressed favorably. In this way, by reflecting past feedback, the optimal registration method can be provided to the user.

[0077] The check unit can estimate the user's emotions using a specific method and adjust the check criteria based on the estimated user's emotions. Specific methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the check unit estimates the user's emotions using facial expression recognition technology. For example, the check unit captures the user's facial expressions with a camera and estimates the emotions using a facial expression recognition algorithm. The check unit can also estimate the user's emotions using voice analysis technology. For example, the check unit analyzes the tone and speed of the user's voice to estimate the emotions. Furthermore, the check unit can estimate the user's emotions using text analysis technology. For example, the check unit analyzes text entered by the user to estimate the emotions. The check unit adjusts the check criteria based on the estimated user's emotions. For example, if the user is relaxed, a detailed check is performed. If the user is in a hurry, a simplified check is performed. Furthermore, if the user is stressed, the frequency of checks is reduced. In this way, the check criteria can be adjusted according to the user's emotions, allowing for appropriate checks.

[0078] The checking unit can improve the accuracy of the check by taking into account the interrelationships of specific information to be made public. Examples of specific information include, but are not limited to, related posts and common tags. For example, the checking unit can check whether the information to be made public is consistent with other information. The checking unit can also check whether the information to be made public is related to other information. Furthermore, the checking unit can check whether the information to be made public is consistent with other information. This improves the accuracy of the check by taking into account the interrelationships of information.

[0079] When checking, the checking unit can take into consideration the attribute information of the submitter of the specific information to be made public. The specific information includes, but is not limited to, for example, age, gender, and occupation. For example, the checking unit can conduct strict checks if the submitter is a minor. The checking unit can also conduct simplified checks if the submitter is elderly. Furthermore, if the submitter is engaged in a specific occupation, the checking unit can prioritize checking information related to that occupation. This allows for appropriate checks by taking into consideration the submitter's attribute information.

[0080] When checking, the checking unit can weight the check based on the submission frequency of specific information to be made public. The specific information includes, for example, the number of past submissions and the submission interval, but is not limited to these examples. For example, the checking unit performs a detailed check on information that is submitted frequently. The checking unit can also perform a simplified check on information that is submitted infrequently. Furthermore, the checking unit performs a balanced check on information that is submitted at a medium frequency. In this way, by weighting based on the submission frequency, appropriate checks can be performed.

[0081] The check unit can estimate the user's emotions using a specific method and adjust the order in which the check results are displayed based on the estimated user's emotions. Specific methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the check unit can estimate the user's emotions using facial expression recognition technology. For example, the check unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The check unit can also estimate the user's emotions using voice analysis technology. For example, the check unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the check unit can estimate the user's emotions using text analysis technology. For example, the check unit can analyze text entered by the user to estimate the emotions. The check unit adjusts the order in which the check results are displayed based on the estimated user's emotions. For example, if the user is relaxed, detailed results can be displayed preferentially. If the user is in a hurry, results that focus on the main points can be displayed preferentially. Furthermore, if the user is stressed, simplified results can be displayed preferentially. This allows the display order of results to be adjusted according to the user's emotions, thereby providing appropriate information.

[0082] When checking, the checking unit can take into account the geographical distribution of specific information to be published. Specific information includes, but is not limited to, for example, the number of posts by region and geographical relevance. For example, if the published information is concentrated in a specific region, the checking unit can prioritize checking information related to that region. Furthermore, if the published information is distributed over a wide area, the checking unit can also perform a comprehensive check. Furthermore, if the published information is related to a specific country or region, the checking unit can perform a check taking into account the regulations of that country or region. This allows for appropriate checks by taking geographical distribution into account.

[0083] During the check, the checking unit may improve the accuracy of the check by referring to related literature for specific information to be published. Specific information includes, but is not limited to, academic papers and technical reports. For example, the checking unit may check whether the published information is consistent with the specific literature. The checking unit may also check whether the published information is consistent with related literature. Furthermore, the checking unit may check whether the published information is consistent with related literature. This improves the accuracy of the check by referring to related literature.

[0084] When checking, the checking unit can perform the check taking into consideration the market value of specific information to be made public. Examples of specific information include, but are not limited to, sales forecasts and market research data. For example, the checking unit performs a detailed check of information with high market value. The checking unit can also perform a simplified check of information with low market value. Furthermore, the checking unit performs a balanced check of information with medium market value. This allows for appropriate checks by taking market value into consideration.

[0085] The blocking unit can estimate the user's emotions using a specific method and adjust the blocking method based on the estimated user's emotions. Specific methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the blocking unit can estimate the user's emotions using facial expression recognition technology. For example, the blocking unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The blocking unit can also estimate the user's emotions using voice analysis technology. For example, the blocking unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the blocking unit can estimate the user's emotions using text analysis technology. For example, the blocking unit can analyze text entered by the user to estimate the emotions. The blocking unit can adjust the blocking method based on the estimated user's emotions. For example, if the user is relaxed, the blocking unit can block the user by explaining a detailed reason. If the user is in a hurry, the blocking unit can block the user by explaining a concise reason. Furthermore, if the user is stressed, the blocking unit can explain the reason for blocking in a gentle manner. This allows the blocking method to be adjusted according to the user's emotions, thereby enabling appropriate blocking.

[0086] When blocking, the blocking unit can analyze the user's past behavioral history and select an appropriate blocking method. Examples of appropriate blocking methods include, but are not limited to, blocking specific keywords or specific users. For example, the blocking unit blocks information if the user has attempted to publish similar information in the past. The blocking unit can also analyze trends in information published by the user in the past and block related information. Furthermore, the blocking unit selects the optimal blocking method based on feedback from information published by the user in the past. This allows the optimal blocking method to be provided by analyzing the user's past behavioral history.

[0087] When blocking, the blocking unit can customize specific blocking measures based on the user's current living situation. Specific blocking measures include, but are not limited to, notification methods and blocking scopes. For example, if the user is a student, the blocking unit may prioritize blocking education-related information. Furthermore, if the user is working, the blocking unit may prioritize blocking work-related information. Furthermore, if the user is traveling, the blocking unit may prioritize blocking travel destination-related information. Thus, by customizing the blocking measures based on living situations, appropriate blocking can be performed.

[0088] When blocking, the blocking unit may improve a specific blocking method by reflecting user feedback. Specific blocking methods include, but are not limited to, methods for reflecting feedback and adjustments to blocking criteria. For example, the blocking unit may simplify the blocking procedure based on feedback previously provided by the user. The blocking unit may also improve blocking methods that users have previously expressed dissatisfaction with. Furthermore, the blocking unit may prioritize blocking methods that users have previously expressed favorable feedback. This allows the blocking method to be improved by reflecting feedback.

[0089] The blocking unit may estimate the user's emotions using a specific method and determine the priority of blocking based on the estimated user's emotions. Specific methods include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the blocking unit may estimate the user's emotions using facial expression recognition technology. For example, the blocking unit may capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The blocking unit may also estimate the user's emotions using voice analysis technology. For example, the blocking unit may analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the blocking unit may use text analysis technology to estimate the user's emotions. For example, the blocking unit may analyze text entered by the user to estimate the emotions. The blocking unit may determine the priority of blocking based on the estimated user's emotions. For example, if the user is relaxed, detailed information may be blocked first. If the user is in a hurry, only important information may be blocked first. Furthermore, if the user is excited, important information may be blocked first. Thus, by determining the priority of blocking based on the user's emotions, important information can be blocked first.

[0090] When blocking, the blocking unit can select an appropriate blocking method by taking into account the user's geographical location information. Suitable blocking methods include, but are not limited to, blocking specific keywords or specific users. For example, when the user is at home, the blocking unit can prioritize blocking information related to the home. Also, when the user is at work, the blocking unit can prioritize blocking information related to the work. Furthermore, when the user is traveling, the blocking unit prioritizes blocking information related to the travel destination. This makes it possible to provide an optimal blocking method by taking into account the geographical location information.

[0091] When blocking, the blocking unit can analyze the user's social media activity and suggest specific blocking measures. Specific blocking measures include, but are not limited to, notification methods and the scope of blocking. For example, the blocking unit can analyze the content frequently posted by the user on social media and block related information. The blocking unit can also analyze the user's friendships on social media and block related information. Furthermore, the blocking unit can analyze the user's social media activity time and suggest optimal blocking measures. In this way, appropriate blocking measures can be suggested by analyzing social media activity.

[0092] When blocking, the blocking unit can customize a specific blocking method by reflecting the user's past feedback. Specific blocking methods include, but are not limited to, methods for reflecting feedback and adjustments to blocking criteria. For example, the blocking unit can simplify the blocking procedure based on feedback provided by the user in the past. The blocking unit can also improve blocking methods that the user has previously expressed dissatisfaction with. Furthermore, the blocking unit prioritizes providing blocking methods that the user has previously expressed favorable feedback about. This allows the optimal blocking method to be provided to the user by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, check unit, and block unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14 and stores the user's personal information in the database 24. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares information to be published on the SNS with previously registered personal information. The block unit is realized, for example, by the control unit 46A of the smart device 14 and notifies the user of the information that has been blocked from publication. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, check unit, and block unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214 and stores the user's personal information in the database 24. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares information to be published on the SNS with previously registered personal information. The block unit is realized, for example, by the control unit 46A of the smart glasses 214 and notifies the user of the information that has been blocked from publication. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, check unit, and block unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314 and stores the user's personal information in the database 24. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and compares information to be made public on the SNS with personal information registered in advance. The block unit is realized, for example, by the control unit 46A of the headset type terminal 314 and notifies the user of information that has been blocked from publication. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, check unit, and block unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414 and stores the user's personal information in the database 24. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and collates information to be made public on the SNS with personal information registered in advance. The block unit is realized, for example, by the control unit 46A of the robot 414 and notifies the user of the information that has been blocked from being made public.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The checking unit can analyze a user's past posting history and detect specific patterns. For example, if a user frequently used a specific keyword in the past, posts containing that keyword can be checked with priority. Also, if a user posted during a specific time period in the past, posts made during that time period can be checked with priority. Furthermore, if a user posted a lot of information in a specific category in the past, information related to that category can be checked with priority. In this way, by analyzing past posting history, the accuracy of the check can be improved.

[0095] The checking unit can take into account the reliability of the source to evaluate the reliability of information published on SNS. For example, information from highly reliable sources is checked in detail, while information from less reliable sources is checked more simply. The unit can also determine the reliability of information based on the source's past reliability evaluation. Furthermore, if the source is an official institution or a certified organization, the information is checked with priority. By taking the reliability of the source into account, the reliability of the information published can be improved.

[0096] The check unit can estimate the user's emotions and adjust the timing of checks based on the estimated emotions. For example, if the user is relaxed, the timing for a detailed check can be selected. Also, if the user is feeling stressed, the check can be postponed. Furthermore, if the user is concentrating, the check can be performed quickly, reducing the burden on the user. This allows for efficient checks by adjusting the timing of checks according to the user's emotions.

[0097] The checking unit can analyze the content of information published on SNS and prioritize checking information related to specific topics. For example, it can prioritize checking information related to currently trending topics. It can also focus on checking information related to topics that users are interested in. It can also prioritize checking information related to specific events or campaigns. By prioritizing checking information related to topics, it is possible to prevent important information from being made public.

[0098] The check unit can estimate the user's emotions and customize the check method based on the estimated emotions. For example, if the user is relaxed, a detailed check can be performed. If the user is in a hurry, a simplified check can be performed. Furthermore, if the user is feeling stressed, the frequency of checks can be reduced. In this way, the check method can be customized according to the user's emotions, allowing for appropriate checks.

[0099] The checking unit can analyze the visual elements of information posted on social media and detect specific patterns. For example, it can analyze the content of images and videos to check whether they contain specific faces or objects. It can also analyze the color and composition of images and videos to determine whether they relate to a specific theme. It can also analyze the metadata of images and videos to confirm the location and time of their capture. This allows for the accuracy of posted information to be improved by analyzing visual elements.

[0100] The check unit can estimate the user's emotions and adjust the way the check results are displayed based on the estimated emotions. For example, if the user is relaxed, detailed results can be displayed. If the user is in a hurry, results that focus on the main points can be displayed. Furthermore, if the user is stressed, simplified results can be displayed. In this way, appropriate information can be provided by adjusting the way the results are displayed according to the user's emotions.

[0101] The checking unit can analyze the language of information published on SNS and prioritize checking information related to a specific language. For example, if a user uses a specific language, it can prioritize checking information related to that language. It can also focus on checking information related to languages ​​used in a specific region or country. It can also prioritize checking languages ​​related to specific events or campaigns. By prioritizing checking information related to a language, it is possible to prevent important information from being made public.

[0102] The checker can analyze the audio of information posted on social media and detect specific patterns. For example, it can analyze the tone and speed of the audio to check whether it contains a specific emotion. It can also analyze the content of the audio to determine whether it contains specific keywords. It can also analyze the background sounds of the audio to check whether they are related to a specific environment. This allows the accuracy of the information posted to be improved by analyzing the audio.

[0103] The check unit can estimate the user's emotions and determine the priority of checks based on the estimated emotions. For example, if the user is relaxed, detailed information can be checked with priority. Also, if the user is in a hurry, only important information can be checked with priority. Furthermore, if the user is excited, important information can be checked with priority. In this way, by determining the priority of checks according to the user's emotions, important information can be checked with priority.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The registration unit registers the user's personal information. This personal information includes name, address, phone number, email address, and face photo. The personal information entered by the user is stored in a database, and may be stored using encryption technology (e.g., AES or RSA). Step 2: The checking unit checks specific information published on the SNS based on the personal information registered in the registration unit. The checking unit checks facial photos and text information published on the SNS using facial recognition technology and text matching technology (for example, deep learning-based facial recognition technology and keyword matching technology). Step 3: If the specific information checked by the checking unit matches the registered personal information, the blocking unit blocks the disclosure of that information. The blocking unit notifies the user of the information that has been blocked from disclosure (for example, by email, in-app notification, SMS notification, etc.).

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 registration unit for registering personal information of users; A check unit that checks specific information to be made public on the SNS based on the personal information registered in the registration unit; a blocking unit that blocks the disclosure of the information when the specific information checked by the checking unit matches the registered personal information. A system characterized by:

2. The checking unit Use specific facial recognition technology to check photos of people's faces published on social media 2. The system of claim 1.

3. The checking unit Checking text information published on social media using specific text matching technology 2. The system of claim 1.

4. The registration unit We use specific encryption technology to encrypt and store your personal information.

2. The system of claim 1.

5. The checking unit Check specific information published on social media in real time 2. The system of claim 1.

6. The block portion is Notify users about specific information that you have blocked from being published 2. The system of claim 1.

7. The registration unit The system estimates a user's emotions using a specific method and adjusts the timing of registering personal information based on the estimated user emotions.

2. The system of claim 1.

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

Images