System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine whether a presented confirmation document is genuine or counterfeit.SOLUTION: A system according to an embodiment includes an image acquisition unit, an authenticity determination unit, and a result output unit. The image acquisition unit acquires image data of a confirmation document. The authenticity determination unit analyzes the image acquired by the image acquiring unit using the generated AI to determine whether the object is genuine or counterfeit. The result output unit outputs a determination result determined by the authenticity determination unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and accurately determine whether a presented verification document is genuine or counterfeit.

[0005] The system according to the embodiment aims to quickly and accurately determine whether a presented verification document is genuine or counterfeit. [Means for solving the problem]

[0006] The system according to the embodiment includes an image acquisition unit, an authenticity determination unit, and a result output unit. The image acquisition unit acquires image data of the verification document. The authenticity determination unit analyzes the image data acquired by the image acquisition unit using a generation AI to determine whether the document is genuine or a counterfeit. The result output unit outputs the determination result made by the authenticity determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine whether a presented verification document is genuine or counterfeit. [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) The identity verification system according to the embodiment of the present invention acquires image data of a verification document, analyzes it using a generation AI, and determines whether it is genuine or forged. This identity verification system prevents fraudulent contracts and can be offered to other companies and organizations as a new business opportunity.

[0029] The identity verification system according to the embodiment includes an image acquisition unit, an authenticity determination unit, and a result output unit. The image acquisition unit acquires image data of a verification document. For example, the image of the verification document is taken using a smartphone camera. Alternatively, the image of the verification document can be acquired using a scanner. Furthermore, the image acquisition unit can directly acquire image data of a verification document submitted in digital format. The authenticity determination unit analyzes the image data acquired by the image acquisition unit using a generation AI to determine whether the document is genuine or forged. For example, the generation AI analyzes the image data using a text generation AI (e.g., LLM). Alternatively, the generation AI can analyze the features of the image data using a multimodal generation AI. Alternatively, the generation AI can analyze the pixel-level features of the image data to determine authenticity. The result output unit outputs the determination result determined by the authenticity determination unit. For example, the result output unit displays the determination result on a screen. Alternatively, the result output unit can store the determination result in a database. Alternatively, the result output unit can transmit the determination result to another system. As a result, the identity verification system according to the embodiment can automatically determine the authenticity of verification documents and prevent fraudulent contracts. For example, the output unit displays the determination results via a web application or a mobile application. If feedback on paper is desired, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the applicant or related parties.

[0030] The image acquisition unit can photograph the verification document from multiple angles, generate a 3D model, and analyze the three-dimensional features of the verification document. The image acquisition unit, for example, uses a smartphone camera to photograph the verification document from multiple angles and generate a 3D model. For example, a driver's license is photographed from above, below, left, and right, and the three-dimensional features are analyzed. The image acquisition unit can also scan the verification document from multiple angles using a scanner and generate a 3D model. For example, a My Number card is scanned from an angle and the three-dimensional features are analyzed. The image acquisition unit can also use a turntable to photograph the verification document while rotating it, and generate a 3D model. For example, a driver's license is placed on the turntable and photographed while rotating it 360 degrees. This improves the accuracy of authenticity determination by analyzing the three-dimensional features of the verification document.

[0031] The image acquisition unit can detect the material of the confirmation document and the presence or absence of a hologram by utilizing light reflection and transparency. The image acquisition unit detects the material of the confirmation document by, for example, utilizing light reflection. For example, it shines light on the surface of a driver's license and analyzes the reflection pattern. The image acquisition unit also detects the presence or absence of a hologram on the confirmation document by utilizing transparency. For example, it holds a My Number card up to the light to confirm the presence of a hologram. The image acquisition unit also detects the material of the confirmation document and the hologram using light of different wavelengths. For example, it analyzes the characteristics of a driver's license using ultraviolet or infrared light. This improves the accuracy of authenticity determination by detecting the material of the confirmation document and the presence or absence of a hologram.

[0032] The image acquisition unit also uses voice recognition technology, allowing the user to read the contents of the confirmation document aloud, thereby confirming a match between the voice and the image. For example, the image acquisition unit has the user read the contents of the confirmation document aloud, and analyze the voice to confirm whether it matches the image. For example, the image acquisition unit reads out the name and address on a driver's license. The image acquisition unit also uses voice recognition technology to convert the content read by the user into text and compare it with the image on the confirmation document. For example, the image acquisition unit reads out the number on a My Number card. The image acquisition unit also builds a system that confirms a match between the voice and the image in real time. For example, it instantly determines whether the content read out by the user matches the image on the confirmation document. This improves the accuracy of identity verification by confirming a match between the voice and the image.

[0033] The image acquisition unit can simultaneously perform fingerprint authentication and facial authentication, and combine multiple pieces of biometric authentication data to improve the accuracy of identity verification. The image acquisition unit, for example, simultaneously performs fingerprint authentication and compares an image of a verification document with the fingerprint data. For example, it simultaneously acquires an image of a driver's license and a fingerprint. The image acquisition unit also simultaneously performs facial authentication and compares an image of a verification document with the facial data. For example, it simultaneously acquires an image of a My Number card and a facial photograph. The image acquisition unit also combines fingerprint authentication and facial authentication to improve the accuracy of identity verification. For example, it simultaneously acquires an image of a driver's license, fingerprints, and a facial photograph, and makes a comprehensive judgment. In this way, the accuracy of identity verification is improved by combining multiple pieces of biometric authentication data.

[0034] The authenticity determination unit uses generation AI to analyze minute pixel-level features of image data and detect traces of counterfeiting. For example, the authenticity determination unit uses generation AI to analyze minute pixel-level features of image data and detect traces of counterfeiting. For example, it analyzes the minute printed patterns on a driver's license. The authenticity determination unit also uses generation AI to analyze minute changes in the color tone and brightness of image data and detect traces of counterfeiting. For example, it analyzes unnatural changes in the color tone of a My Number card. The authenticity determination unit also uses generation AI to analyze minute features of the edges and contours of image data and detect traces of counterfeiting. For example, it analyzes unnatural changes in the edges of characters on a driver's license. This allows for highly accurate detection of traces of counterfeiting by analyzing minute pixel-level features.

[0035] The authenticity determination unit can incorporate a self-learning function that allows the generation AI to learn from past determination results and continuously improve its determination accuracy. The authenticity determination unit, for example, incorporates a self-learning function that allows the generation AI to learn from past determination results and continuously improve its determination accuracy. For example, it learns based on past counterfeit determination data. The authenticity determination unit also incorporates a self-learning function so that the generation AI can adapt to new counterfeiting methods. For example, it learns new counterfeit patterns and improves its determination accuracy. The authenticity determination unit also constructs a system in which the generation AI receives feedback based on past determination results and self-learns. For example, it corrects determination errors and reflects them in the next determination. In this way, by incorporating a self-learning function, it is possible to continuously improve determination accuracy.

[0036] The authenticity determination unit can use the generation AI to develop a general-purpose authentication determination algorithm that can handle different types of confirmation documents. For example, the authenticity determination unit uses the generation AI to develop a general-purpose authentication determination algorithm that can handle different types of confirmation documents (passports, student ID cards, etc.). For example, it builds an algorithm that can authenticate both driver's licenses and passports. The authenticity determination unit also develops a system in which the generation AI learns the characteristics of different types of confirmation documents and performs general-purpose authenticity determination. For example, it builds an algorithm that can determine the authenticity of student ID cards and employee ID cards. The authenticity determination unit also introduces a general-purpose feature extraction method so that the generation AI can handle different types of confirmation documents. For example, it extracts common features from confirmation documents and uses them to determine their authenticity. This develops a general-purpose authentication determination algorithm that can handle different types of confirmation documents, improving the versatility of the system.

[0037] The authenticity determination unit allows the generation AI to analyze not only image data but also video data, and determine authenticity based on dynamic features. For example, the authenticity determination unit builds a system in which the generation AI analyzes not only image data but also video data, and determines authenticity based on dynamic features. For example, it analyzes videos taken while a confirmation document is moving. The authenticity determination unit also develops an algorithm in which the generation AI analyzes the features of each frame of video data to determine authenticity. For example, it analyzes videos of driver's licenses to detect traces of forgery. The authenticity determination unit also builds a system in which the generation AI determines authenticity based on dynamic features of video data. For example, it analyzes the movement of a hologram on a confirmation document to determine authenticity. This makes it possible to analyze video data and determine authenticity with high accuracy based on dynamic features.

[0038] When outputting the determination result, the result output unit can generate a detailed analysis report and clearly indicate which parts have been determined to be counterfeit. For example, when outputting the determination result, the result output unit generates a detailed analysis report and clearly indicates which parts have been determined to be counterfeit. For example, it may point out specific characters or hologram parts on a driver's license. The result output unit also visually indicates which parts of the image data have been determined to be counterfeit in the analysis report. For example, it may highlight the counterfeit parts in red. When outputting the determination result, the result output unit also describes in detail in the analysis report the characteristics that serve as evidence of counterfeiting. For example, it may explain the pixel-level characteristics of the counterfeit parts of a My Number card. In this way, by generating a detailed analysis report, it is possible to clearly indicate the evidence of counterfeiting.

[0039] The result output unit may incorporate a system that monitors the judgment results in real time and issues an alert if an abnormal pattern is detected. The result output unit may incorporate a system that monitors the judgment results in real time and issues an alert if an abnormal pattern is detected. For example, an alert may be issued if multiple counterfeit judgments are made in a short period of time. The result output unit may also monitor the judgment results in real time and issue an alert if an abnormal pattern is detected in a specific region or time period. For example, an alert may be issued if multiple counterfeit judgments are made in a specific region. The result output unit may also monitor the judgment results in real time and issue an alert if an abnormal pattern is detected for a specific user. For example, an alert may be issued if the same user submits multiple counterfeit documents. This allows for real-time monitoring and issuing an alert if an abnormal pattern is detected, enabling rapid response.

[0040] The result output unit can link the judgment result with other security systems to realize comprehensive security measures. For example, the result output unit links the judgment result with an access control system and restricts access if a forged document is detected. For example, it suspends system access for a user who is judged to have a forged document. The result output unit also links the judgment result with other security systems to realize comprehensive security measures. For example, it sends an alert to a surveillance camera system when a forged document is detected. The result output unit also records the judgment result in a security log and works with other security systems to detect abnormal patterns. For example, it analyzes the behavioral history of a user who is judged to have a forged document. In this way, comprehensive security measures can be realized by linking the judgment result with other security systems.

[0041] The result output unit can record the judgment results using blockchain technology and store them in a tamper-proof format. The result output unit, for example, builds a system that records the judgment results using blockchain technology and stores them in a tamper-proof format. For example, the result of a counterfeit judgment is recorded on the blockchain. The result output unit also uses blockchain technology to store the history of the judgment results in a tamper-proof format. For example, past judgment results are recorded on the blockchain so that they can be verified later. The result output unit also builds a system that records the judgment results on the blockchain and shares them with other companies and organizations. For example, the results of counterfeit judgment are shared, thereby achieving widespread deterrence of fraudulent contracts. In this way, by using blockchain technology, the judgment results can be stored in a tamper-proof format.

[0042] A database can encrypt data to be stored and strengthen security. A database, for example, builds a system that encrypts data to be stored and strengthens security. For example, it encrypts and stores image data of confirmation documents and judgment results. A database also encrypts data to be stored and sets access permissions. For example, it allows only specific users to access the data. A database also builds a system that encrypts data to be stored and performs regular security checks. For example, it manages encryption keys and applies security policies. In this way, security can be strengthened by encrypting data.

[0043] A database can record access logs in detail and monitor for signs of unauthorized access. For example, a database can build a system that records access logs in detail and monitors for signs of unauthorized access. For example, an alert can be issued if an abnormal pattern is detected in the access log. A database can also analyze access logs and develop an algorithm that detects signs of unauthorized access. For example, it can detect abnormal access from a specific IP address. A database can also build a system that regularly monitors access logs and detects signs of unauthorized access early. For example, it can detect an abnormal increase in access logs. This makes it possible to strengthen security by recording access logs in detail and monitoring for signs of unauthorized access.

[0044] Databases can be built on the cloud to ensure scalability. For example, a system can be developed to build a database on the cloud and ensure scalability. For example, cloud services such as AWS or Azure can be used. Databases can also be built on the cloud to dynamically adjust resources according to access load. For example, resources can be automatically increased when access is concentrated. Databases can also be built on the cloud to facilitate data backup and recovery. For example, data backups can be periodically saved to cloud storage. In this way, building a database on the cloud can ensure scalability.

[0045] Databases can be API-linked with other companies' systems to achieve seamless data sharing. For example, databases can be API-linked with other companies' systems to build systems that achieve seamless data sharing. For example, data can be shared with other companies' identity verification systems. Databases can also use APIs to link with other companies' systems to share data in real time. For example, the results of counterfeit detection can be instantly sent to other companies' systems. Databases can also be API-linked to allow other companies to easily obtain data. For example, API keys can be issued to allow other companies to access the database. In this way, API-linked with other companies' systems can achieve seamless data sharing.

[0046] A database can introduce a subscription model to ensure regular revenue. For example, a database can introduce a subscription model and build a system to ensure regular revenue. For example, the database can be accessed for a monthly fee. The database can also introduce a subscription model and offer pricing plans based on frequency of use. For example, different pricing plans can be set depending on the number of accesses. The database can also introduce a subscription model and develop a marketing strategy to ensure regular revenue. For example, a free trial period can be set to acquire users. In this way, regular revenue can be ensured by introducing a subscription model.

[0047] The database can introduce a billing model based on the number of uses and provide a flexible fee structure based on the frequency of use. For example, the database can introduce a billing model based on the number of uses and build a system that provides a flexible fee structure based on the frequency of use. For example, a fee is set for each use. The database can also introduce a billing model based on the number of uses and provide discounts to users who use frequently. For example, a discounted fee is applied for uses above a certain number of times. The database can also introduce a billing model based on the number of uses and provide fee plans based on the frequency of use. For example, different fee plans are set depending on the number of uses per month. In this way, by introducing a billing model based on the number of uses, a flexible fee structure based on the frequency of use can be provided.

[0048] The database can provide an identity verification agency service. For example, the database builds a system that provides an identity verification agency service. For example, it provides a service that allows other companies to request identity verification. The database also provides an identity verification outsourcing service. For example, it allows companies to outsource their own identity verification work. The database also develops a service that provides the results of identity verification to other companies. For example, it shares the results of identity verification to support the identity verification work of other companies. In this way, by providing an identity verification agency service, it can support the identity verification work of other companies.

[0049] The database can provide marketing analysis services to support a company's marketing strategies. For example, the database provides marketing analysis services to build a system that supports a company's marketing strategies. For example, it analyzes customer data to propose a marketing strategy. The database also analyzes customer behavior data to optimize the marketing strategy. For example, it conducts targeted marketing based on customer purchase history. The database also provides marketing analysis services to improve a company's profits. For example, it analyzes customer preferences and behavior patterns to propose a marketing strategy. In this way, the marketing analysis services can be provided to support a company's marketing strategies.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The image acquisition unit can acquire the user's location information and compare it with the region where the confirmation document was issued. For example, it can acquire the user's current location using the smartphone's GPS function and check whether it matches the region where the confirmation document was issued. The image acquisition unit can also identify the user's location using Wi-Fi or Bluetooth location information and compare it with the region where the confirmation document was issued. Furthermore, the image acquisition unit can refer to the history of places the user has visited in the past and check whether there is any correlation with the region where the confirmation document was issued. In this way, by using the user's location information, the accuracy of determining the authenticity of the confirmation document can be improved.

[0052] The image acquisition unit can acquire the user's biometric information and compare it with the information on the verification document. For example, it can acquire the user's fingerprint using fingerprint authentication and check whether it matches the fingerprint information on the verification document. It can also acquire the user's facial image using facial authentication and check whether it matches the facial photograph on the verification document. It can also acquire the user's iris information using iris authentication and compare it with the iris information on the verification document. In this way, by using the user's biometric information, it is possible to improve the accuracy of determining the authenticity of the verification document.

[0053] The image acquisition unit can analyze a user's behavioral patterns and detect signs of fraudulent activity. For example, it can analyze the user's actions when submitting verification documents to check for any unnatural movements. It can also analyze the time of day and location when the user submits verification documents to check for any unusual patterns. It can also refer to the history of verification documents submitted by the user in the past and compare them with the current submission to check for any abnormalities. In this way, by analyzing the user's behavioral patterns, it is possible to detect signs of fraudulent activity.

[0054] The image acquisition unit can also use voice recognition technology to have the user read the contents of the confirmation document, and confirm that the voice and image match. For example, the user can read the name and address on their driver's license, and the voice can be analyzed to confirm whether it matches the image. Alternatively, the user can read the number on their My Number card, and the voice can be converted into text and compared with the image on the confirmation document. Furthermore, it is possible to build a system that checks in real time whether the voice and image match when the user reads the contents of the confirmation document. This can improve the accuracy of identity verification by confirming that the voice and image match.

[0055] The image acquisition unit can simultaneously perform fingerprint authentication and facial authentication, and combine multiple biometric authentication data to improve the accuracy of identity verification. For example, fingerprint authentication can be performed simultaneously to compare the image of the verification document with the fingerprint data. Alternatively, facial authentication can be performed simultaneously to compare the image of the verification document with the facial data. Furthermore, fingerprint authentication and facial authentication can be combined to improve the accuracy of identity verification. This allows the accuracy of identity verification to be improved by combining multiple biometric authentication data.

[0056] The authenticity determination unit uses generative AI to analyze minute pixel-level features of image data and detect signs of counterfeiting. For example, it can analyze the minute print patterns on a driver's license to detect signs of counterfeiting. It can also analyze minute changes in the color tone and brightness of image data to detect signs of counterfeiting. It can also analyze minute features of the edges and contours of image data to detect signs of counterfeiting. This allows for highly accurate detection of signs of counterfeiting by analyzing minute pixel-level features.

[0057] The authenticity determination unit can incorporate a self-learning function that allows the generation AI to learn from past determination results and continuously improve its determination accuracy. For example, it can learn based on past counterfeit determination data and improve its determination accuracy. It can also incorporate a self-learning function to be able to adapt to new counterfeiting methods. Furthermore, it is possible to build a system in which the generation AI receives feedback based on past determination results and self-learns. In this way, by incorporating a self-learning function, it is possible to continuously improve determination accuracy.

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

[0059] Step 1: The image acquisition unit acquires image data of the confirmation document. For example, the image of the confirmation document is taken using a smartphone camera. Alternatively, the image of the confirmation document can be acquired using a scanner. Furthermore, the image acquisition unit can directly acquire image data of the confirmation document submitted in digital format. Step 2: The authenticity determination unit uses a generation AI to analyze the image data acquired by the image acquisition unit and determine whether it is genuine or counterfeit. For example, the generation AI may analyze the image data using a text generation AI (e.g., LLM). The generation AI may also analyze the features of the image data using a multimodal generation AI. The generation AI may also analyze the pixel-level features of the image data to determine authenticity. Step 3: The result output unit outputs the determination result determined by the authenticity determination unit. For example, the result output unit displays the determination result on a screen. The result output unit can also store the determination result in a database. The result output unit can also transmit the determination result to another system. This allows the identity verification system according to the embodiment to automatically determine the authenticity of verification documents and prevent fraudulent contracts. For example, the output unit displays the determination result through a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results by email provides quick feedback by sending the results directly to the applicant or related parties.

[0060] (Example 2) The identity verification system according to the embodiment of the present invention acquires image data of a verification document, analyzes it using a generation AI, and determines whether it is genuine or forged. This identity verification system prevents fraudulent contracts and can be offered to other companies and organizations as a new business opportunity.

[0061] The identity verification system according to the embodiment includes an image acquisition unit, an authenticity determination unit, and a result output unit. The image acquisition unit acquires image data of a verification document. For example, the image of the verification document is taken using a smartphone camera. Alternatively, the image of the verification document can be acquired using a scanner. Furthermore, the image acquisition unit can directly acquire image data of a verification document submitted in digital format. The authenticity determination unit analyzes the image data acquired by the image acquisition unit using a generation AI to determine whether the document is genuine or forged. For example, the generation AI analyzes the image data using a text generation AI (e.g., LLM). Alternatively, the generation AI can analyze the features of the image data using a multimodal generation AI. Alternatively, the generation AI can analyze the pixel-level features of the image data to determine authenticity. The result output unit outputs the determination result determined by the authenticity determination unit. For example, the result output unit displays the determination result on a screen. Alternatively, the result output unit can store the determination result in a database. Alternatively, the result output unit can transmit the determination result to another system. As a result, the identity verification system according to the embodiment can automatically determine the authenticity of verification documents and prevent fraudulent contracts. For example, the output unit displays the determination results via a web application or a mobile application. If feedback on paper is desired, the results can be printed using a printer. Sending the results via email provides quick feedback by sending the results directly to the applicant or related parties.

[0062] The image acquisition unit can photograph the verification document from multiple angles, generate a 3D model, and analyze the three-dimensional features of the verification document. The image acquisition unit, for example, uses a smartphone camera to photograph the verification document from multiple angles and generate a 3D model. For example, a driver's license is photographed from above, below, left, and right, and the three-dimensional features are analyzed. The image acquisition unit can also scan the verification document from multiple angles using a scanner and generate a 3D model. For example, a My Number card is scanned from an angle and the three-dimensional features are analyzed. The image acquisition unit can also use a turntable to photograph the verification document while rotating it, and generate a 3D model. For example, a driver's license is placed on the turntable and photographed while rotating it 360 degrees. This improves the accuracy of authenticity determination by analyzing the three-dimensional features of the verification document.

[0063] The image acquisition unit can detect the material of the confirmation document and the presence or absence of a hologram by utilizing light reflection and transparency. The image acquisition unit detects the material of the confirmation document by, for example, utilizing light reflection. For example, it shines light on the surface of a driver's license and analyzes the reflection pattern. The image acquisition unit also detects the presence or absence of a hologram on the confirmation document by utilizing transparency. For example, it holds a My Number card up to the light to confirm the presence of a hologram. The image acquisition unit also detects the material of the confirmation document and the hologram using light of different wavelengths. For example, it analyzes the characteristics of a driver's license using ultraviolet or infrared light. This improves the accuracy of authenticity determination by detecting the material of the confirmation document and the presence or absence of a hologram.

[0064] The image acquisition unit can use the emotion estimation function to analyze the user's facial expressions and behavior when the image is acquired and detect signs of fraudulent activity. For example, the image acquisition unit analyzes the user's facial expressions when the image is acquired to detect signs of fraudulent activity. For example, it detects expressions of tension or impatience. The image acquisition unit also analyzes the user's behavior when the image is acquired to detect signs of fraudulent activity. For example, it analyzes hand tremors and eye movements. The image acquisition unit also analyzes the user's voice when the image is acquired to detect signs of fraudulent activity. For example, it detects changes in tone of voice and speaking style. In this way, signs of fraudulent activity can be detected by analyzing the user's facial expressions and behavior.

[0065] The image acquisition unit also uses voice recognition technology, allowing the user to read the contents of the confirmation document aloud, thereby confirming a match between the voice and the image. For example, the image acquisition unit has the user read the contents of the confirmation document aloud, and analyze the voice to confirm whether it matches the image. For example, the image acquisition unit reads out the name and address on a driver's license. The image acquisition unit also uses voice recognition technology to convert the content read by the user into text and compare it with the image on the confirmation document. For example, the image acquisition unit reads out the number on a My Number card. The image acquisition unit also builds a system that confirms a match between the voice and the image in real time. For example, it instantly determines whether the content read out by the user matches the image on the confirmation document. This improves the accuracy of identity verification by confirming a match between the voice and the image.

[0066] The image acquisition unit can simultaneously perform fingerprint authentication and facial authentication, and combine multiple pieces of biometric authentication data to improve the accuracy of identity verification. The image acquisition unit, for example, simultaneously performs fingerprint authentication and compares an image of a verification document with the fingerprint data. For example, it simultaneously acquires an image of a driver's license and a fingerprint. The image acquisition unit also simultaneously performs facial authentication and compares an image of a verification document with the facial data. For example, it simultaneously acquires an image of a My Number card and a facial photograph. The image acquisition unit also combines fingerprint authentication and facial authentication to improve the accuracy of identity verification. For example, it simultaneously acquires an image of a driver's license, fingerprints, and a facial photograph, and makes a comprehensive judgment. In this way, the accuracy of identity verification is improved by combining multiple pieces of biometric authentication data.

[0067] The image acquisition unit uses the emotion estimation function to monitor the user's emotional state in real time when the image is acquired, and can detect signs of tension or anxiety. For example, the image acquisition unit analyzes the user's facial expression in real time when the image is acquired to detect signs of tension or anxiety. For example, it analyzes frowns and eye movements. The image acquisition unit also analyzes the user's voice in real time when the image is acquired to detect signs of tension or anxiety. For example, it analyzes voice tremors and changes in tone. The image acquisition unit also analyzes the user's behavior in real time when the image is acquired to detect signs of tension or anxiety. For example, it analyzes hand movements and changes in posture. In this way, the user's emotional state can be monitored in real time to detect signs of tension or anxiety.

[0068] The authenticity determination unit uses generation AI to analyze minute pixel-level features of image data and detect traces of counterfeiting. For example, the authenticity determination unit uses generation AI to analyze minute pixel-level features of image data and detect traces of counterfeiting. For example, it analyzes the minute printed patterns on a driver's license. The authenticity determination unit also uses generation AI to analyze minute changes in the color tone and brightness of image data and detect traces of counterfeiting. For example, it analyzes unnatural changes in the color tone of a My Number card. The authenticity determination unit also uses generation AI to analyze minute features of the edges and contours of image data and detect traces of counterfeiting. For example, it analyzes unnatural changes in the edges of characters on a driver's license. This allows for highly accurate detection of traces of counterfeiting by analyzing minute pixel-level features.

[0069] The authenticity determination unit can incorporate a self-learning function that allows the generation AI to learn from past determination results and continuously improve its determination accuracy. The authenticity determination unit, for example, incorporates a self-learning function that allows the generation AI to learn from past determination results and continuously improve its determination accuracy. For example, it learns based on past counterfeit determination data. The authenticity determination unit also incorporates a self-learning function so that the generation AI can adapt to new counterfeiting methods. For example, it learns new counterfeit patterns and improves its determination accuracy. The authenticity determination unit also constructs a system in which the generation AI receives feedback based on past determination results and self-learns. For example, it corrects determination errors and reflects them in the next determination. In this way, by incorporating a self-learning function, it is possible to continuously improve determination accuracy.

[0070] The authenticity determination unit uses the emotion estimation function to analyze the user's emotional state at the time the image data was acquired and can determine whether changes in emotion are associated with signs of forgery. For example, the authenticity determination unit uses the generation AI to analyze the user's emotional state at the time the image data was acquired and determine whether changes in emotion are associated with signs of forgery. For example, it analyzes whether emotions such as tension and impatience are associated with signs of forgery. The authenticity determination unit also builds a system in which the generation AI determines signs of forgery based on the user's emotional data. For example, it determines that a large change in emotion indicates a high possibility of forgery. The authenticity determination unit also uses the generation AI to analyze the emotion estimation data and detect signs of forgery. For example, it determines that a low user emotion score indicates a high possibility of forgery. This allows for highly accurate detection of signs of forgery by analyzing the user's emotional state.

[0071] The authenticity determination unit can use the generation AI to develop a general-purpose authentication determination algorithm that can handle different types of confirmation documents. For example, the authenticity determination unit uses the generation AI to develop a general-purpose authentication determination algorithm that can handle different types of confirmation documents (passports, student ID cards, etc.). For example, it builds an algorithm that can authenticate both driver's licenses and passports. The authenticity determination unit also develops a system in which the generation AI learns the characteristics of different types of confirmation documents and performs general-purpose authenticity determination. For example, it builds an algorithm that can determine the authenticity of student ID cards and employee ID cards. The authenticity determination unit also introduces a general-purpose feature extraction method so that the generation AI can handle different types of confirmation documents. For example, it extracts common features from confirmation documents and uses them to determine their authenticity. This develops a general-purpose authentication determination algorithm that can handle different types of confirmation documents, improving the versatility of the system.

[0072] The authenticity determination unit allows the generation AI to analyze not only image data but also video data, and determine authenticity based on dynamic features. For example, the authenticity determination unit builds a system in which the generation AI analyzes not only image data but also video data, and determines authenticity based on dynamic features. For example, it analyzes videos taken while a confirmation document is moving. The authenticity determination unit also develops an algorithm in which the generation AI analyzes the features of each frame of video data to determine authenticity. For example, it analyzes videos of driver's licenses to detect traces of forgery. The authenticity determination unit also builds a system in which the generation AI determines authenticity based on dynamic features of video data. For example, it analyzes the movement of a hologram on a confirmation document to determine authenticity. This makes it possible to analyze video data and determine authenticity with high accuracy based on dynamic features.

[0073] When outputting the determination result, the result output unit can generate a detailed analysis report and clearly indicate which parts have been determined to be counterfeit. For example, when outputting the determination result, the result output unit generates a detailed analysis report and clearly indicates which parts have been determined to be counterfeit. For example, it may point out specific characters or hologram parts on a driver's license. The result output unit also visually indicates which parts of the image data have been determined to be counterfeit in the analysis report. For example, it may highlight the counterfeit parts in red. When outputting the determination result, the result output unit also describes in detail in the analysis report the characteristics that serve as evidence of counterfeiting. For example, it may explain the pixel-level characteristics of the counterfeit parts of a My Number card. In this way, by generating a detailed analysis report, it is possible to clearly indicate the evidence of counterfeiting.

[0074] The result output unit may incorporate a system that monitors the judgment results in real time and issues an alert if an abnormal pattern is detected. The result output unit may incorporate a system that monitors the judgment results in real time and issues an alert if an abnormal pattern is detected. For example, an alert may be issued if multiple counterfeit judgments are made in a short period of time. The result output unit may also monitor the judgment results in real time and issue an alert if an abnormal pattern is detected in a specific region or time period. For example, an alert may be issued if multiple counterfeit judgments are made in a specific region. The result output unit may also monitor the judgment results in real time and issue an alert if an abnormal pattern is detected for a specific user. For example, an alert may be issued if the same user submits multiple counterfeit documents. This allows for real-time monitoring and issuing an alert if an abnormal pattern is detected, enabling rapid response.

[0075] The result output unit can use the emotion estimation function to analyze the user's emotional reaction to the judgment result and further verify the possibility of fraudulent activity. The result output unit, for example, analyzes the user's emotional reaction to the judgment result and further verify the possibility of fraudulent activity. For example, the result output unit analyzes the user's facial expressions of surprise or impatience in response to the counterfeit judgment. The result output unit also uses the emotion estimation function to analyze the user's vocal reaction to the judgment result and verify the possibility of fraudulent activity. For example, the result output unit analyzes changes in tone of voice or speaking style. The result output unit also analyzes the user's behavior in response to the judgment result and verify the possibility of fraudulent activity. For example, the result output unit analyzes the user's agitation or unnatural behavior after hearing the judgment result. In this way, the possibility of fraudulent activity can be further verified by analyzing the user's emotional reaction.

[0076] The result output unit can link the judgment result with other security systems to realize comprehensive security measures. For example, the result output unit links the judgment result with an access control system and restricts access if a forged document is detected. For example, it suspends system access for a user who is judged to have a forged document. The result output unit also links the judgment result with other security systems to realize comprehensive security measures. For example, it sends an alert to a surveillance camera system when a forged document is detected. The result output unit also records the judgment result in a security log and works with other security systems to detect abnormal patterns. For example, it analyzes the behavioral history of a user who is judged to have a forged document. In this way, comprehensive security measures can be realized by linking the judgment result with other security systems.

[0077] The result output unit can record the judgment results using blockchain technology and store them in a tamper-proof format. The result output unit, for example, builds a system that records the judgment results using blockchain technology and stores them in a tamper-proof format. For example, the result of a counterfeit judgment is recorded on the blockchain. The result output unit also uses blockchain technology to store the history of the judgment results in a tamper-proof format. For example, past judgment results are recorded on the blockchain so that they can be verified later. The result output unit also builds a system that records the judgment results on the blockchain and shares them with other companies and organizations. For example, the results of counterfeit judgment are shared, thereby achieving widespread deterrence of fraudulent contracts. In this way, by using blockchain technology, the judgment results can be stored in a tamper-proof format.

[0078] A database can encrypt data to be stored and strengthen security. A database, for example, builds a system that encrypts data to be stored and strengthens security. For example, it encrypts and stores image data of confirmation documents and judgment results. A database also encrypts data to be stored and sets access permissions. For example, it allows only specific users to access the data. A database also builds a system that encrypts data to be stored and performs regular security checks. For example, it manages encryption keys and applies security policies. In this way, security can be strengthened by encrypting data.

[0079] A database can record access logs in detail and monitor for signs of unauthorized access. For example, a database can build a system that records access logs in detail and monitors for signs of unauthorized access. For example, an alert can be issued if an abnormal pattern is detected in the access log. A database can also analyze access logs and develop an algorithm that detects signs of unauthorized access. For example, it can detect abnormal access from a specific IP address. A database can also build a system that regularly monitors access logs and detects signs of unauthorized access early. For example, it can detect an abnormal increase in access logs. This makes it possible to strengthen security by recording access logs in detail and monitoring for signs of unauthorized access.

[0080] The database can use the emotion estimation function to analyze the emotional state of a user when using the database and detect signs of fraudulent use. For example, the database can analyze the emotional state of a user when using the database and build a system to detect signs of fraudulent use. For example, an alert can be issued if emotions of tension or impatience are detected. The database can also use the emotion estimation function to analyze the user's voice and facial expression when using the database and detect signs of fraudulent use. For example, changes in tone of voice and facial expression can be analyzed. The database can also analyze the user's behavior when using the database and build a system to detect signs of fraudulent use. For example, abnormal operation patterns and access frequency can be analyzed. In this way, signs of fraudulent use can be detected by analyzing the user's emotional state.

[0081] Databases can be built on the cloud to ensure scalability. For example, a system can be developed to build a database on the cloud and ensure scalability. For example, cloud services such as AWS or Azure can be used. Databases can also be built on the cloud to dynamically adjust resources according to access load. For example, resources can be automatically increased when access is concentrated. Databases can also be built on the cloud to facilitate data backup and recovery. For example, data backups can be periodically saved to cloud storage. In this way, building a database on the cloud can ensure scalability.

[0082] Databases can be API-linked with other companies' systems to achieve seamless data sharing. For example, databases can be API-linked with other companies' systems to build systems that achieve seamless data sharing. For example, data can be shared with other companies' identity verification systems. Databases can also use APIs to link with other companies' systems to share data in real time. For example, the results of counterfeit detection can be instantly sent to other companies' systems. Databases can also be API-linked to allow other companies to easily obtain data. For example, API keys can be issued to allow other companies to access the database. In this way, API-linked with other companies' systems can achieve seamless data sharing.

[0083] The database can use the emotion estimation function to analyze the emotional state of a user when using the database and detect signs of fraudulent use. For example, the database can analyze the emotional state of a user when using the database and build a system to detect signs of fraudulent use. For example, an alert can be issued if emotions of tension or impatience are detected. The database can also use the emotion estimation function to analyze the user's voice and facial expression when using the database and detect signs of fraudulent use. For example, changes in tone of voice and facial expression can be analyzed. The database can also analyze the user's behavior when using the database and build a system to detect signs of fraudulent use. For example, abnormal operation patterns and access frequency can be analyzed. In this way, signs of fraudulent use can be detected by analyzing the user's emotional state.

[0084] A database can introduce a subscription model to ensure regular revenue. For example, a database can introduce a subscription model and build a system to ensure regular revenue. For example, the database can be accessed for a monthly fee. The database can also introduce a subscription model and offer pricing plans based on frequency of use. For example, different pricing plans can be set depending on the number of accesses. The database can also introduce a subscription model and develop a marketing strategy to ensure regular revenue. For example, a free trial period can be set to acquire users. In this way, regular revenue can be ensured by introducing a subscription model.

[0085] The database can introduce a billing model based on the number of uses and provide a flexible fee structure based on the frequency of use. For example, the database can introduce a billing model based on the number of uses and build a system that provides a flexible fee structure based on the frequency of use. For example, a fee is set for each use. The database can also introduce a billing model based on the number of uses and provide discounts to users who use frequently. For example, a discounted fee is applied for uses above a certain number of times. The database can also introduce a billing model based on the number of uses and provide fee plans based on the frequency of use. For example, different fee plans are set depending on the number of uses per month. In this way, by introducing a billing model based on the number of uses, a flexible fee structure based on the frequency of use can be provided.

[0086] The database can use the emotion estimation function to analyze the user's emotional state and propose optimal business models. For example, the database uses the emotion estimation function to build a system that analyzes the user's emotional state and proposes optimal business models. For example, the database proposes optimal pricing plans based on the user's emotional score. The database also analyzes the user's emotional responses in real time and adjusts the business model based on the results. For example, it proposes premium plans to users with strong positive emotions. The database also develops a system that proposes business models tailored to the user's preferences and interests based on the emotion estimation data. For example, it dynamically adjusts the content of proposals according to changes in the user's emotions. In this way, it is possible to propose optimal business models by analyzing the user's emotional state.

[0087] The database can provide an identity verification agency service. For example, the database builds a system that provides an identity verification agency service. For example, it provides a service that allows other companies to request identity verification. The database also provides an identity verification outsourcing service. For example, it allows companies to outsource their own identity verification work. The database also develops a service that provides the results of identity verification to other companies. For example, it shares the results of identity verification to support the identity verification work of other companies. In this way, by providing an identity verification agency service, it can support the identity verification work of other companies.

[0088] The database can provide marketing analysis services to support a company's marketing strategies. For example, the database provides marketing analysis services to build a system that supports a company's marketing strategies. For example, it analyzes customer data to propose a marketing strategy. The database also analyzes customer behavior data to optimize the marketing strategy. For example, it conducts targeted marketing based on customer purchase history. The database also provides marketing analysis services to improve a company's profits. For example, it analyzes customer preferences and behavior patterns to propose a marketing strategy. In this way, the marketing analysis services can be provided to support a company's marketing strategies.

[0089] The database can use the emotion estimation function to analyze the user's emotional state and propose optimal business models. For example, the database uses the emotion estimation function to build a system that analyzes the user's emotional state and proposes optimal business models. For example, the database proposes optimal pricing plans based on the user's emotional score. The database also analyzes the user's emotional responses in real time and adjusts the business model based on the results. For example, it proposes premium plans to users with strong positive emotions. The database also develops a system that proposes business models tailored to the user's preferences and interests based on the emotion estimation data. For example, it dynamically adjusts the content of proposals according to changes in the user's emotions. In this way, it is possible to propose optimal business models by analyzing the user's emotional state.

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

[0091] The image acquisition unit can acquire the user's location information and compare it with the region where the confirmation document was issued. For example, it can acquire the user's current location using the smartphone's GPS function and check whether it matches the region where the confirmation document was issued. The image acquisition unit can also identify the user's location using Wi-Fi or Bluetooth location information and compare it with the region where the confirmation document was issued. Furthermore, the image acquisition unit can refer to the history of places the user has visited in the past and check whether there is any correlation with the region where the confirmation document was issued. In this way, by using the user's location information, the accuracy of determining the authenticity of the confirmation document can be improved.

[0092] The image acquisition unit can acquire the user's biometric information and compare it with the information on the verification document. For example, it can acquire the user's fingerprint using fingerprint authentication and check whether it matches the fingerprint information on the verification document. It can also acquire the user's facial image using facial authentication and check whether it matches the facial photograph on the verification document. It can also acquire the user's iris information using iris authentication and compare it with the iris information on the verification document. In this way, by using the user's biometric information, it is possible to improve the accuracy of determining the authenticity of the verification document.

[0093] The image acquisition unit can analyze a user's behavioral patterns and detect signs of fraudulent activity. For example, it can analyze the user's actions when submitting verification documents to check for any unnatural movements. It can also analyze the time of day and location when the user submits verification documents to check for any unusual patterns. It can also refer to the history of verification documents submitted by the user in the past and compare them with the current submission to check for any abnormalities. In this way, by analyzing the user's behavioral patterns, it is possible to detect signs of fraudulent activity.

[0094] The image acquisition unit uses the emotion estimation function to analyze the emotional state of the user when submitting the verification documents and detect signs of fraudulent activity. For example, it can analyze the user's facial expression when submitting the verification documents to check for signs of nervousness or impatience. It can also analyze the user's voice when submitting the verification documents to detect changes in tone of voice and speaking style. It can also analyze the user's behavior when submitting the verification documents to detect unnatural movements such as hand tremors and eye movements. In this way, it is possible to detect signs of fraudulent activity by analyzing the user's emotional state.

[0095] The image acquisition unit can also use voice recognition technology to have the user read the contents of the confirmation document, and confirm that the voice and image match. For example, the user can read the name and address on their driver's license, and the voice can be analyzed to confirm whether it matches the image. Alternatively, the user can read the number on their My Number card, and the voice can be converted into text and compared with the image on the confirmation document. Furthermore, it is possible to build a system that checks in real time whether the voice and image match when the user reads the contents of the confirmation document. This can improve the accuracy of identity verification by confirming that the voice and image match.

[0096] The image acquisition unit can simultaneously perform fingerprint authentication and facial authentication, and combine multiple biometric authentication data to improve the accuracy of identity verification. For example, fingerprint authentication can be performed simultaneously to compare the image of the verification document with the fingerprint data. Alternatively, facial authentication can be performed simultaneously to compare the image of the verification document with the facial data. Furthermore, fingerprint authentication and facial authentication can be combined to improve the accuracy of identity verification. This allows the accuracy of identity verification to be improved by combining multiple biometric authentication data.

[0097] The image acquisition unit uses the emotion estimation function to monitor the emotional state of the user in real time when submitting the verification documents and detect signs of tension or anxiety. For example, the image acquisition unit can analyze the user's facial expressions in real time when submitting the verification documents and detect signs of tension or anxiety, such as frowns or eye movements. The image acquisition unit can also analyze the user's voice in real time when submitting the verification documents and detect voice tremors and changes in tone. The image acquisition unit can also analyze the user's behavior in real time when submitting the verification documents and detect signs of tension or anxiety, such as hand movements and changes in posture. In this way, the user's emotional state can be monitored in real time to detect signs of tension or anxiety.

[0098] The authenticity determination unit uses generative AI to analyze minute pixel-level features of image data and detect signs of counterfeiting. For example, it can analyze the minute print patterns on a driver's license to detect signs of counterfeiting. It can also analyze minute changes in the color tone and brightness of image data to detect signs of counterfeiting. It can also analyze minute features of the edges and contours of image data to detect signs of counterfeiting. This allows for highly accurate detection of signs of counterfeiting by analyzing minute pixel-level features.

[0099] The authenticity determination unit can incorporate a self-learning function that allows the generation AI to learn from past determination results and continuously improve its determination accuracy. For example, it can learn based on past counterfeit determination data and improve its determination accuracy. It can also incorporate a self-learning function to be able to adapt to new counterfeiting methods. Furthermore, it is possible to build a system in which the generation AI receives feedback based on past determination results and self-learns. In this way, by incorporating a self-learning function, it is possible to continuously improve determination accuracy.

[0100] The authenticity determination unit can use the emotion estimation function to analyze the user's emotional state at the time the image data was acquired and determine whether changes in emotion are associated with signs of forgery. For example, it can analyze whether emotions such as tension or impatience are associated with signs of forgery. It can also build a system that determines signs of forgery based on the user's emotional data. Furthermore, it can analyze the emotion estimation data to detect signs of forgery. In this way, by analyzing the user's emotional state, it is possible to detect signs of forgery with high accuracy.

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

[0102] Step 1: The image acquisition unit acquires image data of the confirmation document. For example, the image of the confirmation document is taken using a smartphone camera. Alternatively, the image of the confirmation document can be acquired using a scanner. Furthermore, the image acquisition unit can directly acquire image data of the confirmation document submitted in digital format. Step 2: The authenticity determination unit uses a generation AI to analyze the image data acquired by the image acquisition unit and determine whether it is genuine or counterfeit. For example, the generation AI may analyze the image data using a text generation AI (e.g., LLM). The generation AI may also analyze the features of the image data using a multimodal generation AI. The generation AI may also analyze the pixel-level features of the image data to determine authenticity. Step 3: The result output unit outputs the determination result determined by the authenticity determination unit. For example, the result output unit displays the determination result on a screen. The result output unit can also store the determination result in a database. The result output unit can also transmit the determination result to another system. This allows the identity verification system according to the embodiment to automatically determine the authenticity of verification documents and prevent fraudulent contracts. For example, the output unit displays the determination result through a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending the results by email provides quick feedback by sending the results directly to the applicant or related parties.

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

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

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

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] In the robot 414, 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 robot 414 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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. an image acquisition unit that acquires image data of the confirmation document; an authenticity determination unit that analyzes the image data acquired by the image acquisition unit using a generation AI and determines whether the image data is genuine or counterfeit; a result output unit that outputs the determination result determined by the authenticity determination unit. A system characterized by:

2. The image acquisition unit The document is photographed from multiple angles, a 3D model is generated, and the three-dimensional features of the document are analyzed.

2. The system of claim 1.

3. The image acquisition unit Detects the material of the confirmation document and the presence or absence of a hologram by utilizing light reflection and transparency.

2. The system of claim 1.

4. The image acquisition unit Analyzes the user's facial expressions and behavior when capturing an image to detect signs of fraud 2. The system of claim 1.

5. The image acquisition unit Using voice recognition technology, the user reads out the contents of the confirmation document to confirm that the voice and image match.

2. The system of claim 1.

6. The image acquisition unit Simultaneous fingerprint and facial recognition will be performed, and multiple biometric data will be combined to improve the accuracy of identity verification.

2. The system of claim 1.

7. The image acquisition unit Monitors the user's emotional state in real time as the image is captured to detect signs of tension or anxiety 2. The system of claim 1.

8. The authenticity determination unit The generative AI is used to analyze minute pixel-level features of the image data to detect signs of counterfeiting.

2. The system of claim 1.

Images