System

Abstract

An object of a system according to an embodiment is to generate a face photograph illustration based on a photograph of an employee ID card and register the face photograph illustration in an employee information management system.SOLUTION: A system includes a photograph acquisition unit, a feature extraction unit, an illustration generation unit, and a registration unit. The photograph acquisition unit acquires a photograph of an employee ID card. The feature extraction unit extracts a feature of a face from the photograph of the employee ID card acquired by the photograph acquisition unit. The illustration generation unit generates a face photograph illustration based on the feature extracted by the feature extraction unit. The registration part registers the face photograph illustration generated by the illustration generation part in the employee information management system.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, if employees' facial photos were not registered, it was difficult to know the faces of their business partners.

[0005] The system according to the embodiment aims to generate a facial photo illustration based on the photograph on an employee ID card and register it in an employee information management system. [Means for solving the problem]

[0006] The system according to the embodiment includes a photo acquisition unit, a feature extraction unit, an illustration generation unit, and a registration unit. The photo acquisition unit acquires a photo of an employee ID card. The feature extraction unit extracts facial features from the employee ID card photo acquired by the photo acquisition unit. The illustration generation unit generates a facial photo illustration based on the features extracted by the feature extraction unit. The registration unit registers the facial photo illustration generated by the illustration generation unit in an employee information management system. [Effects of the Invention]

[0007] The system according to the embodiment can generate a facial photo illustration based on the photograph on an employee ID card and register it in an employee information management system. [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 employee information management system according to an embodiment of the present invention is a system that creates and registers facial photo illustrations that capture the characteristics of the photograph on an employee ID card. As a result, the employee information management system can facilitate smooth communication by displaying illustrations of the faces of employees who do not have registered photographs.

[0029] An employee information management system according to an embodiment includes a photo acquisition unit, a feature extraction unit, an illustration generation unit, and a registration unit. The photo acquisition unit acquires a photo of an employee ID card. For example, the photo of the employee ID card is digitized and uploaded to the system. The photo acquisition unit can also acquire photos registered in the employee information management system, Google, Zoom, or other communication tools. The feature extraction unit extracts facial features from the employee ID card photo acquired by the photo acquisition unit. For example, the feature extraction unit uses image recognition technology to analyze features such as facial contours, eyes, nose, and mouth. The feature extraction unit can also extract facial features using a generation AI (e.g., a text generation AI or a multimodal generation AI). The illustration generation unit generates a facial photo illustration based on the features extracted by the feature extraction unit. For example, the illustration generation unit generates an illustration when the generation AI receives a prompt such as, "Please generate a facial photo illustration of an employee based on the features in this photo." The illustration generation unit can also use the generation AI to generate an illustration that differs from the photo while faithfully reproducing facial features. The registration unit registers the facial photo illustration generated by the illustration generation unit in the employee information management system. For example, the registration unit can also register the generated illustration in Google, Zoom, or other communication tools. As a result, the employee information management system according to the embodiment can facilitate communication by displaying an illustration of the face of an employee who does not have a registered facial photo.

[0030] When acquiring a photo for an employee ID card, the photo acquisition unit automatically corrects variations in facial direction and facial expressions to select the optimal photo. For example, when acquiring a photo for an employee ID card, if the face is angled, the photo acquisition unit uses technology to automatically correct the face to face forward. For example, the photo acquisition unit detects the contours of the face and rotates the image based on the center of the face. Furthermore, when acquiring a photo for an employee ID card, the photo acquisition unit uses technology to automatically correct the facial expression to a smile, a neutral expression, or other expression if the facial expression is unclear. For example, the photo acquisition unit analyzes facial feature points and applies an algorithm to change the facial expression. Furthermore, when acquiring a photo for an employee ID card, the photo acquisition unit builds a system that automatically evaluates variations in facial direction and facial expressions to select the optimal one from multiple photos. For example, the photo acquisition unit scores the facial direction and facial expression of each photo and selects the photo with the highest score. This allows the optimal photo to be selected, thereby generating a more accurate facial photo illustration.

[0031] When acquiring a photo of an employee ID card, the photo acquisition unit automatically removes background noise, making facial features clearer. For example, when acquiring a photo of an employee ID card, the photo acquisition unit uses technology to automatically remove unwanted objects and noise in the background. For example, the photo acquisition unit separates the background from the facial area and blurs the background. When acquiring a photo of an employee ID card, the photo acquisition unit also uses technology to automatically convert the background to a single color if the background color or pattern obscures facial features. For example, the photo acquisition unit converts the background to white or gray. When acquiring a photo of an employee ID card, the photo acquisition unit also uses technology to detect the facial area and mask areas other than that area to remove background noise. For example, the outline of the face is detected and the background is painted black. By doing this, background noise is removed, making it possible to extract facial features more clearly.

[0032] The photo acquisition unit acquires not only the photo on the employee ID card but also other photos provided by the employee, and can select the best one from the multiple photos. For example, the photo acquisition unit acquires profile photos uploaded by the employee to social media in addition to the photo on the employee ID card, and selects the best one from the multiple photos. For example, it acquires photos using the social media API. Furthermore, the photo acquisition unit acquires photos from personal photo albums provided by the employee in addition to the photo on the employee ID card, and selects the best one from the multiple photos. For example, it acquires photos from a cloud storage service. Furthermore, the photo acquisition unit acquires photos previously submitted by the employee from a database in addition to the photo on the employee ID card, and selects the best one from the multiple photos. For example, it acquires photos stored in an employee information management system. In this way, by selecting the best one from the multiple photos, a more accurate facial photo illustration can be generated.

[0033] When acquiring a photo for an employee ID card, the photo acquisition unit can extract still images from a video and select the most suitable frame. For example, when acquiring a photo for an employee ID card, the photo acquisition unit extracts still images from a video provided by the employee and selects the most suitable frame. For example, the frame in which the employee's face is most clearly visible in the video is selected. When acquiring a photo for an employee ID card, the photo acquisition unit extracts still images from a video taken by the employee during a Zoom meeting and selects the most suitable frame. For example, the frame showing a natural expression during the meeting is selected. When acquiring a photo for an employee ID card, the photo acquisition unit extracts still images from a video taken by the employee during a presentation and selects the most suitable frame. For example, the frame showing a confident expression during the presentation is selected. In this way, by selecting the optimal frame from the video, a photo with a more natural expression can be acquired.

[0034] The feature extraction unit extracts not only facial features but also hairstyle and clothing features, thereby generating a more detailed illustration. The feature extraction unit uses, for example, image recognition technology to extract not only facial features but also hairstyle features, thereby generating a more detailed illustration. For example, the length, color, and style of hair are analyzed and reflected in the illustration. The feature extraction unit also uses image recognition technology to extract not only facial features but also clothing features, thereby generating a more detailed illustration. For example, the color, design, and accessories of clothing are analyzed and reflected in the illustration. The feature extraction unit also uses image recognition technology to simultaneously extract not only facial features but also hairstyle and clothing features, thereby generating a more detailed illustration. For example, the facial contour, hairstyle, and clothing are analyzed, and the overall image is reflected in the illustration. In this way, a more detailed face photo illustration can be generated by reflecting hairstyle and clothing features as well.

[0035] The feature extraction unit can extract facial features as a 3D model and generate a three-dimensional illustration. The feature extraction unit, for example, uses image recognition technology to extract facial features as a 3D model and generate a three-dimensional illustration. For example, the feature extraction unit acquires facial contours and feature points as 3D data and generates a 3D illustration. The feature extraction unit also uses image recognition technology to extract facial features as a 3D model and generate a three-dimensional illustration. For example, the feature extraction unit analyzes the texture and shading of the surface of the face to generate a realistic 3D illustration. The feature extraction unit also uses image recognition technology to extract facial features as a 3D model and generate a three-dimensional illustration. For example, the feature extraction unit analyzes facial movement and generates a 3D illustration corresponding to the movement. As a result, a three-dimensional facial photo illustration can be generated by using the 3D model.

[0036] The feature extraction unit can analyze voice data when extracting employee features and generate an illustration that reflects the voice features. For example, the feature extraction unit uses image recognition technology to analyze voice data when extracting employee facial features and generate an illustration that reflects the voice features. For example, the tone and pitch of the voice are analyzed and reflected in the illustration. Furthermore, the feature extraction unit can analyze voice data when extracting employee facial features and generate an illustration that reflects the voice features. For example, the emotional state of the voice is analyzed and an illustration with an expression corresponding to the emotion is generated. Furthermore, the feature extraction unit can analyze voice data when extracting employee facial features and generate an illustration that reflects the voice features using image recognition technology. For example, an illustration that reflects the employee's personality and atmosphere is generated based on the voice features. In this way, by analyzing voice data, an illustration that reflects the voice features can be generated.

[0037] The feature extraction unit can generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's features. The feature extraction unit can generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's facial features using, for example, image recognition technology. For example, the feature extraction unit compares past photographs with current photographs and reflects the changes. The feature extraction unit can also generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's facial features using image recognition technology. For example, past changes in hairstyle or clothing are reflected. The feature extraction unit can also generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's facial features using image recognition technology. For example, past changes in facial expressions or emotional states are reflected. In this way, by referring to past photographic data, an illustration that reflects changes over time can be generated.

[0038] The illustration generation unit can allow users to select from multiple styles when generating a facial photo illustration. For example, the illustration generation unit uses a generation AI to allow users to select from multiple styles, such as a realistic style, a cartoon style, or an anime style, when generating a facial photo illustration. For example, an interface is provided that allows users to select their preferred style. The illustration generation unit also uses the generation AI to train a dataset of different art styles to increase style options when generating a facial photo illustration. For example, styles such as oil painting, pencil drawing, and watercolor painting are added. The illustration generation unit also provides a function that allows users to customize the style when generating a facial photo illustration using the generation AI. For example, an interface is provided that allows users to adjust the color tone, line thickness, and level of detail. This allows users to select from multiple styles, making it possible to provide illustrations that suit their preferences.

[0039] The illustration generation unit can also enable the addition of backgrounds and accessories when generating a facial photo illustration. The illustration generation unit, for example, uses a generation AI to provide a function that allows a background to be added when generating a facial photo illustration. For example, multiple background options, such as an office background or a natural landscape, can be selected. The illustration generation unit also provides a function that allows accessories, such as glasses and a hat, to be added when generating a facial photo illustration using the generation AI. For example, the user can select their favorite accessories and reflect them in the illustration. The illustration generation unit also provides a function that allows the user to customize the background and accessories when generating a facial photo illustration using the generation AI. For example, an interface is provided that allows the user to adjust the color and design of the background and the type and placement of accessories. This allows the user to generate more unique illustrations by adding backgrounds and accessories.

[0040] The illustration generation unit can add a design that reflects the employee's hobbies and interests when generating a face photo illustration. For example, the illustration generation unit uses generation AI to add a design that reflects the employee's hobbies and interests when generating a face photo illustration. For example, a sports-related design is added for an employee whose hobby is sports. The illustration generation unit also uses generation AI to add a design that reflects the employee's interests when generating a face photo illustration. For example, a design of an instrument or musical note is added for an employee who likes music. The illustration generation unit also provides a function that allows the user to customize the design that reflects the employee's hobbies and interests when generating a face photo illustration using generation AI. For example, the user can select a design that matches their hobbies and interests and reflect it in the illustration. This allows for more unique illustrations to be generated by reflecting hobbies and interests.

[0041] The illustration generation unit can add a design according to the employee's job title and job content when generating a face photo illustration. For example, the illustration generation unit uses generation AI to add a design according to the employee's job title when generating a face photo illustration. For example, a design of a person wearing a suit is added for a manager. Furthermore, the illustration generation unit uses generation AI to add a design according to the employee's job content when generating a face photo illustration. For example, designs of tools and computers are added for an engineer. Furthermore, the illustration generation unit provides a function that allows the user to customize the design according to the employee's job title and job content when generating a face photo illustration using generation AI. For example, the user can select a design according to the job title and job content and reflect it in the illustration. In this way, by adding a design according to the job title and job content, an illustration that is more suitable for the job can be generated.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] When acquiring a photo for an employee ID card, the photo acquisition unit can automatically correct variations in facial direction and facial expressions to select the most suitable photo. For example, it uses technology that detects the contours of the face and rotates it based on the center of the face. Furthermore, if the facial expression is unclear, it can also use technology that automatically corrects it to a smile, a serious expression, or other similar expression. Furthermore, it is possible to build a system that automatically evaluates variations in facial direction and facial expressions to select the most suitable one from multiple photos. This allows for the selection of the most suitable photo, resulting in a more accurate facial photo illustration.

[0044] When acquiring employee ID photos, the photo acquisition unit can automatically remove background noise to make facial features clearer. For example, it uses technology to separate the facial area from the background and blur the background. In addition, if the background color or pattern hides facial features, it can also use technology to automatically convert the background to a single color. Furthermore, it is also possible to remove background noise by using technology to detect the facial area and mask everything other than that area. In this way, by removing background noise, facial features can be extracted more clearly.

[0045] The photo acquisition unit can acquire not only the employee ID photo but also other photos provided by the employee and select the best one from multiple photos. For example, it can acquire profile photos uploaded to social media and select the best one. It can also acquire photos from a personal photo album and select the best one. It can also acquire photos submitted in the past from a database and select the best one. This allows for the generation of more accurate facial photo illustrations by selecting the best one from multiple photos.

[0046] When acquiring employee ID photos, the photo acquisition unit can extract still images from video and select the most suitable frame. For example, it can extract still images from videos provided by employees and select the frame that most clearly captures their faces. It can also extract still images from videos taken during Zoom meetings and select the frame that shows the most natural facial expression. It can also extract still images from videos taken during presentations and select the frame that shows the most confident facial expression. This allows for the acquisition of photos with more natural facial expressions by selecting the optimal frame from the video.

[0047] The feature extraction unit can extract not only facial features but also hairstyle and clothing features, generating more detailed illustrations. For example, it can analyze hair length, color, and style and reflect them in the illustration. It can also analyze clothing color, design, and accessories and reflect them in the illustration. It can also simultaneously analyze facial contours, hairstyle, and clothing to reflect the overall image in the illustration. This allows for the generation of more detailed facial photo illustrations by incorporating hairstyle and clothing features.

[0048] The feature extraction unit can extract facial features as a 3D model and generate a three-dimensional illustration. For example, it can obtain the facial contours and feature points as 3D data and generate a 3D illustration. It can also analyze the texture and shading of the face's surface to generate a realistic 3D illustration. It can also analyze facial movement and generate a 3D illustration that corresponds to the movement. This makes it possible to generate a three-dimensional facial photo illustration using the 3D model.

[0049] When extracting employee characteristics, the feature extraction unit can also analyze voice data and generate illustrations that reflect the voice characteristics. For example, it can analyze the tone and pitch of the voice and reflect them in the illustration. It can also analyze the emotional state of the voice and generate illustrations with facial expressions that correspond to the emotion. Furthermore, it can generate illustrations that reflect the personality and atmosphere of employees based on the voice characteristics. This makes it possible to generate illustrations that reflect the voice characteristics by analyzing the voice data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The photo acquisition unit acquires a photo of the employee ID card. For example, the photo of the employee ID card is digitized and uploaded to the system. The photo acquisition unit can also acquire photos registered in the employee information management system, Google, Zoom, or other communication tools. Step 2: The feature extraction unit extracts facial features from the employee ID photo acquired by the photo acquisition unit. For example, the feature extraction unit uses image recognition technology to analyze facial features such as the facial contours, eyes, nose, and mouth. The feature extraction unit can also extract facial features using generative AI (e.g., text generation AI or multimodal generation AI). Step 3: The illustration generation unit generates a facial photo illustration based on the features extracted by the feature extraction unit. For example, the illustration generation unit generates an illustration when the generation AI receives a prompt such as, "Please generate a facial photo illustration of an employee based on the features of this photo." The illustration generation unit can also use the generation AI to generate an illustration that differs from the photo while faithfully reproducing the facial features. Step 4: The registration unit registers the facial photo illustration generated by the illustration generation unit in the employee information management system. For example, the registration unit can also register the generated illustration in Google, Zoom, or other communication tools.

[0052] (Example 2) The employee information management system according to an embodiment of the present invention is a system that creates and registers facial photo illustrations that capture the characteristics of the photograph on an employee ID card. As a result, the employee information management system can facilitate smooth communication by displaying illustrations of the faces of employees who do not have registered photographs.

[0053] An employee information management system according to an embodiment includes a photo acquisition unit, a feature extraction unit, an illustration generation unit, and a registration unit. The photo acquisition unit acquires a photo of an employee ID card. For example, the photo of the employee ID card is digitized and uploaded to the system. The photo acquisition unit can also acquire photos registered in the employee information management system, Google, Zoom, or other communication tools. The feature extraction unit extracts facial features from the employee ID card photo acquired by the photo acquisition unit. For example, the feature extraction unit uses image recognition technology to analyze features such as facial contours, eyes, nose, and mouth. The feature extraction unit can also extract facial features using a generation AI (e.g., a text generation AI or a multimodal generation AI). The illustration generation unit generates a facial photo illustration based on the features extracted by the feature extraction unit. For example, the illustration generation unit generates an illustration when the generation AI receives a prompt such as, "Please generate a facial photo illustration of an employee based on the features in this photo." The illustration generation unit can also use the generation AI to generate an illustration that differs from the photo while faithfully reproducing facial features. The registration unit registers the facial photo illustration generated by the illustration generation unit in the employee information management system. For example, the registration unit can also register the generated illustration in Google, Zoom, or other communication tools. As a result, the employee information management system according to the embodiment can facilitate communication by displaying an illustration of the face of an employee who does not have a registered facial photo.

[0054] When acquiring a photo for an employee ID card, the photo acquisition unit automatically corrects variations in facial direction and facial expressions to select the optimal photo. For example, when acquiring a photo for an employee ID card, if the face is angled, the photo acquisition unit uses technology to automatically correct the face to face forward. For example, the photo acquisition unit detects the contours of the face and rotates the image based on the center of the face. Furthermore, when acquiring a photo for an employee ID card, the photo acquisition unit uses technology to automatically correct the facial expression to a smile, a neutral expression, or other expression if the facial expression is unclear. For example, the photo acquisition unit analyzes facial feature points and applies an algorithm to change the facial expression. Furthermore, when acquiring a photo for an employee ID card, the photo acquisition unit builds a system that automatically evaluates variations in facial direction and facial expressions to select the optimal one from multiple photos. For example, the photo acquisition unit scores the facial direction and facial expression of each photo and selects the photo with the highest score. This allows the optimal photo to be selected, thereby generating a more accurate facial photo illustration.

[0055] When acquiring a photo of an employee ID card, the photo acquisition unit automatically removes background noise, making facial features clearer. For example, when acquiring a photo of an employee ID card, the photo acquisition unit uses technology to automatically remove unwanted objects and noise in the background. For example, the photo acquisition unit separates the background from the facial area and blurs the background. When acquiring a photo of an employee ID card, the photo acquisition unit also uses technology to automatically convert the background to a single color if the background color or pattern obscures facial features. For example, the photo acquisition unit converts the background to white or gray. When acquiring a photo of an employee ID card, the photo acquisition unit also uses technology to detect the facial area and mask areas other than that area to remove background noise. For example, the outline of the face is detected and the background is painted black. By doing this, background noise is removed, making it possible to extract facial features more clearly.

[0056] The photo acquisition unit can use the emotion estimation function to estimate the emotional state of an employee from the employee ID photo and select the most appropriate photo based on that information. For example, when acquiring an employee ID photo, the photo acquisition unit uses the emotion estimation function to analyze the employee's emotional state from their facial expression and selects a photo that shows the most positive emotion. For example, it prioritizes selecting a photo of a smiling face. Furthermore, when acquiring an employee ID photo, the photo acquisition unit uses the emotion estimation function to calculate the employee's emotional score and selects the most appropriate photo based on that score. For example, it selects a photo with a high emotion score. Furthermore, when acquiring an employee ID photo, the photo acquisition unit uses the emotion estimation function to analyze the employee's emotional state in real time and selects a photo that shows the most natural expression. For example, it selects a photo with a relaxed, unafraid expression. In this way, by using the emotion estimation function, a photo with a more natural expression can be selected.

[0057] The photo acquisition unit acquires not only the photo on the employee ID card but also other photos provided by the employee, and can select the best one from the multiple photos. For example, the photo acquisition unit acquires profile photos uploaded by the employee to social media in addition to the photo on the employee ID card, and selects the best one from the multiple photos. For example, it acquires photos using the social media API. Furthermore, the photo acquisition unit acquires photos from personal photo albums provided by the employee in addition to the photo on the employee ID card, and selects the best one from the multiple photos. For example, it acquires photos from a cloud storage service. Furthermore, the photo acquisition unit acquires photos previously submitted by the employee from a database in addition to the photo on the employee ID card, and selects the best one from the multiple photos. For example, it acquires photos stored in an employee information management system. In this way, by selecting the best one from the multiple photos, a more accurate facial photo illustration can be generated.

[0058] When acquiring a photo for an employee ID card, the photo acquisition unit can extract still images from a video and select the most suitable frame. For example, when acquiring a photo for an employee ID card, the photo acquisition unit extracts still images from a video provided by the employee and selects the most suitable frame. For example, the frame in which the employee's face is most clearly visible in the video is selected. When acquiring a photo for an employee ID card, the photo acquisition unit extracts still images from a video taken by the employee during a Zoom meeting and selects the most suitable frame. For example, the frame showing a natural expression during the meeting is selected. When acquiring a photo for an employee ID card, the photo acquisition unit extracts still images from a video taken by the employee during a presentation and selects the most suitable frame. For example, the frame showing a confident expression during the presentation is selected. In this way, by selecting the optimal frame from the video, a photo with a more natural expression can be acquired.

[0059] The photo acquisition unit can use the emotion estimation function to select a photo in which the employee shows the most positive emotion and generate an illustration based on that photo. For example, the photo acquisition unit can use the emotion estimation function to select a photo in which the employee shows the most positive emotion and generate an illustration based on that photo. For example, it can select a photo of a smiling face and generate an illustration of a smiling face. The photo acquisition unit can also use the emotion estimation function to select a photo in which the employee shows the most relaxed emotion and generate an illustration based on that photo. For example, it can select a photo with a relaxed facial expression and generate an illustration of a relaxed facial expression. The photo acquisition unit can also use the emotion estimation function to select a photo in which the employee shows the most confident emotion and generate an illustration based on that photo. For example, it can select a photo with a confident facial expression and generate an illustration of a confident facial expression. In this way, by generating an illustration based on a photo in which positive emotion is present, a facial photo illustration that makes a more favorable impression can be provided.

[0060] The feature extraction unit extracts not only facial features but also hairstyle and clothing features, thereby generating a more detailed illustration. The feature extraction unit uses, for example, image recognition technology to extract not only facial features but also hairstyle features, thereby generating a more detailed illustration. For example, the length, color, and style of hair are analyzed and reflected in the illustration. The feature extraction unit also uses image recognition technology to extract not only facial features but also clothing features, thereby generating a more detailed illustration. For example, the color, design, and accessories of clothing are analyzed and reflected in the illustration. The feature extraction unit also uses image recognition technology to simultaneously extract not only facial features but also hairstyle and clothing features, thereby generating a more detailed illustration. For example, the facial contour, hairstyle, and clothing are analyzed, and the overall image is reflected in the illustration. In this way, a more detailed face photo illustration can be generated by reflecting hairstyle and clothing features as well.

[0061] The feature extraction unit can extract facial features as a 3D model and generate a three-dimensional illustration. The feature extraction unit, for example, uses image recognition technology to extract facial features as a 3D model and generate a three-dimensional illustration. For example, the feature extraction unit acquires facial contours and feature points as 3D data and generates a 3D illustration. The feature extraction unit also uses image recognition technology to extract facial features as a 3D model and generate a three-dimensional illustration. For example, the feature extraction unit analyzes the texture and shading of the surface of the face to generate a realistic 3D illustration. The feature extraction unit also uses image recognition technology to extract facial features as a 3D model and generate a three-dimensional illustration. For example, the feature extraction unit analyzes facial movement and generates a 3D illustration corresponding to the movement. As a result, a three-dimensional facial photo illustration can be generated by using the 3D model.

[0062] The feature extraction unit can use the emotion estimation function to consider the emotional state of the employee when extracting facial features and generate an illustration with an expression corresponding to the emotion. For example, the feature extraction unit uses the emotion estimation function to analyze the emotional state of the employee when extracting facial features and generate an illustration with an expression corresponding to the emotion. For example, if the employee is showing a smiling emotion, an illustration of a smiling face is generated. The feature extraction unit also uses the emotion estimation function to calculate the employee's emotion score when extracting facial features and generate an illustration with an expression based on the score. For example, if the emotion score is high, an illustration of a positive expression is generated. The feature extraction unit also uses the emotion estimation function to analyze the employee's emotional state in real time when extracting facial features and generate an illustration with an expression corresponding to the emotion. For example, if the employee is showing a relaxed emotion, an illustration of a relaxed expression is generated. This makes it possible to generate illustrations with more natural expressions by considering the emotional state.

[0063] The feature extraction unit can analyze voice data when extracting employee features and generate an illustration that reflects the voice features. For example, the feature extraction unit uses image recognition technology to analyze voice data when extracting employee facial features and generate an illustration that reflects the voice features. For example, the tone and pitch of the voice are analyzed and reflected in the illustration. Furthermore, the feature extraction unit can analyze voice data when extracting employee facial features and generate an illustration that reflects the voice features. For example, the emotional state of the voice is analyzed and an illustration with an expression corresponding to the emotion is generated. Furthermore, the feature extraction unit can analyze voice data when extracting employee facial features and generate an illustration that reflects the voice features using image recognition technology. For example, an illustration that reflects the employee's personality and atmosphere is generated based on the voice features. In this way, by analyzing voice data, an illustration that reflects the voice features can be generated.

[0064] The feature extraction unit can generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's features. The feature extraction unit can generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's facial features using, for example, image recognition technology. For example, the feature extraction unit compares past photographs with current photographs and reflects the changes. The feature extraction unit can also generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's facial features using image recognition technology. For example, past changes in hairstyle or clothing are reflected. The feature extraction unit can also generate an illustration that reflects changes over time by referring to past photographic data when extracting the employee's facial features using image recognition technology. For example, past changes in facial expressions or emotional states are reflected. In this way, by referring to past photographic data, an illustration that reflects changes over time can be generated.

[0065] The feature extraction unit can use the emotion estimation function to consider the emotional relationships with other employees when extracting the features of an employee and generate an illustration that reflects the relationships. For example, the feature extraction unit can use the emotion estimation function to analyze the emotional relationships with other employees when extracting the features of an employee and generate an illustration that reflects the relationships. For example, an illustration showing a relationship with a close colleague can be generated. The feature extraction unit can also use the emotion estimation function to analyze the emotional relationships with other employees when extracting the features of an employee and generate an illustration that reflects the relationships. For example, an illustration showing the emotional connections within a team can be generated. The feature extraction unit can also use the emotion estimation function to analyze the emotional relationships with other employees when extracting the features of an employee and generate an illustration that reflects the relationships. For example, an illustration showing the relationship between a boss and a subordinate can be generated. In this way, by reflecting the emotional relationships with other employees, a more approachable illustration can be generated.

[0066] The illustration generation unit can allow users to select from multiple styles when generating a facial photo illustration. For example, the illustration generation unit uses a generation AI to allow users to select from multiple styles, such as a realistic style, a cartoon style, or an anime style, when generating a facial photo illustration. For example, an interface is provided that allows users to select their preferred style. The illustration generation unit also uses the generation AI to train a dataset of different art styles to increase style options when generating a facial photo illustration. For example, styles such as oil painting, pencil drawing, and watercolor painting are added. The illustration generation unit also provides a function that allows users to customize the style when generating a facial photo illustration using the generation AI. For example, an interface is provided that allows users to adjust the color tone, line thickness, and level of detail. This allows users to select from multiple styles, making it possible to provide illustrations that suit their preferences.

[0067] The illustration generation unit can also enable the addition of backgrounds and accessories when generating a facial photo illustration. The illustration generation unit, for example, uses a generation AI to provide a function that allows a background to be added when generating a facial photo illustration. For example, multiple background options, such as an office background or a natural landscape, can be selected. The illustration generation unit also provides a function that allows accessories, such as glasses and a hat, to be added when generating a facial photo illustration using the generation AI. For example, the user can select their favorite accessories and reflect them in the illustration. The illustration generation unit also provides a function that allows the user to customize the background and accessories when generating a facial photo illustration using the generation AI. For example, an interface is provided that allows the user to adjust the color and design of the background and the type and placement of accessories. This allows the user to generate more unique illustrations by adding backgrounds and accessories.

[0068] The illustration generation unit can use the emotion estimation function to generate an illustration with a facial expression that reflects the emotional state of an employee when generating a facial photo illustration. For example, the illustration generation unit uses the emotion estimation function to analyze the emotional state of the employee when generating a facial photo illustration, and generate an illustration with a facial expression that reflects that emotion. For example, an illustration with a smiling or surprised expression is generated. The illustration generation unit also uses the emotion estimation function to calculate the employee's emotional score when generating a facial photo illustration, and generate an illustration with a facial expression based on that score. For example, if the emotional score is high, an illustration with a positive expression is generated. The illustration generation unit also uses the emotion estimation function to analyze the employee's emotional state in real time when generating a facial photo illustration, and generate an illustration with a facial expression that reflects that emotion. For example, if the employee shows a relaxed emotion, an illustration with a relaxed expression is generated. This allows for the generation of illustrations with more natural expressions by reflecting the emotional state.

[0069] The illustration generation unit can add a design that reflects the employee's hobbies and interests when generating a face photo illustration. For example, the illustration generation unit uses generation AI to add a design that reflects the employee's hobbies and interests when generating a face photo illustration. For example, a sports-related design is added for an employee whose hobby is sports. The illustration generation unit also uses generation AI to add a design that reflects the employee's interests when generating a face photo illustration. For example, a design of an instrument or musical note is added for an employee who likes music. The illustration generation unit also provides a function that allows the user to customize the design that reflects the employee's hobbies and interests when generating a face photo illustration using generation AI. For example, the user can select a design that matches their hobbies and interests and reflect it in the illustration. This allows for more unique illustrations to be generated by reflecting hobbies and interests.

[0070] The illustration generation unit can add a design according to the employee's job title and job content when generating a face photo illustration. For example, the illustration generation unit uses generation AI to add a design according to the employee's job title when generating a face photo illustration. For example, a design of a person wearing a suit is added for a manager. Furthermore, the illustration generation unit uses generation AI to add a design according to the employee's job content when generating a face photo illustration. For example, designs of tools and computers are added for an engineer. Furthermore, the illustration generation unit provides a function that allows the user to customize the design according to the employee's job title and job content when generating a face photo illustration using generation AI. For example, the user can select a design according to the job title and job content and reflect it in the illustration. In this way, by adding a design according to the job title and job content, an illustration that is more suitable for the job can be generated.

[0071] The illustration generation unit can use the emotion estimation function to generate an illustration with an expression that shows the employee's most positive emotion when generating a facial photo illustration. For example, the illustration generation unit uses the emotion estimation function to generate an illustration with an expression that shows the employee's most positive emotion when generating a facial photo illustration. For example, it generates an illustration with a smiling expression. Furthermore, the illustration generation unit uses the emotion estimation function to generate an illustration with an expression that shows the employee's most relaxed emotion when generating a facial photo illustration. For example, it generates an illustration with a relaxed expression. Furthermore, the illustration generation unit uses the emotion estimation function to generate an illustration with an expression that shows the employee's most confident emotion when generating a facial photo illustration. For example, it generates an illustration with a confident expression. In this way, by generating an illustration with an expression that shows a positive emotion, it is possible to provide a facial photo illustration that makes a more favorable impression.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] When acquiring a photo for an employee ID card, the photo acquisition unit can automatically correct variations in facial direction and facial expressions to select the most suitable photo. For example, it uses technology that detects the contours of the face and rotates it based on the center of the face. Furthermore, if the facial expression is unclear, it can also use technology that automatically corrects it to a smile, a serious expression, or other similar expression. Furthermore, it is possible to build a system that automatically evaluates variations in facial direction and facial expressions to select the most suitable one from multiple photos. This allows for the selection of the most suitable photo, resulting in a more accurate facial photo illustration.

[0074] When acquiring employee ID photos, the photo acquisition unit can automatically remove background noise to make facial features clearer. For example, it uses technology to separate the facial area from the background and blur the background. In addition, if the background color or pattern hides facial features, it can also use technology to automatically convert the background to a single color. Furthermore, it is also possible to remove background noise by using technology to detect the facial area and mask everything other than that area. In this way, by removing background noise, facial features can be extracted more clearly.

[0075] The photo acquisition unit can use the emotion estimation function to estimate the emotional state of an employee from the photo on their employee ID card and select the most suitable photo based on that information. For example, it can prioritize the selection of photos showing smiling faces. It can also calculate an emotion score and select the most suitable photo based on that score. It can also analyze the emotional state in real time and select the photo that shows the most natural expression. As a result, the emotion estimation function can be used to select photos with more natural expressions.

[0076] The photo acquisition unit can acquire not only the employee ID photo but also other photos provided by the employee and select the best one from multiple photos. For example, it can acquire profile photos uploaded to social media and select the best one. It can also acquire photos from a personal photo album and select the best one. It can also acquire photos submitted in the past from a database and select the best one. This allows for the generation of more accurate facial photo illustrations by selecting the best one from multiple photos.

[0077] When acquiring employee ID photos, the photo acquisition unit can extract still images from video and select the most suitable frame. For example, it can extract still images from videos provided by employees and select the frame that most clearly captures their faces. It can also extract still images from videos taken during Zoom meetings and select the frame that shows the most natural facial expression. It can also extract still images from videos taken during presentations and select the frame that shows the most confident facial expression. This allows for the acquisition of photos with more natural facial expressions by selecting the optimal frame from the video.

[0078] The feature extraction unit can extract not only facial features but also hairstyle and clothing features, generating more detailed illustrations. For example, it can analyze hair length, color, and style and reflect them in the illustration. It can also analyze clothing color, design, and accessories and reflect them in the illustration. It can also simultaneously analyze facial contours, hairstyle, and clothing to reflect the overall image in the illustration. This allows for the generation of more detailed facial photo illustrations by incorporating hairstyle and clothing features.

[0079] The feature extraction unit can extract facial features as a 3D model and generate a three-dimensional illustration. For example, it can obtain the facial contours and feature points as 3D data and generate a 3D illustration. It can also analyze the texture and shading of the face's surface to generate a realistic 3D illustration. It can also analyze facial movement and generate a 3D illustration that corresponds to the movement. This makes it possible to generate a three-dimensional facial photo illustration using the 3D model.

[0080] The feature extraction unit uses the emotion estimation function to consider the employee's emotional state when extracting facial features, and can generate illustrations with facial expressions that correspond to the emotion. For example, if a smiling emotion is indicated, a smiling illustration is generated. It is also possible to calculate an emotion score and generate illustrations with facial expressions based on that score. Furthermore, by analyzing the emotional state in real time, if a relaxed emotion is indicated, an illustration with a relaxed expression can be generated. This allows for the generation of illustrations with more natural facial expressions by taking the emotional state into consideration.

[0081] When extracting employee characteristics, the feature extraction unit can also analyze voice data and generate illustrations that reflect the voice characteristics. For example, it can analyze the tone and pitch of the voice and reflect them in the illustration. It can also analyze the emotional state of the voice and generate illustrations with facial expressions that correspond to the emotion. Furthermore, it can generate illustrations that reflect the personality and atmosphere of employees based on the voice characteristics. This makes it possible to generate illustrations that reflect the voice characteristics by analyzing the voice data.

[0082] The feature extraction unit uses the emotion estimation function to consider the emotional relationships with other employees when extracting an employee's features, and can generate illustrations that reflect those relationships. For example, an illustration showing a relationship with a close colleague can be generated. It is also possible to generate an illustration showing the emotional connection within a team. Furthermore, it is also possible to generate an illustration showing the relationship between a superior and a subordinate. This allows for the generation of more approachable illustrations by reflecting the emotional relationships with other employees.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The photo acquisition unit acquires a photo of the employee ID card. For example, the photo of the employee ID card is digitized and uploaded to the system. The photo acquisition unit can also acquire photos registered in the employee information management system, Google, Zoom, or other communication tools. Step 2: The feature extraction unit extracts facial features from the employee ID photo acquired by the photo acquisition unit. For example, the feature extraction unit uses image recognition technology to analyze facial features such as the facial contours, eyes, nose, and mouth. The feature extraction unit can also extract facial features using generative AI (e.g., text generation AI or multimodal generation AI). Step 3: The illustration generation unit generates a facial photo illustration based on the features extracted by the feature extraction unit. For example, the illustration generation unit generates an illustration when the generation AI receives a prompt such as, "Please generate a facial photo illustration of an employee based on the features of this photo." The illustration generation unit can also use the generation AI to generate an illustration that differs from the photo while faithfully reproducing the facial features. Step 4: The registration unit registers the facial photo illustration generated by the illustration generation unit in the employee information management system. For example, the registration unit can also register the generated illustration in Google, Zoom, or other communication tools.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a photo acquisition unit for acquiring a photo of an employee ID card; a feature extraction unit that extracts facial features from the photograph of the employee ID card acquired by the photograph acquisition unit; an illustration generation unit that generates a facial photo illustration based on the features extracted by the feature extraction unit; a registration unit that registers the facial photo illustration generated by the illustration generation unit in an employee information management system. A system characterized by:

2. The photo acquisition unit When acquiring a photo for the employee ID card, the face orientation and facial expression variations are automatically corrected to select the most suitable photo.

2. The system of claim 1.

3. The photo acquisition unit When capturing the employee ID photo, background noise is automatically removed to make the facial features more distinct.

2. The system of claim 1.

4. The photo acquisition unit The emotional state of the employee is estimated from the photo on the employee ID card, and the most suitable photo is selected based on that information.

2. The system of claim 1.

5. The photo acquisition unit In addition to the photo on the employee ID card, other photos provided by the employee are also acquired, and the most suitable one is selected from the multiple photos.

2. The system of claim 1.

Images