system

Abstract

The system according to the embodiment aims to recommend optimal hairstyles and fashions based on the user's personal identification and appearance analysis. [Solution] A system according to an embodiment includes a face authentication unit, an analysis unit, a recommendation unit, and a linkage unit. The face authentication unit recognizes the user's face and identifies the individual. The analysis unit analyzes the appearance of the user identified by the face authentication unit. The recommendation unit recommends hairstyles or fashions based on the data analyzed by the analysis unit. The linkage unit provides products recommended by the recommendation unit in collaboration with services provided by group companies.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately recommend hairstyles and fashions based on the user's personal identification and appearance analysis, and there is room for improvement.

[0005] The system according to the embodiment aims to recommend optimal hairstyles and fashions based on the user's personal identification and appearance analysis. [Means for solving the problem]

[0006] The system according to the embodiment includes a face authentication unit, an analysis unit, a recommendation unit, and a linkage unit. The face authentication unit recognizes the user's face and identifies the individual. The analysis unit analyzes the appearance of the user identified by the face authentication unit. The recommendation unit recommends hairstyles or fashions based on the data analyzed by the analysis unit. The linkage unit provides products recommended by the recommendation unit in collaboration with services of group companies. [Effects of the Invention]

[0007] The system according to the embodiment can recommend optimal hairstyles and fashions based on the user's personal identification and appearance analysis. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention utilizes IoT technology in a full-length mirror, identifies individuals using a facial recognition system, and recommends hairstyles and fashion based on their appearance. When a user stands in front of the mirror, the facial recognition system identifies the user, analyzes the user's image in the mirror, and recommends hairstyles and fashion. This recommendation is implemented in cooperation with a group company's services (e.g., an electronic payment system or a fashion website). For example, when a user stands in front of the mirror, the facial recognition system captures the user's face with a camera and identifies the user by comparing it with pre-registered facial data. The system then analyzes the user's image in the mirror. The full-length mirror is equipped with a built-in camera that captures the user's entire body. This video data is analyzed using AI. For example, the AI ​​analyzes the user's hairstyle and clothing and recommends optimal hairstyles and fashion based on current trends and the user's preferences. Furthermore, the recommended hairstyles and fashions are provided in cooperation with a group company's services. For example, by linking with electronic payment systems or fashion sites, users can purchase recommended products on the spot. This allows users to easily adopt the latest trends. This system allows users to easily find the hairstyle and fashion that best suits them just by standing in front of the full-length mirror. Furthermore, by linking with the services of group companies, users can purchase recommended products on the spot, improving convenience. As a result, the IoT-enabled full-length mirror system can recognize the user's face, analyze their appearance, and recommend hairstyles and fashions, which can be provided in conjunction with the services of group companies.

[0029] A full-length mirror IoT system according to an embodiment includes a face authentication unit, an analysis unit, a recommendation unit, and a linking unit. The face authentication unit recognizes a user's face and identifies the individual. For example, the face authentication unit can identify the user by capturing a picture of the user's face with a camera and comparing the captured picture with pre-registered face data. The face authentication unit can also use multiple cameras to improve the accuracy of face authentication. The analysis unit captures a full-body image of the user with a camera built into the full-length mirror and analyzes the video data using AI. For example, the analysis unit can analyze the user's hairstyle and clothing and recommend optimal hairstyles and fashions based on current trends and the user's preferences. The recommendation unit can analyze the user's hairstyle and clothing using AI and recommend optimal hairstyles and fashions based on current trends and the user's preferences. For example, the recommendation unit can recommend optimal hairstyles and fashions based on the user's past purchase history and preferred styles. The linking unit links with an electronic payment system or a fashion site, allowing the user to purchase the recommended product on the spot. For example, by linking with an electronic payment system, the linking unit allows the user to purchase the recommended product on the spot. As a result, the full-length mirror IoT system according to the embodiment can recognize the user's face, analyze their appearance, recommend hairstyles and fashions, and provide these in cooperation with services provided by group companies.

[0030] The facial authentication unit can capture a user's face with a camera and compare it with pre-registered facial data. The facial authentication unit can identify an individual, for example, by capturing a user's face with a camera and comparing it with pre-registered facial data. When capturing a picture with a camera, the resolution and shooting angle of the camera used are important. For example, using a high-resolution camera can accurately recognize even the smallest details of the face. Furthermore, adjusting the shooting angle can capture the entire face. The pre-registered facial data is stored in a database and is used by the facial authentication unit for matching. The storage format and registration method of the facial data are also important. For example, storing the facial data in a high-precision image format can improve the accuracy of matching. This improves the accuracy of identifying an individual by capturing a user's face with a camera and comparing it with pre-registered facial data.

[0031] The analysis unit can capture the user's entire body with a camera built into the full-length mirror and analyze the video data using AI. For example, the analysis unit can capture the user's entire body with a camera built into the full-length mirror and analyze the video data using AI. The specific specifications and installation method of the camera built into the full-length mirror are also important. For example, by installing a high-resolution camera on top of the full-length mirror, it is possible to accurately capture the user's entire body. When analyzing video data using AI, the AI ​​technology and analysis method used are important. For example, by using image recognition technology using deep learning, it is possible to accurately analyze the user's appearance. This makes it possible to capture the user's entire body with a camera built into the full-length mirror and analyze the video data using AI, making it possible to accurately analyze the user's appearance.

[0032] The recommendation unit can use AI to analyze a user's hairstyle or clothing and recommend hairstyles or fashions based on current trends or the user's preferences. For example, the recommendation unit can use AI to analyze a user's hairstyle or clothing and recommend optimal hairstyles and fashions based on current trends and the user's preferences. Specific definitions and criteria for current trends are also important. For example, clarifying the trend data source and update frequency enables recommendations based on the latest trends. Methods and criteria for identifying user preferences are also important. For example, identifying user preferences based on past purchase history or survey results enables more personalized recommendations. This allows the system to analyze a user's hairstyle or clothing using AI and recommend optimal hairstyles and fashions based on current trends and the user's preferences, thereby making optimal suggestions to the user.

[0033] The linking unit can link with an electronic payment system or a fashion site to enable users to purchase recommended products on the spot. The linking unit can link with, for example, an electronic payment system or a fashion site to enable users to purchase recommended products on the spot. The specific type of electronic payment system and the linking method are also important. For example, clarifying the payment provider to be used and the linking process will enable smooth linking. The specific type of fashion site and the linking method are also important. For example, clarifying the list of linked sites and the linking process will enable users to easily purchase recommended products. As a result, by linking with an electronic payment system or a fashion site, users will be able to purchase recommended products on the spot.

[0034] The facial authentication unit can optimize the authentication speed by referring to the user's past authentication history during facial authentication. The facial authentication unit can optimize the authentication speed by referring to the user's past authentication history during facial authentication, for example. The method of saving and referencing the past authentication history is also important. For example, by clarifying the data saving format and the reference process, the past authentication history can be accurately referenced. The method and criteria for optimizing the authentication speed are also important. For example, by clarifying the parameters to be optimized and the optimization process, the authentication speed can be optimized. In this way, the authentication speed can be optimized by referring to the user's past authentication history.

[0035] The face authentication unit can improve authentication accuracy based on changes in the user's face angle or facial expression during face authentication. The face authentication unit improves authentication accuracy, for example, by taking into account changes in the user's face angle and facial expression during face authentication. Specific methods and standards for measuring face angles are also important. For example, by clarifying the range of angles to be measured and the sensor to be used, the face angle can be measured accurately. Specific methods and standards for measuring changes in facial expression are also important. For example, by clarifying the type of facial expression to be measured and the algorithm to be used, changes in facial expression can be measured accurately. This improves authentication accuracy by taking into account changes in the user's face angle and facial expression.

[0036] The facial recognition unit can optimize the authentication process by taking into account the user's geographical location information during facial recognition. For example, the facial recognition unit optimizes the authentication process by taking into account the user's geographical location information during facial recognition. The method of acquiring and using geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, geographical location information can be acquired accurately. This allows the authentication process to be optimized by taking into account the user's geographical location information.

[0037] The facial recognition unit can analyze the user's social media activity during facial recognition to improve authentication accuracy. The facial recognition unit can analyze the user's social media activity during facial recognition, for example, to improve authentication accuracy. Specific analysis methods and standards for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. This allows for analysis of the user's social media activity to improve authentication accuracy.

[0038] The analysis unit can improve the accuracy of the analysis by referring to the user's past fashion history during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to the user's past fashion history during analysis. The method of saving and referencing the past fashion history is also important. For example, by clarifying the data saving format and the reference process, the past fashion history can be accurately referenced. In this way, by referring to the user's past fashion history, the accuracy of the analysis is improved.

[0039] The analysis unit can optimize the analysis results based on the user's body shape or posture during analysis. For example, the analysis unit optimizes the analysis results by taking the user's body shape and posture into consideration during analysis. Specific measurement methods and standards for body shape are also important. For example, by clarifying the parts to be measured and the sensors to be used, the body shape can be measured accurately. Specific measurement methods and standards for posture are also important. For example, by clarifying the type of posture to be measured and the sensors to be used, the posture can be measured accurately. In this way, the analysis results are optimized by taking the user's body shape and posture into consideration.

[0040] The analysis unit can optimize the analysis process by taking into account the user's geographical location information during analysis. For example, the analysis unit optimizes the analysis process by taking into account the user's geographical location information during analysis. The method of acquiring and using geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, geographical location information can be acquired accurately. This allows the analysis process to be optimized by taking into account the user's geographical location information.

[0041] The analysis unit analyzes the user's social media activity during analysis, thereby improving the accuracy of the analysis. The analysis unit, for example, analyzes the user's social media activity during analysis, thereby improving the accuracy of the analysis. The specific analysis method and criteria for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. This improves the accuracy of the analysis by analyzing the user's social media activity.

[0042] The recommendation unit can improve recommendation accuracy by referring to the user's past purchase history when making a recommendation. For example, the recommendation unit can improve recommendation accuracy by referring to the user's past purchase history when making a recommendation. The method of saving and referencing past purchase history is also important. For example, by clarifying the data saving format and the reference process, past purchase history can be accurately referenced. In this way, by referring to the user's past purchase history, recommendation accuracy is improved.

[0043] The recommendation unit can optimize the recommendation content based on the user's current fashion trends when making a recommendation. For example, the recommendation unit optimizes the recommendation content by taking into account the user's current fashion trends when making a recommendation. Specific definitions and criteria for current fashion trends are also important. For example, by clarifying the trend data source and update frequency, it becomes possible to make recommendations based on the latest trends. This makes it possible to provide optimal recommendation content by taking into account the user's current fashion trends.

[0044] The recommendation unit can optimize the recommendation content based on the user's geographical location information when making a recommendation. For example, the recommendation unit optimizes the recommendation content by taking the user's geographical location information into consideration when making a recommendation. The method of acquiring and using the geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, the geographical location information can be acquired accurately. This allows the recommendation content to be optimized by taking the user's geographical location information into consideration.

[0045] The recommendation unit can analyze the user's social media activity when making a recommendation, thereby improving the accuracy of the recommendation. For example, the recommendation unit can analyze the user's social media activity when making a recommendation, thereby improving the accuracy of the recommendation. The specific analysis method and criteria for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. In this way, the accuracy of the recommendation can be improved by analyzing the user's social media activity.

[0046] The collaboration unit can optimize the collaboration process by referring to the user's past collaboration history when collaborating. For example, the collaboration unit can optimize the collaboration process by referring to the user's past collaboration history when collaborating. The method of saving and referencing the past collaboration history is also important. For example, by clarifying the data storage format and the reference process, the past collaboration history can be accurately referenced. In this way, the collaboration process can be optimized by referring to the user's past collaboration history.

[0047] The collaboration unit can optimize the collaboration content by taking into account the user's current purchasing willingness at the time of collaboration. For example, the collaboration unit optimizes the collaboration content by taking into account the user's current purchasing willingness at the time of collaboration. The specific measurement method and criteria for purchasing willingness are also important. For example, by clarifying the parameters to be measured and the algorithm to be used, purchasing willingness can be accurately measured. This allows the collaboration content to be optimized according to the user's purchasing willingness, making it possible to make the most suitable suggestions to the user.

[0048] The collaboration unit can optimize the collaboration process by taking into account the user's geographical location information during collaboration. For example, the collaboration unit optimizes the collaboration process by taking into account the user's geographical location information during collaboration. The method of acquiring and using the geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, the geographical location information can be acquired accurately. This allows the collaboration process to be optimized by taking into account the user's geographical location information.

[0049] The linking unit can analyze the user's social media activity at the time of linking and improve the accuracy of linking. For example, the linking unit can analyze the user's social media activity at the time of linking and improve the accuracy of linking. The specific analysis method and criteria for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. This improves the accuracy of linking by analyzing the user's social media activity.

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

[0051] The full-length mirror IoT system can also be equipped with a health management unit that monitors the user's health condition. The health management unit acquires data such as the user's weight, height, and body fat percentage, and evaluates the user's health condition based on this data. For example, a sensor built into the full-length mirror can measure the user's weight and compare it with past data to understand weight fluctuations. The system can also analyze the user's posture and suggest improvements. Furthermore, the health management unit can provide appropriate exercise and dietary advice based on the user's health condition. This allows the user to receive health monitoring and improvement suggestions simply by standing in front of the full-length mirror.

[0052] The face authentication unit can estimate the user's age and gender based on the user's facial features and optimize the authentication process based on the estimated age and gender. For example, the authentication accuracy can be improved by estimating the user's age using an age estimation algorithm and setting authentication parameters according to the user's age. It can also estimate the user's gender using a gender estimation algorithm and apply an authentication process according to the user's gender. This improves authentication accuracy by optimizing the authentication process based on the user's age and gender.

[0053] The analysis unit can analyze the user's movements in real time and recommend fashion and hairstyles based on the movements. For example, it can analyze the user's walking movements and suggest fashion items that suit the movements. It can also analyze the user's movements when touching their hair and suggest hairstyle changes. It can also estimate the amount of exercise based on the user's movements and recommend fashion items that are suitable for exercise. This makes it possible to suggest optimal fashion and hairstyles based on the user's movements.

[0054] The recommendation unit can adjust the recommendation content based on the user's lifestyle. For example, if the user likes outdoor activities, it can recommend fashion items suitable for outdoor activities. Also, if the user wishes to use the device in a business setting, it can suggest fashion items suitable for business. Furthermore, it can recommend fashion items according to the season and weather based on the user's lifestyle. This makes it possible to suggest fashion items that are best suited to the user's lifestyle.

[0055] The linking unit can strengthen links with specific brands and shops based on the user's purchase history. For example, if a user frequently purchases products from a specific brand, it can provide that brand's new product and sale information preferentially. Also, if a user has a large purchase history from a specific shop, it can strengthen links with that shop and provide special discount and campaign information. Furthermore, it can recommend related products based on the user's purchase history. This makes it possible to provide more personalized services based on the user's purchase history.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The facial recognition unit recognizes the user's face and identifies the individual. For example, the facial recognition unit can identify the user by taking a picture of the user's face with a camera and comparing it with pre-registered facial data. The facial recognition unit can also use multiple cameras to improve the accuracy of facial recognition. Step 2: The analysis unit captures the user's entire body using a camera built into the full-length mirror and analyzes the video data using AI. For example, the analysis unit can analyze the user's hairstyle and clothing and recommend optimal hairstyles and fashions based on current trends and the user's preferences. Step 3: The recommendation unit uses AI to analyze the user's hairstyle and clothing, and recommends the most suitable hairstyle and fashion based on current trends and the user's preferences. For example, the recommendation unit can recommend the most suitable hairstyle and fashion based on the user's past purchase history and preferred styles. Step 4: The linking unit links with electronic payment systems and fashion sites to enable users to purchase recommended products on the spot. For example, by linking with an electronic payment system, the linking unit allows users to purchase recommended products on the spot.

[0058] (Example 2) A system according to an embodiment of the present invention utilizes IoT technology in a full-length mirror, identifies individuals using a facial recognition system, and recommends hairstyles and fashion based on their appearance. When a user stands in front of the mirror, the facial recognition system identifies the user, analyzes the user's image in the mirror, and recommends hairstyles and fashion. This recommendation is implemented in cooperation with a group company's services (e.g., an electronic payment system or a fashion website). For example, when a user stands in front of the mirror, the facial recognition system captures the user's face with a camera and identifies the user by comparing it with pre-registered facial data. The system then analyzes the user's image in the mirror. The full-length mirror is equipped with a built-in camera that captures the user's entire body. This video data is analyzed using AI. For example, the AI ​​analyzes the user's hairstyle and clothing and recommends optimal hairstyles and fashion based on current trends and the user's preferences. Furthermore, the recommended hairstyles and fashions are provided in cooperation with a group company's services. For example, by linking with electronic payment systems or fashion sites, users can purchase recommended products on the spot. This allows users to easily adopt the latest trends. This system allows users to easily find the hairstyle and fashion that best suits them just by standing in front of the full-length mirror. Furthermore, by linking with the services of group companies, users can purchase recommended products on the spot, improving convenience. As a result, the IoT-enabled full-length mirror system can recognize the user's face, analyze their appearance, and recommend hairstyles and fashions, which can be provided in conjunction with the services of group companies.

[0059] A full-length mirror IoT system according to an embodiment includes a face authentication unit, an analysis unit, a recommendation unit, and a linking unit. The face authentication unit recognizes a user's face and identifies the individual. For example, the face authentication unit can identify the user by capturing a picture of the user's face with a camera and comparing the captured picture with pre-registered face data. The face authentication unit can also use multiple cameras to improve the accuracy of face authentication. The analysis unit captures a full-body image of the user with a camera built into the full-length mirror and analyzes the video data using AI. For example, the analysis unit can analyze the user's hairstyle and clothing and recommend optimal hairstyles and fashions based on current trends and the user's preferences. The recommendation unit can analyze the user's hairstyle and clothing using AI and recommend optimal hairstyles and fashions based on current trends and the user's preferences. For example, the recommendation unit can recommend optimal hairstyles and fashions based on the user's past purchase history and preferred styles. The linking unit links with an electronic payment system or a fashion site, allowing the user to purchase the recommended product on the spot. For example, by linking with an electronic payment system, the linking unit allows the user to purchase the recommended product on the spot. As a result, the full-length mirror IoT system according to the embodiment can recognize the user's face, analyze their appearance, recommend hairstyles and fashions, and provide these in cooperation with services provided by group companies.

[0060] The facial authentication unit can capture a user's face with a camera and compare it with pre-registered facial data. The facial authentication unit can identify an individual, for example, by capturing a user's face with a camera and comparing it with pre-registered facial data. When capturing a picture with a camera, the resolution and shooting angle of the camera used are important. For example, using a high-resolution camera can accurately recognize even the smallest details of the face. Furthermore, adjusting the shooting angle can capture the entire face. The pre-registered facial data is stored in a database and is used by the facial authentication unit for matching. The storage format and registration method of the facial data are also important. For example, storing the facial data in a high-precision image format can improve the accuracy of matching. This improves the accuracy of identifying an individual by capturing a user's face with a camera and comparing it with pre-registered facial data.

[0061] The analysis unit can capture the user's entire body with a camera built into the full-length mirror and analyze the video data using AI. For example, the analysis unit can capture the user's entire body with a camera built into the full-length mirror and analyze the video data using AI. The specific specifications and installation method of the camera built into the full-length mirror are also important. For example, by installing a high-resolution camera on top of the full-length mirror, it is possible to accurately capture the user's entire body. When analyzing video data using AI, the AI ​​technology and analysis method used are important. For example, by using image recognition technology using deep learning, it is possible to accurately analyze the user's appearance. This makes it possible to capture the user's entire body with a camera built into the full-length mirror and analyze the video data using AI, making it possible to accurately analyze the user's appearance.

[0062] The recommendation unit can use AI to analyze a user's hairstyle or clothing and recommend hairstyles or fashions based on current trends or the user's preferences. For example, the recommendation unit can use AI to analyze a user's hairstyle or clothing and recommend optimal hairstyles and fashions based on current trends and the user's preferences. Specific definitions and criteria for current trends are also important. For example, clarifying the trend data source and update frequency enables recommendations based on the latest trends. Methods and criteria for identifying user preferences are also important. For example, identifying user preferences based on past purchase history or survey results enables more personalized recommendations. This allows the system to analyze a user's hairstyle or clothing using AI and recommend optimal hairstyles and fashions based on current trends and the user's preferences, thereby making optimal suggestions to the user.

[0063] The linking unit can link with an electronic payment system or a fashion site to enable users to purchase recommended products on the spot. The linking unit can link with, for example, an electronic payment system or a fashion site to enable users to purchase recommended products on the spot. The specific type of electronic payment system and the linking method are also important. For example, clarifying the payment provider to be used and the linking process will enable smooth linking. The specific type of fashion site and the linking method are also important. For example, clarifying the list of linked sites and the linking process will enable users to easily purchase recommended products. As a result, by linking with an electronic payment system or a fashion site, users will be able to purchase recommended products on the spot.

[0064] The face authentication unit can estimate the user's emotions and adjust the accuracy of face authentication based on the estimated user emotions. The face authentication unit, for example, estimates the user's emotions and adjusts the accuracy of face authentication based on the estimated user emotions. The specific method and criteria for emotion estimation are also important. For example, clarifying the algorithm to be used and the accuracy of the estimation makes it possible to accurately estimate emotions. The method and criteria for adjusting the accuracy of face authentication are also important. For example, clarifying the parameters to be adjusted and the adjustment process makes it possible to optimize the accuracy of face authentication. As a result, the accuracy of face authentication can be improved by adjusting the accuracy of face authentication based on the user's emotions.

[0065] The facial authentication unit can optimize the authentication speed by referring to the user's past authentication history during facial authentication. The facial authentication unit can optimize the authentication speed by referring to the user's past authentication history during facial authentication, for example. The method of saving and referencing the past authentication history is also important. For example, by clarifying the data saving format and the reference process, the past authentication history can be accurately referenced. The method and criteria for optimizing the authentication speed are also important. For example, by clarifying the parameters to be optimized and the optimization process, the authentication speed can be optimized. In this way, the authentication speed can be optimized by referring to the user's past authentication history.

[0066] The face authentication unit can improve authentication accuracy based on changes in the user's face angle or facial expression during face authentication. The face authentication unit improves authentication accuracy, for example, by taking into account changes in the user's face angle and facial expression during face authentication. Specific methods and standards for measuring face angles are also important. For example, by clarifying the range of angles to be measured and the sensor to be used, the face angle can be measured accurately. Specific methods and standards for measuring changes in facial expression are also important. For example, by clarifying the type of facial expression to be measured and the algorithm to be used, changes in facial expression can be measured accurately. This improves authentication accuracy by taking into account changes in the user's face angle and facial expression.

[0067] The facial authentication unit can estimate the user's emotions and adjust the display method of the authentication result based on the estimated user's emotions. The facial authentication unit, for example, estimates the user's emotions and adjusts the display method of the authentication result based on the estimated user's emotions. The display method and criteria for the authentication result are also important. For example, by clarifying the type of information to be displayed and the display format, it is possible to display the result in a way that is easy for the user to view. In this way, by adjusting the display method of the authentication result based on the user's emotions, it is possible to display the result in a way that is easy for the user to view.

[0068] The facial recognition unit can optimize the authentication process by taking into account the user's geographical location information during facial recognition. For example, the facial recognition unit optimizes the authentication process by taking into account the user's geographical location information during facial recognition. The method of acquiring and using geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, geographical location information can be acquired accurately. This allows the authentication process to be optimized by taking into account the user's geographical location information.

[0069] The facial recognition unit can analyze the user's social media activity during facial recognition to improve authentication accuracy. The facial recognition unit can analyze the user's social media activity during facial recognition, for example, to improve authentication accuracy. Specific analysis methods and standards for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. This allows for analysis of the user's social media activity to improve authentication accuracy.

[0070] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis algorithm based on the estimated user's emotions. The method and criteria for adjusting the analysis algorithm are also important. For example, the analysis algorithm can be optimized by clarifying the parameters to be adjusted and the adjustment process. This improves the analysis accuracy by adjusting the analysis algorithm based on the user's emotions.

[0071] The analysis unit can improve the accuracy of the analysis by referring to the user's past fashion history during analysis. The analysis unit can improve the accuracy of the analysis by, for example, referring to the user's past fashion history during analysis. The method of saving and referencing the past fashion history is also important. For example, by clarifying the data saving format and the reference process, the past fashion history can be accurately referenced. In this way, by referring to the user's past fashion history, the accuracy of the analysis is improved.

[0072] The analysis unit can optimize the analysis results based on the user's body shape or posture during analysis. For example, the analysis unit optimizes the analysis results by taking the user's body shape and posture into consideration during analysis. Specific measurement methods and standards for body shape are also important. For example, by clarifying the parts to be measured and the sensors to be used, the body shape can be measured accurately. Specific measurement methods and standards for posture are also important. For example, by clarifying the type of posture to be measured and the sensors to be used, the posture can be measured accurately. In this way, the analysis results are optimized by taking the user's body shape and posture into consideration.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The display method and criteria for the analysis results are also important. For example, by clarifying the type of information to be displayed and the display format, it is possible to display the analysis results in a way that is easy for the user to view. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to display the analysis results in a way that is easy for the user to view.

[0074] The analysis unit can optimize the analysis process by taking into account the user's geographical location information during analysis. For example, the analysis unit optimizes the analysis process by taking into account the user's geographical location information during analysis. The method of acquiring and using geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, geographical location information can be acquired accurately. This allows the analysis process to be optimized by taking into account the user's geographical location information.

[0075] The analysis unit analyzes the user's social media activity during analysis, thereby improving the accuracy of the analysis. The analysis unit, for example, analyzes the user's social media activity during analysis, thereby improving the accuracy of the analysis. The specific analysis method and criteria for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. This improves the accuracy of the analysis by analyzing the user's social media activity.

[0076] The recommendation unit can estimate the user's emotions and adjust the recommendation content based on the estimated user emotions. The recommendation unit, for example, estimates the user's emotions and adjusts the recommendation content based on the estimated user emotions. The method and criteria for adjusting the recommendation content are also important. For example, by clarifying the parameters to be adjusted and the adjustment process, the recommendation content can be optimized. This makes it possible to adjust the recommendation content based on the user's emotions and provide the most suitable recommendation for the user.

[0077] The recommendation unit can improve recommendation accuracy by referring to the user's past purchase history when making a recommendation. For example, the recommendation unit can improve recommendation accuracy by referring to the user's past purchase history when making a recommendation. The method of saving and referencing past purchase history is also important. For example, by clarifying the data saving format and the reference process, past purchase history can be accurately referenced. In this way, by referring to the user's past purchase history, recommendation accuracy is improved.

[0078] The recommendation unit can optimize the recommendation content based on the user's current fashion trends when making a recommendation. For example, the recommendation unit optimizes the recommendation content by taking into account the user's current fashion trends when making a recommendation. Specific definitions and criteria for current fashion trends are also important. For example, by clarifying the trend data source and update frequency, it becomes possible to make recommendations based on the latest trends. This makes it possible to provide optimal recommendation content by taking into account the user's current fashion trends.

[0079] The recommendation unit can estimate the user's emotions and adjust the display method of recommendations based on the estimated user emotions. The recommendation unit, for example, estimates the user's emotions and adjusts the display method of recommendations based on the estimated user emotions. The display method and criteria for recommendations are also important. For example, by clarifying the type of information to be displayed and the display format, it is possible to display information that is easy for users to view. In this way, by adjusting the display method of recommendations based on the user's emotions, it is possible to display information that is easy for users to view.

[0080] The recommendation unit can optimize the recommendation content based on the user's geographical location information when making a recommendation. For example, the recommendation unit optimizes the recommendation content by taking the user's geographical location information into consideration when making a recommendation. The method of acquiring and using the geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, the geographical location information can be acquired accurately. This allows the recommendation content to be optimized by taking the user's geographical location information into consideration.

[0081] The recommendation unit can analyze the user's social media activity when making a recommendation, thereby improving the accuracy of the recommendation. For example, the recommendation unit can analyze the user's social media activity when making a recommendation, thereby improving the accuracy of the recommendation. The specific analysis method and criteria for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. In this way, the accuracy of the recommendation can be improved by analyzing the user's social media activity.

[0082] The collaboration unit can estimate the user's emotions and select a collaborative service based on the estimated user emotions. The collaboration unit, for example, estimates the user's emotions and selects a collaborative service based on the estimated user emotions. The method and criteria for selecting a collaborative service are also important. For example, by clarifying the type of service to be selected and the selection process, it is possible to provide the user with the optimal service. In this way, by selecting a collaborative service based on the user's emotions, it is possible to provide the user with the optimal service.

[0083] The collaboration unit can optimize the collaboration process by referring to the user's past collaboration history when collaborating. For example, the collaboration unit can optimize the collaboration process by referring to the user's past collaboration history when collaborating. The method of saving and referencing the past collaboration history is also important. For example, by clarifying the data storage format and the reference process, the past collaboration history can be accurately referenced. In this way, the collaboration process can be optimized by referring to the user's past collaboration history.

[0084] The collaboration unit can optimize the collaboration content by taking into account the user's current purchasing willingness at the time of collaboration. For example, the collaboration unit optimizes the collaboration content by taking into account the user's current purchasing willingness at the time of collaboration. The specific measurement method and criteria for purchasing willingness are also important. For example, by clarifying the parameters to be measured and the algorithm to be used, purchasing willingness can be accurately measured. This allows the collaboration content to be optimized according to the user's purchasing willingness, making it possible to make the most suitable suggestions to the user.

[0085] The linking unit can estimate the user's emotions and adjust the display method of the linking results based on the estimated user emotions. The linking unit, for example, estimates the user's emotions and adjusts the display method of the linking results based on the estimated user emotions. The display method and criteria for the linking results are also important. For example, by clarifying the type of information to be displayed and the display format, it is possible to display the linking results in a way that is easy for the user to view. In this way, by adjusting the display method of the linking results based on the user's emotions, it is possible to display the linking results in a way that is easy for the user to view.

[0086] The collaboration unit can optimize the collaboration process by taking into account the user's geographical location information during collaboration. For example, the collaboration unit optimizes the collaboration process by taking into account the user's geographical location information during collaboration. The method of acquiring and using the geographical location information is also important. For example, by clarifying the accuracy of the GPS data and location information used, the geographical location information can be acquired accurately. This allows the collaboration process to be optimized by taking into account the user's geographical location information.

[0087] The linking unit can analyze the user's social media activity at the time of linking and improve the accuracy of linking. For example, the linking unit can analyze the user's social media activity at the time of linking and improve the accuracy of linking. The specific analysis method and criteria for social media activity are also important. For example, by clarifying the type of data to be analyzed and the algorithm to be used, social media activity can be analyzed accurately. This improves the accuracy of linking by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the face authentication unit, analysis unit, recommendation unit, and linking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the face authentication unit is realized by capturing an image of the user's face using the camera 42 of the smart device 14 and comparing the captured image with the face data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized by capturing an image of the user's entire body using the camera 42 built into a full-length mirror and analyzing the video data using the specific processing unit 290 of the data processing device 12. The recommendation unit is realized by analyzing the user's hairstyle and clothing using the specific processing unit 290 of the data processing device 12 and recommending an optimal hairstyle and fashion. The linking unit is realized by linking the specific processing unit 290 of the data processing device 12 with an electronic payment system or a fashion site, allowing the user to purchase recommended products on the spot. === Hard Collateral 1-2 === Each of the multiple elements including the face authentication unit, analysis unit, recommendation unit, and linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the face authentication unit is realized by capturing an image of the user's face using the camera 42 of the smart glasses 214 and comparing the captured image with the face data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized by capturing an image of the user's entire body using the camera 42 built into a full-length mirror and analyzing the video data using the specific processing unit 290 of the data processing device 12. The recommendation unit is realized by analyzing the user's hairstyle and clothing using the specific processing unit 290 of the data processing device 12 and recommending an optimal hairstyle and fashion. The linking unit is realized by linking the specific processing unit 290 of the data processing device 12 with an electronic payment system or a fashion site, allowing the user to purchase recommended products on the spot. === Hard Collateral 1-3 === Each of the multiple elements including the face authentication unit, analysis unit, recommendation unit, and linking unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the face authentication unit is realized by capturing an image of the user's face using the camera 42 of the headset-type terminal 314 and comparing the captured image with the face data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized by capturing an image of the user's entire body using the camera 42 built into a full-length mirror and analyzing the video data using the specific processing unit 290 of the data processing device 12. The recommendation unit is realized by analyzing the user's hairstyle and clothing using the specific processing unit 290 of the data processing device 12 and recommending an optimal hairstyle and fashion. The linking unit is realized by linking the specific processing unit 290 of the data processing device 12 with an electronic payment system or a fashion site, allowing the user to purchase recommended products on the spot. === Hard Collateral 1-4 === Each of the multiple elements including the face authentication unit, analysis unit, recommendation unit, and linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the face authentication unit is realized by capturing an image of the user's face using the camera 42 of the robot 414 and comparing the captured image with the face data using the specific processing unit 290 of the data processing device 12. The analysis unit is realized by capturing an image of the user's entire body using the camera 42 built into a full-length mirror and analyzing the video data using the specific processing unit 290 of the data processing device 12. The recommendation unit is realized by analyzing the user's hairstyle and clothing using the specific processing unit 290 of the data processing device 12 and recommending an optimal hairstyle and fashion. The linking unit is realized by linking the specific processing unit 290 of the data processing device 12 with an electronic payment system or a fashion site, allowing the user to purchase recommended products on the spot.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The full-length mirror IoT system can also be equipped with a health management unit that monitors the user's health condition. The health management unit acquires data such as the user's weight, height, and body fat percentage, and evaluates the user's health condition based on this data. For example, a sensor built into the full-length mirror can measure the user's weight and compare it with past data to understand weight fluctuations. The system can also analyze the user's posture and suggest improvements. Furthermore, the health management unit can provide appropriate exercise and dietary advice based on the user's health condition. This allows the user to receive health monitoring and improvement suggestions simply by standing in front of the full-length mirror.

[0090] The face authentication unit can estimate the user's age and gender based on the user's facial features and optimize the authentication process based on the estimated age and gender. For example, the authentication accuracy can be improved by estimating the user's age using an age estimation algorithm and setting authentication parameters according to the user's age. It can also estimate the user's gender using a gender estimation algorithm and apply an authentication process according to the user's gender. This improves authentication accuracy by optimizing the authentication process based on the user's age and gender.

[0091] The analysis unit can analyze the user's movements in real time and recommend fashion and hairstyles based on the movements. For example, it can analyze the user's walking movements and suggest fashion items that suit the movements. It can also analyze the user's movements when touching their hair and suggest hairstyle changes. It can also estimate the amount of exercise based on the user's movements and recommend fashion items that are suitable for exercise. This makes it possible to suggest optimal fashion and hairstyles based on the user's movements.

[0092] The recommendation unit can adjust the recommendation content based on the user's lifestyle. For example, if the user likes outdoor activities, it can recommend fashion items suitable for outdoor activities. Also, if the user wishes to use the device in a business setting, it can suggest fashion items suitable for business. Furthermore, it can recommend fashion items according to the season and weather based on the user's lifestyle. This makes it possible to suggest fashion items that are best suited to the user's lifestyle.

[0093] The linking unit can strengthen links with specific brands and shops based on the user's purchase history. For example, if a user frequently purchases products from a specific brand, it can provide that brand's new product and sale information preferentially. Also, if a user has a large purchase history from a specific shop, it can strengthen links with that shop and provide special discount and campaign information. Furthermore, it can recommend related products based on the user's purchase history. This makes it possible to provide more personalized services based on the user's purchase history.

[0094] The face authentication unit can estimate the user's emotions and flexibly adjust the authentication process based on the estimated emotions. For example, if the user is feeling stressed, the authentication process can be simplified to reduce the burden on the user. Alternatively, if the user is relaxed, the authentication process can be performed as usual. Furthermore, the method of displaying the authentication result can be adjusted according to the user's emotions. This makes it possible to improve the user experience by flexibly adjusting the authentication process based on the user's emotions.

[0095] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is excited, the analysis results can be displayed simply to prevent information overload. On the other hand, if the user is relaxed, detailed analysis results can be displayed. Furthermore, the display speed of the analysis results can be adjusted according to the user's emotions. This allows the display method of the analysis results to be adjusted based on the user's emotions, making it possible to display the results in a way that is easy for the user to view.

[0096] The recommendation unit can estimate the user's emotions and adjust the recommendation content based on the estimated emotions. For example, if the user is feeling down, it can improve their mood by recommending fashion items with bright colors and fun designs. On the other hand, if the user is excited, it can suggest items with a calming design. Furthermore, it can adjust the frequency and timing of recommendations according to the user's emotions. This makes it possible to provide optimal recommendations based on the user's emotions.

[0097] The linking unit can estimate the user's emotions and select a linked service based on the estimated emotions. For example, if the user is feeling stressed, the linking unit can link with a service that provides relaxation services or stress relief goods. If the user is excited, the linking unit can link with a service that provides entertainment services or activities. Furthermore, the linking unit can adjust the priority of linked services according to the user's emotions. This makes it possible to provide the optimal linked service based on the user's emotions.

[0098] The linking unit can estimate the user's emotions and adjust the display method of the linking results based on the estimated emotions. For example, if the user is relaxed, the linking results can be displayed in detail to allow the user to fully understand the information. If the user is in a hurry, the linking results can be displayed concisely to support quick decision-making. Furthermore, the display format and color of the linking results can be adjusted according to the user's emotions. This allows the display method of the linking results to be adjusted based on the user's emotions, making it easier for the user to see.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The facial recognition unit recognizes the user's face and identifies the individual. For example, the facial recognition unit can identify the user by taking a picture of the user's face with a camera and comparing it with pre-registered facial data. The facial recognition unit can also use multiple cameras to improve the accuracy of facial recognition. Step 2: The analysis unit captures the user's entire body using a camera built into the full-length mirror and analyzes the video data using AI. For example, the analysis unit can analyze the user's hairstyle and clothing and recommend optimal hairstyles and fashions based on current trends and the user's preferences. Step 3: The recommendation unit uses AI to analyze the user's hairstyle and clothing, and recommends the most suitable hairstyle and fashion based on current trends and the user's preferences. For example, the recommendation unit can recommend the most suitable hairstyle and fashion based on the user's past purchase history and preferred styles. Step 4: The linking unit links with electronic payment systems and fashion sites to enable users to purchase recommended products on the spot. For example, by linking with an electronic payment system, the linking unit allows users to purchase recommended products on the spot.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 face authentication unit that recognizes the face of a user and identifies the individual; an analysis unit that analyzes the appearance of the user identified by the face authentication unit; a recommendation unit that recommends a hairstyle or fashion based on the data analyzed by the analysis unit; a linking unit that provides the products recommended by the recommendation unit in cooperation with services of a group company. A system characterized by:

2. The face authentication unit The user's face is photographed with a camera and compared with pre-registered facial data. The system of claim 1 .

3. The analysis unit The mirror's built-in camera captures the user's entire body, and the image data is analyzed using AI. The system of claim 1 .

4. The recommendation unit Using AI to analyze a user's hairstyle or clothing and recommend hairstyles or fashion based on current trends or the user's preferences The system of claim 1 .

5. The linking unit is Linking with electronic payment systems or fashion sites to allow users to purchase recommended products on the spot The system of claim 1 .

6. The face authentication unit Estimate the user's emotions and adjust the accuracy of facial recognition based on the estimated user emotions. The system of claim 1 .

7. The face authentication unit When performing facial recognition, the authentication speed is optimized by referring to the user's past authentication history. The system of claim 1 .

8. The face authentication unit Improves accuracy of facial recognition based on changes in the user's facial angle or facial expression. The system of claim 1 .

Images