system

Abstract

The system according to the embodiment aims to fully automate the equipment lending and returning process, improving efficiency and accuracy. [Solution] The system according to the embodiment includes a registration unit, a recognition unit, an unlocking unit, a tracking unit, a recording unit, and a guide unit. The registration unit registers a facial image of a user. The recognition unit recognizes the user based on the facial image registered by the registration unit. The unlocking unit unlocks the door of the equipment storage room for the user recognized by the recognition unit. The tracking unit tracks borrowed equipment using specific object recognition technology. The recording unit records returned equipment. The guide unit uses a virtual assistant to guide the user through the entire equipment lending and returning process.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of lending and returning equipment is often done manually, posing challenges in efficiency and accuracy.

[0005] The system according to the embodiment aims to fully automate the equipment lending and returning process, improving efficiency and accuracy. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a recognition unit, an unlocking unit, a tracking unit, a recording unit, and a guide unit. The registration unit registers a facial image of a user. The recognition unit recognizes the user based on the facial image registered by the registration unit. The unlocking unit unlocks the door of the equipment storage room for the user recognized by the recognition unit. The tracking unit tracks borrowed equipment using specific object recognition technology. The recording unit records returned equipment. The guide unit uses a virtual assistant to guide the user through the entire equipment lending and returning process. [Effects of the Invention]

[0007] The system according to the embodiment can fully automate the equipment lending and returning process, improving efficiency and accuracy. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An equipment lending and return system according to an embodiment of the present invention utilizes multimodal generative AI technology to provide a fully automated solution for lending and returning equipment. The system registers users and recognizes them using facial recognition technology. Then, for the recognized users, it unlocks the equipment storage door. At the same time, it uses object recognition technology to accurately track what has been borrowed and record returns. A virtual assistant guides users through the entire equipment lending and return process and provides support. For example, when registering a user, the system registers the user's facial image in the system and recognizes the user using facial recognition technology. When the user borrows equipment, the system performs facial recognition to recognize the user and confirm that the user has legitimate rights. Then, for the recognized users, it unlocks the equipment storage door. For example, after the user passes facial recognition, the system automatically unlocks the equipment storage door, allowing the user to retrieve the equipment. At the same time, it uses object recognition technology to accurately track what has been borrowed. For example, when a user takes out equipment, the system performs object recognition and records which equipment has been borrowed. This makes equipment management easier. The same is true when equipment is returned. For example, when a user returns equipment, the system performs object recognition and records which equipment has been returned. This allows for accurate tracking of the equipment's return status. In addition, a virtual assistant guides the user through the entire equipment lending and returning process, providing support to the user. For example, when a user borrows equipment, the virtual assistant explains the procedure to help the user borrow the equipment smoothly. Similarly, when returning equipment, the virtual assistant explains the procedure to help the user return the equipment smoothly. This fully automates equipment lending and returning, providing a convenient and efficient system for users. The equipment lending and returning system can fully automate equipment lending and returning by registering the user's facial image and performing recognition, unlocking, tracking, recording, and guidance.

[0029] An equipment lending and returning system according to an embodiment includes a registration unit, a recognition unit, an unlocking unit, a tracking unit, a recording unit, and a guide unit. The registration unit registers a facial image of a user. For example, the registration unit may take a facial image of the user with a camera and store it in the system. The recognition unit recognizes the user based on the facial image registered by the registration unit. For example, the recognition unit may recognize the user using facial recognition technology. Facial recognition technology includes deep learning-based facial recognition and 2D / 3D facial recognition. The unlocking unit unlocks the door of the equipment storage unit for the user recognized by the recognition unit. For example, the unlocking unit unlocks the door using an electronic lock. The tracking unit tracks borrowed equipment using object recognition technology. For example, the tracking unit tracks equipment using an image recognition algorithm or sensor technology. The recording unit records returned equipment. For example, the recording unit stores information about returned equipment in a database. The guide unit uses a virtual assistant to guide the user through the entire equipment lending and returning process. For example, the guide unit provides guidance to the user using voice recognition and natural language processing. As a result, the equipment lending and returning system according to the embodiment can fully automate the lending and returning of equipment by registering the user's facial image and performing recognition, unlocking, tracking, recording, and guidance.

[0030] The recognition unit can recognize users using facial recognition technology. Facial recognition technology includes deep learning-based facial recognition and 2D / 3D facial recognition. For example, deep learning-based facial recognition can learn large amounts of facial image data and perform highly accurate authentication. 2D facial recognition is a technology that extracts facial features based on a flat image and performs authentication, while 3D facial recognition is a technology that performs authentication based on the three-dimensional shape of the face. As a result, the use of facial recognition technology improves the accuracy of user recognition. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input facial image data to a generation AI and have the generation AI perform facial recognition.

[0031] The unlocking unit can unlock the door of the equipment storage cabinet for the recognized user. The unlocking unit unlocks the door using, for example, an electronic lock. Electronic locks include magnetic locks, electric locks, smart locks, etc. For example, a magnetic lock is a lock that secures the door using magnetic force, and an electric lock is a device that controls the lock using an electric signal. A smart lock is a device that unlocks the lock using a smartphone, card key, etc. This improves convenience by automatically unlocking the lock for the recognized user. Some or all of the above-mentioned processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input information about the user recognized by the recognition unit into the generation AI and have the generation AI execute control of unlocking the lock.

[0032] The tracking unit can track the rented equipment using object recognition technology. Object recognition technology includes image recognition algorithms and sensor technology. For example, image recognition algorithms are a technology that recognizes and tracks objects from images captured by a camera. Sensor technology includes RFID tags and GPS. For example, RFID tags are a technology that identifies the location of an object using wireless communication, and GPS is a technology that identifies the location of an object using satellites. As a result, the use of object recognition technology improves the tracking accuracy of the rented equipment. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input object recognition data to a generation AI and have the generation AI track the object.

[0033] The recording unit can record returned equipment. The recording unit saves information about the returned equipment in a database, for example. The database includes the equipment ID, the return date and time, and information about the returner. For example, an SQL database or a NoSQL database is used as the database. An SQL database is a database for efficiently managing structured data, while a NoSQL database is a database for efficiently managing unstructured data and large-scale data. This facilitates equipment management by accurately recording returned equipment. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input information about the returned equipment into a generation AI and have the generation AI perform the recording process.

[0034] The guide unit can use a virtual assistant to guide users through the entire equipment rental and return process. Virtual assistants use technologies such as voice recognition and natural language processing. For example, voice recognition technology converts a user's voice into text and formats it understandable to the system. Natural language processing technology analyzes text data and understands its meaning. This allows users to smoothly rent and return equipment using a virtual assistant. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without AI. For example, the guide unit can input the user's voice data into a generation AI and have the generation AI execute the content of the guide.

[0035] The registration unit can analyze the user's past usage history and select the optimal facial image registration method. The registration unit, for example, retrieves and analyzes the user's past usage history from a database. For example, a detailed guide can be provided to a user who has failed to register a facial image in the past. A simplified procedure can also be provided to a user who has successfully registered a facial image in the past. Furthermore, it is possible to register a facial image at a specific time period based on the past usage history. In this way, by analyzing the past usage history, the optimal facial image registration method can be provided. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's past usage history data into a generation AI and have the generation AI select the optimal registration method.

[0036] The registration unit can perform filtering based on the user's current activity status and areas of interest when registering a facial image. The registration unit, for example, monitors the user's current activity status in real time and analyzes the user's areas of interest. For example, if the user is at work, the registration of facial images can be postponed. Also, if the user is engaged in a hobby activity, the registration of facial images can be prioritized. Furthermore, if the user is traveling, the registration of facial images can be simplified. This improves the accuracy of registration by registering facial images based on the user's activity status and areas of interest. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's activity status data to the generation AI and cause the generation AI to perform the filtering process.

[0037] When registering a facial image, the registration unit can prioritize registering highly relevant facial images by taking into account the user's geographical location information. The registration unit, for example, acquires and analyzes the user's geographical location information from GPS data or a location information service. For example, when the user is at home, facial images at home can be prioritized for registration. Also, when the user is at work, facial images at work can be prioritized for registration. Furthermore, when the user is traveling, facial images at the travel destination can also be prioritized for registration. In this way, by taking the geographical location information into account, highly relevant facial images can be prioritized for registration. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority order of facial images.

[0038] When registering a facial image, the registration unit can analyze the user's social media activity and register related facial images. The registration unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the registration unit can register facial images by referring to images frequently posted by the user on social media. The registration unit can also register facial images by referring to images tagged by the user on social media. Furthermore, the registration unit can also register facial images by referring to images shared by the user on social media. In this way, related facial images can be registered by analyzing social media activity. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's social media data into the generation AI and cause the generation AI to register facial images.

[0039] During recognition, the recognition unit can optimize the recognition algorithm by referring to the user's past recognition history. The recognition unit, for example, retrieves and analyzes the user's past recognition history from a database. For example, the recognition algorithm can be adjusted for a user who has failed recognition in the past. The same algorithm can also be used for a user who has performed recognition smoothly in the past. Furthermore, the recognition algorithm can be optimized for a specific time period based on the past recognition history. In this way, the recognition algorithm can be optimized by referring to the past recognition history. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's past recognition history data into the generation AI and cause the generation AI to optimize the recognition algorithm.

[0040] The recognition unit can improve the accuracy of recognition by taking into account the user's attribute information during recognition. The recognition unit, for example, acquires and analyzes the user's attribute information from a database. For example, the recognition accuracy can be adjusted by taking into account the user's age. The recognition accuracy can also be adjusted by taking into account the user's gender. Furthermore, the recognition accuracy can be adjusted by taking into account the user's occupation. In this way, the recognition accuracy is improved by taking into account the user's attribute information. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's attribute information into the generation AI and cause the generation AI to adjust the recognition accuracy.

[0041] The recognition unit can perform recognition taking into account the geographical distribution of the user. For example, the recognition unit obtains and analyzes the user's geographical distribution from GPS data or location information services. For example, if the user is at home, recognition at home can be prioritized. Also, if the user is at work, recognition at work can be prioritized. Furthermore, if the user is traveling, recognition at the travel destination can be prioritized. In this way, by taking geographical distribution into consideration, the accuracy of recognition is improved. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's geographical distribution data into the generation AI and have the generation AI perform the recognition processing.

[0042] The recognition unit can improve the accuracy of recognition by referring to the user's related literature during recognition. The recognition unit, for example, retrieves and analyzes the user's related literature from a database. For example, the recognition unit can improve the accuracy of recognition by referring to literature the user has read in the past. The recognition unit can also improve the accuracy of recognition by referring to literature the user is currently reading. Furthermore, the recognition unit can also improve the accuracy of recognition by referring to literature the user plans to read in the future. In this way, the accuracy of recognition is improved by referring to the related literature. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of recognition.

[0043] When unlocking, the unlocking unit can select the optimal unlocking method by referring to the user's past usage history. The unlocking unit, for example, retrieves and analyzes the user's past usage history from a database. For example, a detailed guide can be provided to a user who has failed to unlock in the past. A simplified procedure can also be provided to a user who has successfully unlocked in the past. Furthermore, it is possible to unlock at a specific time period based on the past usage history. In this way, the optimal unlocking method can be provided by referring to the past usage history. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's past usage history data into a generation AI and have the generation AI select the optimal unlocking method.

[0044] When unlocking, the unlocking unit can customize the unlocking means based on the user's current activity status. The unlocking unit, for example, monitors and analyzes the user's current activity status in real time. For example, if the user is at work, unlocking can be postponed. Also, if the user is engaged in a hobby activity, unlocking can be prioritized. Furthermore, if the user is traveling, unlocking can be simplified. This improves the accuracy of unlocking by customizing the unlocking means according to the user's activity status. Some or all of the above-mentioned processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's activity status data into a generation AI and have the generation AI customize the unlocking means.

[0045] When unlocking, the unlocking unit can select the optimal unlocking method by taking into account the user's geographical location information. The unlocking unit, for example, acquires and analyzes the user's geographical location information from GPS data or a location information service. For example, if the user is at home, unlocking at home can be prioritized. Also, if the user is at work, unlocking at work can be prioritized. Furthermore, if the user is traveling, unlocking at the travel destination can be prioritized. In this way, by taking the geographical location information into consideration, the optimal unlocking method can be provided. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal unlocking method.

[0046] When unlocking, the unlocking unit can analyze the user's social media activity and suggest an unlocking method. The unlocking unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the unlocking unit can suggest unlocking at times when the user frequently posts on social media. The unlocking unit can also suggest unlocking at locations tagged by the user on social media. Furthermore, the unlocking unit can suggest unlocking at locations shared by the user on social media. In this way, by analyzing social media activity, the optimal unlocking method can be suggested. Some or all of the above-described processing in the unlocking unit may be performed using, or without, AI. For example, the unlocking unit can input the user's social media data into a generation AI and have the generation AI suggest unlocking methods.

[0047] During tracking, the tracking unit can optimize the tracking algorithm by referring to the past usage history of the rented equipment. The tracking unit, for example, retrieves and analyzes the past usage history of the rented equipment from a database. For example, the tracking algorithm is adjusted for equipment that has failed to be tracked in the past. The same algorithm can also be used for equipment that has been tracked smoothly in the past. Furthermore, the tracking algorithm can be optimized for a specific time period based on the past usage history. In this way, the tracking algorithm can be optimized by referring to the past usage history. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the past usage history data of the rented equipment into the generation AI and cause the generation AI to optimize the tracking algorithm.

[0048] The tracking unit can improve the tracking accuracy by taking into account the attribute information of the rented equipment during tracking. The tracking unit, for example, obtains and analyzes the attribute information of the rented equipment from a database. For example, the tracking accuracy can be adjusted by taking into account the type of equipment. The tracking accuracy can also be adjusted by taking into account the frequency of use of the equipment. Furthermore, the tracking accuracy can be adjusted by taking into account the value of the equipment. In this way, by taking into account the attribute information of the equipment, the tracking accuracy is improved. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the attribute information of the rented equipment into the generation AI and cause the generation AI to adjust the tracking accuracy.

[0049] The tracking unit can take into account the geographical distribution of the rented equipment when tracking. For example, the tracking unit obtains and analyzes the geographical distribution of the rented equipment from GPS data or location information services. For example, if the equipment is at home, tracking at home can be prioritized. Also, if the equipment is at work, tracking at work can be prioritized. Furthermore, if the equipment is at a travel destination, tracking at the travel destination can be prioritized. In this way, by taking the geographical distribution into consideration, the accuracy of tracking is improved. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input geographical distribution data of the rented equipment to the generation AI and cause the generation AI to perform the tracking processing.

[0050] The tracking unit can improve the accuracy of tracking by referring to literature related to the borrowed equipment during tracking. The tracking unit, for example, retrieves and analyzes literature related to the borrowed equipment from a database. For example, the tracking accuracy can be improved by referring to literature on how to use the equipment. The tracking accuracy can also be improved by referring to literature on how to store the equipment. Furthermore, the tracking accuracy can be improved by referring to literature on equipment maintenance. In this way, the tracking accuracy is improved by referring to related literature. Some or all of the above-described processing in the tracking unit may be performed using, or without, AI, for example. For example, the tracking unit can input literature data related to the borrowed equipment into the generation AI and cause the generation AI to improve the tracking accuracy.

[0051] When recording, the recording unit can optimize the recording algorithm by referring to the past usage history of the returned equipment. The recording unit, for example, retrieves and analyzes the past usage history of the returned equipment from a database. For example, the recording algorithm can be adjusted for equipment that has failed to be recorded in the past. The same algorithm can also be used for equipment that has been recorded smoothly in the past. Furthermore, the recording algorithm can be optimized for a specific time period based on the past usage history. In this way, the recording algorithm can be optimized by referring to the past usage history. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past usage history data of the returned equipment to the generation AI and have the generation AI optimize the recording algorithm.

[0052] The recording unit can improve the accuracy of the recording by taking into account the attribute information of the returned equipment when recording. The recording unit, for example, obtains and analyzes the attribute information of the returned equipment from a database. For example, the recording unit adjusts the accuracy of the recording by taking into account the type of equipment. The recording accuracy can also be adjusted by taking into account the frequency of use of the equipment. Furthermore, the recording accuracy can also be adjusted by taking into account the value of the equipment. In this way, by taking into account the attribute information of the equipment, the accuracy of the recording is improved. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the attribute information of the returned equipment to the generation AI and cause the generation AI to adjust the accuracy of the recording.

[0053] The recording unit can take into account the geographical distribution of the returned equipment when recording. The recording unit, for example, obtains and analyzes the geographical distribution of the returned equipment from GPS data or location information services. For example, if the equipment is returned to home, priority can be given to recording at home. Also, if the equipment is returned to work, priority can be given to recording at work. Furthermore, if the equipment is returned to a travel destination, priority can be given to recording at the travel destination. In this way, by taking the geographical distribution into consideration, the accuracy of recording is improved. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input geographical distribution data of the returned equipment to a generation AI and have the generation AI perform the recording processing.

[0054] When recording, the recording unit can improve the accuracy of the recording by referring to literature related to the returned equipment. The recording unit, for example, retrieves and analyzes literature related to the returned equipment from a database. For example, the recording accuracy can be improved by referring to literature on how to use the equipment. The recording accuracy can also be improved by referring to literature on how to store the equipment. Furthermore, the recording accuracy can be improved by referring to literature on equipment maintenance. In this way, the accuracy of the recording is improved by referring to related literature. Some or all of the above-mentioned processing in the recording unit may be performed using, or without using, AI, for example. For example, the recording unit can input literature data related to the returned equipment into a generation AI and cause the generation AI to improve the accuracy of the recording.

[0055] When providing guidance, the guide unit can select the optimal guidance method by referring to the user's past usage history. The guide unit, for example, retrieves and analyzes the user's past usage history from a database. For example, the guide unit can provide the same guidance method to a user who has successfully followed a guide in the past. Also, a different guidance method can be provided to a user who has unsuccessfully followed a guide in the past. Furthermore, the optimal guidance method for a specific time period can be selected from the past usage history. In this way, the optimal guidance method can be provided by referring to the past usage history. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's past usage history data into a generation AI and have the generation AI select the optimal guidance method.

[0056] The guide unit can improve the accuracy of the guide during guidance by taking into account the user's attribute information. The guide unit, for example, acquires and analyzes the user's attribute information from a database. For example, the guide unit adjusts the accuracy of the guide by taking into account the user's age. The guide unit can also adjust the accuracy of the guide by taking into account the user's gender. Furthermore, the guide unit can also adjust the accuracy of the guide by taking into account the user's occupation. In this way, the accuracy of the guide is improved by taking into account the user's attribute information. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's attribute information into a generation AI and have the generation AI adjust the accuracy of the guide.

[0057] The guide unit can select the optimal guide method during guidance by taking into account the user's geographical location information. The guide unit, for example, acquires and analyzes the user's geographical location information from GPS data or location information services. For example, if the user is at home, guidance at home can be prioritized. Also, if the user is at work, guidance at work can be prioritized. Furthermore, if the user is traveling, guidance at the travel destination can be prioritized. In this way, by taking the geographical location information into consideration, the optimal guide method can be provided. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's geographical location information to a generation AI and have the generation AI select the optimal guide method.

[0058] During guidance, the guide unit can analyze the user's social media activity and suggest guidance methods. The guide unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the guide unit can suggest guidance for times when the user frequently posts on social media. The guide unit can also suggest guidance for places tagged by the user on social media. Furthermore, the guide unit can also suggest guidance for places shared by the user on social media. In this way, by analyzing social media activity, the optimal guidance method can be suggested. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's social media data into a generation AI and have the generation AI suggest guidance methods.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The registration unit can monitor the user's health condition and adjust the timing of facial image registration based on the health condition. For example, if the user is tired, the registration of a facial image can be postponed. Also, if the user is in good health, the registration of a facial image can be performed immediately. Furthermore, if the user is ill, the registration of a facial image can be postponed. This improves the accuracy of registration by adjusting the timing of facial image registration according to the user's health condition. The health condition is monitored, for example, using a wearable device or a health app. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's health data into the generation AI and cause the generation AI to monitor the health condition.

[0061] The recognition unit can recognize a user's voiceprint and recognize the user based on the voiceprint recognition. For example, when the user speaks to the system, the system can recognize the user using voiceprint recognition technology. Recognition can also be performed by the user pronouncing a specific phrase. Furthermore, the accuracy of recognition can be improved by analyzing the tone and rhythm of the user's voice. Thus, voiceprint recognition improves the accuracy of user recognition. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's voiceprint data into a generation AI and have the generation AI perform voiceprint recognition.

[0062] The unlocking unit can learn the user's behavioral patterns and adjust the timing of unlocking based on the behavioral patterns. For example, if a user borrows equipment at a specific time every morning, the unlocking unit can automatically unlock the equipment at that time. Also, if a user returns equipment on a specific day of the week, the unlocking unit can prioritize unlocking on that day. Furthermore, if the user's behavioral patterns change, the unlocking unit can adjust the timing of unlocking based on the new pattern. This improves the accuracy of unlocking by adjusting the timing of unlocking according to the user's behavioral patterns. The behavioral patterns are learned using, for example, AI. Some or all of the above-mentioned processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's behavioral data into the generation AI and cause the generation AI to learn the behavioral patterns.

[0063] The tracking unit can monitor the usage status of the rented equipment in real time and adjust the tracking accuracy based on the usage status. For example, if the equipment is used frequently, the tracking accuracy can be increased. Also, if the equipment has not been used for a long period of time, the tracking accuracy can be decreased. Furthermore, if the equipment is being used abnormally, the tracking accuracy can be adjusted. In this way, by adjusting the tracking accuracy according to the usage status of the rented equipment, the tracking accuracy can be improved. The usage status is monitored using, for example, sensor technology or IoT devices. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input equipment usage data to the generation AI and cause the generation AI to monitor the usage status.

[0064] The recording unit can inspect the condition of the returned equipment and adjust the accuracy of the recording based on the condition. For example, if the equipment is returned in a normal condition, the accuracy of the recording can be increased. If the equipment is damaged, the accuracy of the recording can be decreased. Furthermore, if the equipment is dirty, the accuracy of the recording can be adjusted. In this way, the accuracy of the recording can be improved by adjusting the accuracy of the recording according to the condition of the returned equipment. The inspection of the equipment condition is performed using, for example, image recognition technology or sensor technology. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input equipment condition data into a generation AI and have the generation AI perform a condition inspection.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The registration unit registers a facial image of the user. For example, the registration unit takes a facial image of the user with a camera and stores it in the system. Step 2: The recognition unit recognizes the user based on the facial image registered by the registration unit. For example, the recognition unit recognizes the user using facial recognition technology. Facial recognition technology includes deep learning-based facial recognition and 2D / 3D facial recognition. Step 3: The unlocking unit unlocks the door of the equipment storage unit for the user recognized by the recognition unit. For example, the unlocking unit unlocks the door using an electronic lock. Step 4: The tracking unit tracks the rented equipment using object recognition technology. For example, the tracking unit tracks the equipment using image recognition algorithms and sensor technology. Step 5: The recording unit records the returned equipment. For example, the recording unit stores information about the returned equipment in a database. Step 6: The guide section uses a virtual assistant to guide the user through the entire equipment rental and return process. For example, the guide section uses voice recognition and natural language processing to provide guidance to the user.

[0067] (Example 2) An equipment lending and return system according to an embodiment of the present invention utilizes multimodal generative AI technology to provide a fully automated solution for lending and returning equipment. The system registers users and recognizes them using facial recognition technology. Then, for the recognized users, it unlocks the equipment storage door. At the same time, it uses object recognition technology to accurately track what has been borrowed and record returns. A virtual assistant guides users through the entire equipment lending and return process and provides support. For example, when registering a user, the system registers the user's facial image in the system and recognizes the user using facial recognition technology. When the user borrows equipment, the system performs facial recognition to recognize the user and confirm that the user has legitimate rights. Then, for the recognized users, it unlocks the equipment storage door. For example, after the user passes facial recognition, the system automatically unlocks the equipment storage door, allowing the user to retrieve the equipment. At the same time, it uses object recognition technology to accurately track what has been borrowed. For example, when a user takes out equipment, the system performs object recognition and records which equipment has been borrowed. This makes equipment management easier. The same is true when equipment is returned. For example, when a user returns equipment, the system performs object recognition and records which equipment has been returned. This allows for accurate tracking of the equipment's return status. In addition, a virtual assistant guides the user through the entire equipment lending and returning process, providing support to the user. For example, when a user borrows equipment, the virtual assistant explains the procedure to help the user borrow the equipment smoothly. Similarly, when returning equipment, the virtual assistant explains the procedure to help the user return the equipment smoothly. This fully automates equipment lending and returning, providing a convenient and efficient system for users. The equipment lending and returning system can fully automate equipment lending and returning by registering the user's facial image and performing recognition, unlocking, tracking, recording, and guidance.

[0068] An equipment lending and returning system according to an embodiment includes a registration unit, a recognition unit, an unlocking unit, a tracking unit, a recording unit, and a guide unit. The registration unit registers a facial image of a user. For example, the registration unit may take a facial image of the user with a camera and store it in the system. The recognition unit recognizes the user based on the facial image registered by the registration unit. For example, the recognition unit may recognize the user using facial recognition technology. Facial recognition technology includes deep learning-based facial recognition and 2D / 3D facial recognition. The unlocking unit unlocks the door of the equipment storage unit for the user recognized by the recognition unit. For example, the unlocking unit unlocks the door using an electronic lock. The tracking unit tracks borrowed equipment using object recognition technology. For example, the tracking unit tracks equipment using an image recognition algorithm or sensor technology. The recording unit records returned equipment. For example, the recording unit stores information about returned equipment in a database. The guide unit uses a virtual assistant to guide the user through the entire equipment lending and returning process. For example, the guide unit provides guidance to the user using voice recognition and natural language processing. As a result, the equipment lending and returning system according to the embodiment can fully automate the lending and returning of equipment by registering the user's facial image and performing recognition, unlocking, tracking, recording, and guidance.

[0069] The recognition unit can recognize users using facial recognition technology. Facial recognition technology includes deep learning-based facial recognition and 2D / 3D facial recognition. For example, deep learning-based facial recognition can learn large amounts of facial image data and perform highly accurate authentication. 2D facial recognition is a technology that extracts facial features based on a flat image and performs authentication, while 3D facial recognition is a technology that performs authentication based on the three-dimensional shape of the face. As a result, the use of facial recognition technology improves the accuracy of user recognition. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input facial image data to a generation AI and have the generation AI perform facial recognition.

[0070] The unlocking unit can unlock the door of the equipment storage cabinet for the recognized user. The unlocking unit unlocks the door using, for example, an electronic lock. Electronic locks include magnetic locks, electric locks, smart locks, etc. For example, a magnetic lock is a lock that secures the door using magnetic force, and an electric lock is a device that controls the lock using an electric signal. A smart lock is a device that unlocks the lock using a smartphone, card key, etc. This improves convenience by automatically unlocking the lock for the recognized user. Some or all of the above-mentioned processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input information about the user recognized by the recognition unit into the generation AI and have the generation AI execute control of unlocking the lock.

[0071] The tracking unit can track the rented equipment using object recognition technology. Object recognition technology includes image recognition algorithms and sensor technology. For example, image recognition algorithms are a technology that recognizes and tracks objects from images captured by a camera. Sensor technology includes RFID tags and GPS. For example, RFID tags are a technology that identifies the location of an object using wireless communication, and GPS is a technology that identifies the location of an object using satellites. As a result, the use of object recognition technology improves the tracking accuracy of the rented equipment. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input object recognition data to a generation AI and have the generation AI track the object.

[0072] The recording unit can record returned equipment. The recording unit saves information about the returned equipment in a database, for example. The database includes the equipment ID, the return date and time, and information about the returner. For example, an SQL database or a NoSQL database is used as the database. An SQL database is a database for efficiently managing structured data, while a NoSQL database is a database for efficiently managing unstructured data and large-scale data. This facilitates equipment management by accurately recording returned equipment. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input information about the returned equipment into a generation AI and have the generation AI perform the recording process.

[0073] The guide unit can use a virtual assistant to guide users through the entire equipment rental and return process. Virtual assistants use technologies such as voice recognition and natural language processing. For example, voice recognition technology converts a user's voice into text and formats it understandable to the system. Natural language processing technology analyzes text data and understands its meaning. This allows users to smoothly rent and return equipment using a virtual assistant. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without AI. For example, the guide unit can input the user's voice data into a generation AI and have the generation AI execute the content of the guide.

[0074] The registration unit can estimate the user's emotion and adjust the timing of facial image registration based on the estimated user emotion. For example, the registration unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the facial image can be registered immediately. If the user is nervous, the facial image can be registered after allowing the user time to relax. Furthermore, if the user is in a hurry, the facial image can be registered quickly. This improves the accuracy of registration by adjusting the timing of facial image registration according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the registration unit can be performed using AI, for example, or without AI. For example, the registration unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] The registration unit can analyze the user's past usage history and select the optimal facial image registration method. The registration unit, for example, retrieves and analyzes the user's past usage history from a database. For example, a detailed guide can be provided to a user who has failed to register a facial image in the past. A simplified procedure can also be provided to a user who has successfully registered a facial image in the past. Furthermore, it is possible to register a facial image at a specific time period based on the past usage history. In this way, by analyzing the past usage history, the optimal facial image registration method can be provided. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's past usage history data into a generation AI and have the generation AI select the optimal registration method.

[0076] The registration unit can perform filtering based on the user's current activity status and areas of interest when registering a facial image. The registration unit, for example, monitors the user's current activity status in real time and analyzes the user's areas of interest. For example, if the user is at work, the registration of facial images can be postponed. Also, if the user is engaged in a hobby activity, the registration of facial images can be prioritized. Furthermore, if the user is traveling, the registration of facial images can be simplified. This improves the accuracy of registration by registering facial images based on the user's activity status and areas of interest. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or can be performed without using AI. For example, the registration unit can input the user's activity status data to the generation AI and cause the generation AI to perform the filtering process.

[0077] The registration unit can estimate the user's emotions and determine the priority of facial images to be registered based on the estimated user emotions. For example, the registration unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the registration of facial images can be prioritized. Also, if the user is nervous, other registration tasks can be prioritized. Furthermore, if the user is in a hurry, the registration of facial images can be postponed. This improves registration efficiency by determining the priority of facial images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the registration unit can be performed using AI, for example, or without AI. For example, the registration unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0078] When registering a facial image, the registration unit can prioritize registering highly relevant facial images by taking into account the user's geographical location information. The registration unit, for example, acquires and analyzes the user's geographical location information from GPS data or a location information service. For example, when the user is at home, facial images at home can be prioritized for registration. Also, when the user is at work, facial images at work can be prioritized for registration. Furthermore, when the user is traveling, facial images at the travel destination can also be prioritized for registration. In this way, by taking the geographical location information into account, highly relevant facial images can be prioritized for registration. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's geographical location information to the generation AI and cause the generation AI to determine the priority order of facial images.

[0079] When registering a facial image, the registration unit can analyze the user's social media activity and register related facial images. The registration unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the registration unit can register facial images by referring to images frequently posted by the user on social media. The registration unit can also register facial images by referring to images tagged by the user on social media. Furthermore, the registration unit can also register facial images by referring to images shared by the user on social media. In this way, related facial images can be registered by analyzing social media activity. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's social media data into the generation AI and cause the generation AI to register facial images.

[0080] The recognition unit can estimate the user's emotions and adjust the accuracy of recognition based on the estimated user emotions. For example, the recognition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the recognition accuracy can be increased. Also, if the user is nervous, the recognition accuracy can be decreased. Furthermore, if the user is in a hurry, the recognition accuracy can be adjusted. This improves the recognition accuracy by adjusting the recognition accuracy according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recognition unit can be performed using, for example, AI, or without AI. For example, the recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0081] During recognition, the recognition unit can optimize the recognition algorithm by referring to the user's past recognition history. The recognition unit, for example, retrieves and analyzes the user's past recognition history from a database. For example, the recognition algorithm can be adjusted for a user who has failed recognition in the past. The same algorithm can also be used for a user who has performed recognition smoothly in the past. Furthermore, the recognition algorithm can be optimized for a specific time period based on the past recognition history. In this way, the recognition algorithm can be optimized by referring to the past recognition history. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's past recognition history data into the generation AI and cause the generation AI to optimize the recognition algorithm.

[0082] The recognition unit can improve the accuracy of recognition by taking into account the user's attribute information during recognition. The recognition unit, for example, acquires and analyzes the user's attribute information from a database. For example, the recognition accuracy can be adjusted by taking into account the user's age. The recognition accuracy can also be adjusted by taking into account the user's gender. Furthermore, the recognition accuracy can be adjusted by taking into account the user's occupation. In this way, the recognition accuracy is improved by taking into account the user's attribute information. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's attribute information into the generation AI and cause the generation AI to adjust the recognition accuracy.

[0083] The recognition unit can estimate the user's emotions and adjust the order in which the recognition results are displayed based on the estimated user emotions. For example, the recognition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, detailed recognition results can be displayed preferentially. If the user is nervous, concise recognition results can be displayed preferentially. Furthermore, if the user is in a hurry, recognition results that focus on the main points can be displayed preferentially. This improves the understanding of the recognition results by adjusting the order in which the recognition results are displayed according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit can be performed using, for example, an AI. For example, the recognition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The recognition unit can perform recognition taking into account the geographical distribution of the user. For example, the recognition unit obtains and analyzes the user's geographical distribution from GPS data or location information services. For example, if the user is at home, recognition at home can be prioritized. Also, if the user is at work, recognition at work can be prioritized. Furthermore, if the user is traveling, recognition at the travel destination can be prioritized. In this way, by taking geographical distribution into consideration, the accuracy of recognition is improved. Some or all of the above-mentioned processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's geographical distribution data into the generation AI and have the generation AI perform the recognition processing.

[0085] The recognition unit can improve the accuracy of recognition by referring to the user's related literature during recognition. The recognition unit, for example, retrieves and analyzes the user's related literature from a database. For example, the recognition unit can improve the accuracy of recognition by referring to literature the user has read in the past. The recognition unit can also improve the accuracy of recognition by referring to literature the user is currently reading. Furthermore, the recognition unit can also improve the accuracy of recognition by referring to literature the user plans to read in the future. In this way, the accuracy of recognition is improved by referring to the related literature. Some or all of the above-mentioned processing in the recognition unit may be performed, for example, using AI, or may be performed without using AI. For example, the recognition unit can input the user's related literature data into the generation AI and cause the generation AI to improve the accuracy of recognition.

[0086] The unlocking unit can estimate the user's emotions and adjust the timing of unlocking based on the estimated user emotions. For example, the unlocking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the unlocking unit can immediately unlock the device. If the user is nervous, the unlocking unit can give the user time to relax before unlocking the device. Furthermore, if the user is in a hurry, the unlocking unit can quickly unlock the device. This improves the accuracy of unlocking by adjusting the timing of unlocking according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the unlocking unit can be performed using AI, for example, or without AI. For example, the unlocking unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0087] When unlocking, the unlocking unit can select the optimal unlocking method by referring to the user's past usage history. The unlocking unit, for example, retrieves and analyzes the user's past usage history from a database. For example, a detailed guide can be provided to a user who has failed to unlock in the past. A simplified procedure can also be provided to a user who has successfully unlocked in the past. Furthermore, it is possible to unlock at a specific time period based on the past usage history. In this way, the optimal unlocking method can be provided by referring to the past usage history. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's past usage history data into a generation AI and have the generation AI select the optimal unlocking method.

[0088] When unlocking, the unlocking unit can customize the unlocking means based on the user's current activity status. The unlocking unit, for example, monitors and analyzes the user's current activity status in real time. For example, if the user is at work, unlocking can be postponed. Also, if the user is engaged in a hobby activity, unlocking can be prioritized. Furthermore, if the user is traveling, unlocking can be simplified. This improves the accuracy of unlocking by customizing the unlocking means according to the user's activity status. Some or all of the above-mentioned processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's activity status data into a generation AI and have the generation AI customize the unlocking means.

[0089] The unlocking unit can estimate the user's emotions and determine the priority of unlocking based on the estimated user emotions. For example, the unlocking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, unlocking can be prioritized. Also, if the user is nervous, other unlocking tasks can be prioritized. Furthermore, if the user is in a hurry, unlocking can be postponed. This improves unlocking efficiency by determining the priority of unlocking according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the unlocking unit can be performed using AI, for example, or without AI. For example, the unlocking unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0090] When unlocking, the unlocking unit can select the optimal unlocking method by taking into account the user's geographical location information. The unlocking unit, for example, acquires and analyzes the user's geographical location information from GPS data or a location information service. For example, if the user is at home, unlocking at home can be prioritized. Also, if the user is at work, unlocking at work can be prioritized. Furthermore, if the user is traveling, unlocking at the travel destination can be prioritized. In this way, by taking the geographical location information into consideration, the optimal unlocking method can be provided. Some or all of the above-described processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal unlocking method.

[0091] When unlocking, the unlocking unit can analyze the user's social media activity and suggest an unlocking method. The unlocking unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the unlocking unit can suggest unlocking at times when the user frequently posts on social media. The unlocking unit can also suggest unlocking at locations tagged by the user on social media. Furthermore, the unlocking unit can suggest unlocking at locations shared by the user on social media. In this way, by analyzing social media activity, the optimal unlocking method can be suggested. Some or all of the above-described processing in the unlocking unit may be performed using, or without, AI. For example, the unlocking unit can input the user's social media data into a generation AI and have the generation AI suggest unlocking methods.

[0092] The tracking unit can estimate the user's emotion and adjust the tracking accuracy based on the estimated user emotion. For example, the tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the tracking accuracy can be increased. Also, if the user is nervous, the tracking accuracy can be decreased. Furthermore, if the user is in a hurry, the tracking accuracy can be adjusted. In this way, by adjusting the tracking accuracy according to the user's emotion, the tracking accuracy can be improved. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the tracking unit can be performed using an AI, for example, or without an AI. For example, the tracking unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] During tracking, the tracking unit can optimize the tracking algorithm by referring to the past usage history of the rented equipment. The tracking unit, for example, retrieves and analyzes the past usage history of the rented equipment from a database. For example, the tracking algorithm is adjusted for equipment that has failed to be tracked in the past. The same algorithm can also be used for equipment that has been tracked smoothly in the past. Furthermore, the tracking algorithm can be optimized for a specific time period based on the past usage history. In this way, the tracking algorithm can be optimized by referring to the past usage history. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the past usage history data of the rented equipment into the generation AI and cause the generation AI to optimize the tracking algorithm.

[0094] The tracking unit can improve the tracking accuracy by taking into account the attribute information of the rented equipment during tracking. The tracking unit, for example, obtains and analyzes the attribute information of the rented equipment from a database. For example, the tracking accuracy can be adjusted by taking into account the type of equipment. The tracking accuracy can also be adjusted by taking into account the frequency of use of the equipment. Furthermore, the tracking accuracy can be adjusted by taking into account the value of the equipment. In this way, by taking into account the attribute information of the equipment, the tracking accuracy is improved. Some or all of the above-described processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the attribute information of the rented equipment into the generation AI and cause the generation AI to adjust the tracking accuracy.

[0095] The tracking unit can estimate the user's emotion and adjust the order in which the tracking results are displayed based on the estimated user emotion. For example, the tracking unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, detailed tracking results can be displayed preferentially. Also, if the user is nervous, concise tracking results can be displayed preferentially. Furthermore, if the user is in a hurry, tracking results that focus on the main points can be displayed preferentially. This improves the understanding of the tracking results by adjusting the order in which the tracking results are displayed according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the tracking unit can be performed using an AI, for example, or without an AI. For example, the tracking unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The tracking unit can take into account the geographical distribution of the rented equipment when tracking. For example, the tracking unit obtains and analyzes the geographical distribution of the rented equipment from GPS data or location information services. For example, if the equipment is at home, tracking at home can be prioritized. Also, if the equipment is at work, tracking at work can be prioritized. Furthermore, if the equipment is at a travel destination, tracking at the travel destination can be prioritized. In this way, by taking the geographical distribution into consideration, the accuracy of tracking is improved. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input geographical distribution data of the rented equipment to the generation AI and cause the generation AI to perform the tracking processing.

[0097] The tracking unit can improve the accuracy of tracking by referring to literature related to the borrowed equipment during tracking. The tracking unit, for example, retrieves and analyzes literature related to the borrowed equipment from a database. For example, the tracking accuracy can be improved by referring to literature on how to use the equipment. The tracking accuracy can also be improved by referring to literature on how to store the equipment. Furthermore, the tracking accuracy can be improved by referring to literature on equipment maintenance. In this way, the tracking accuracy is improved by referring to related literature. Some or all of the above-described processing in the tracking unit may be performed using, or without, AI, for example. For example, the tracking unit can input literature data related to the borrowed equipment into the generation AI and cause the generation AI to improve the tracking accuracy.

[0098] The recording unit can estimate the user's emotions and adjust the accuracy of the recording based on the estimated user emotions. For example, the recording unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the recording accuracy can be increased. Also, if the user is nervous, the recording accuracy can be decreased. Furthermore, if the user is in a hurry, the recording accuracy can be adjusted. This improves the accuracy of the recording by adjusting the recording accuracy according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit can be performed using AI, for example, or without AI. For example, the recording unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0099] When recording, the recording unit can optimize the recording algorithm by referring to the past usage history of the returned equipment. The recording unit, for example, retrieves and analyzes the past usage history of the returned equipment from a database. For example, the recording algorithm can be adjusted for equipment that has failed to be recorded in the past. The same algorithm can also be used for equipment that has been recorded smoothly in the past. Furthermore, the recording algorithm can be optimized for a specific time period based on the past usage history. In this way, the recording algorithm can be optimized by referring to the past usage history. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the past usage history data of the returned equipment to the generation AI and have the generation AI optimize the recording algorithm.

[0100] The recording unit can improve the accuracy of the recording by taking into account the attribute information of the returned equipment when recording. The recording unit, for example, obtains and analyzes the attribute information of the returned equipment from a database. For example, the recording unit adjusts the accuracy of the recording by taking into account the type of equipment. The recording accuracy can also be adjusted by taking into account the frequency of use of the equipment. Furthermore, the recording accuracy can also be adjusted by taking into account the value of the equipment. In this way, by taking into account the attribute information of the equipment, the accuracy of the recording is improved. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the attribute information of the returned equipment to the generation AI and cause the generation AI to adjust the accuracy of the recording.

[0101] The recording unit can estimate the user's emotions and adjust the order in which the recording results are displayed based on the estimated user emotions. For example, the recording unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is relaxed, detailed recording results can be displayed preferentially. Also, if the user is nervous, concise recording results can be displayed preferentially. Furthermore, if the user is in a hurry, recording results that highlight the main points can be displayed preferentially. This allows the recording results to be adjusted in order to display the recording results according to the user's emotions, improving comprehension. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit can be performed using, for example, an AI, or without an AI. For example, the recording unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotions.

[0102] The recording unit can take into account the geographical distribution of the returned equipment when recording. The recording unit, for example, obtains and analyzes the geographical distribution of the returned equipment from GPS data or location information services. For example, if the equipment is returned to home, priority can be given to recording at home. Also, if the equipment is returned to work, priority can be given to recording at work. Furthermore, if the equipment is returned to a travel destination, priority can be given to recording at the travel destination. In this way, by taking the geographical distribution into consideration, the accuracy of recording is improved. Some or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input geographical distribution data of the returned equipment to a generation AI and have the generation AI perform the recording processing.

[0103] When recording, the recording unit can improve the accuracy of the recording by referring to literature related to the returned equipment. The recording unit, for example, retrieves and analyzes literature related to the returned equipment from a database. For example, the recording accuracy can be improved by referring to literature on how to use the equipment. The recording accuracy can also be improved by referring to literature on how to store the equipment. Furthermore, the recording accuracy can be improved by referring to literature on equipment maintenance. In this way, the accuracy of the recording is improved by referring to related literature. Some or all of the above-mentioned processing in the recording unit may be performed using, or without using, AI, for example. For example, the recording unit can input literature data related to the returned equipment into a generation AI and cause the generation AI to improve the accuracy of the recording.

[0104] The guide unit can estimate the user's emotions and adjust the way the guidance is presented based on the estimated user emotions. For example, the guide unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, if the user is relaxed, the guide unit can provide guidance at a leisurely pace. If the user is nervous, the guide unit can provide concise and clear guidance. If the user is in a hurry, the guide unit can provide quick and to-the-point guidance. This improves the accuracy of the guidance by adjusting the way the guidance is presented based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the guide unit can be performed using AI, for example, or without AI. For example, the guide unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0105] When providing guidance, the guide unit can select the optimal guidance method by referring to the user's past usage history. The guide unit, for example, retrieves and analyzes the user's past usage history from a database. For example, the guide unit can provide the same guidance method to a user who has successfully followed a guide in the past. Also, a different guidance method can be provided to a user who has unsuccessfully followed a guide in the past. Furthermore, the optimal guidance method for a specific time period can be selected from the past usage history. In this way, the optimal guidance method can be provided by referring to the past usage history. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's past usage history data into a generation AI and have the generation AI select the optimal guidance method.

[0106] The guide unit can improve the accuracy of the guide during guidance by taking into account the user's attribute information. The guide unit, for example, acquires and analyzes the user's attribute information from a database. For example, the guide unit adjusts the accuracy of the guide by taking into account the user's age. The guide unit can also adjust the accuracy of the guide by taking into account the user's gender. Furthermore, the guide unit can also adjust the accuracy of the guide by taking into account the user's occupation. In this way, the accuracy of the guide is improved by taking into account the user's attribute information. Some or all of the above-described processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's attribute information into a generation AI and have the generation AI adjust the accuracy of the guide.

[0107] The guide unit can estimate the user's emotions and adjust the order of guidance based on the estimated user emotions. For example, the guide unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, if the user is relaxed, detailed guidance can be provided preferentially. Also, if the user is nervous, concise guidance can be provided preferentially. Furthermore, if the user is in a hurry, guidance that focuses on the main points can be provided preferentially. This improves the efficiency of guidance by adjusting the order of guidance according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the guide unit can be performed using AI, for example, or without AI. For example, the guide unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0108] The guide unit can select the optimal guide method during guidance by taking into account the user's geographical location information. The guide unit, for example, acquires and analyzes the user's geographical location information from GPS data or location information services. For example, if the user is at home, guidance at home can be prioritized. Also, if the user is at work, guidance at work can be prioritized. Furthermore, if the user is traveling, guidance at the travel destination can be prioritized. In this way, by taking the geographical location information into consideration, the optimal guide method can be provided. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's geographical location information to a generation AI and have the generation AI select the optimal guide method.

[0109] During guidance, the guide unit can analyze the user's social media activity and suggest guidance methods. The guide unit, for example, retrieves and analyzes the user's social media activity from a database. For example, the guide unit can suggest guidance for times when the user frequently posts on social media. The guide unit can also suggest guidance for places tagged by the user on social media. Furthermore, the guide unit can also suggest guidance for places shared by the user on social media. In this way, by analyzing social media activity, the optimal guidance method can be suggested. Some or all of the above-mentioned processing in the guide unit may be performed using, for example, AI, or may be performed without using AI. For example, the guide unit can input the user's social media data into a generation AI and have the generation AI suggest guidance methods. === Hard Collateral 1-1 === Each of the multiple elements including the registration unit, recognition unit, release unit, tracking unit, recording unit, and guide unit described above is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the registration unit takes a facial image of the user using the camera 42 of the smart device 14 and stores the image in the database 24 of the data processing device 12. The recognition unit recognizes the user using facial recognition technology via the specific processing unit 290 of the data processing device 12. The release unit unlocks the electronic lock via the control unit 46A of the smart device 14. The tracking unit tracks the equipment using object recognition technology via the camera 42 of the smart device 14 and processes the tracking results via the specific processing unit 290 of the data processing device 12. The recording unit saves information about the returned equipment in the database 24 of the data processing device 12. The guide unit provides guidance to the user using voice recognition and natural language processing via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the registration unit, recognition unit, release unit, tracking unit, recording unit, and guide unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit takes a facial image of the user using the camera 42 of the smart glasses 214 and stores it in the database 24 of the data processing device 12. The recognition unit recognizes the user using facial recognition technology via the specific processing unit 290 of the data processing device 12. The release unit unlocks the electronic lock via the control unit 46A of the smart glasses 214. The tracking unit tracks the equipment using object recognition technology via the camera 42 of the smart glasses 214 and processes the tracking results via the specific processing unit 290 of the data processing device 12. The recording unit saves information about the returned equipment in the database 24 of the data processing device 12. The guide unit provides guidance to the user using voice recognition and natural language processing via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, recognition unit, release unit, tracking unit, recording unit, and guide unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit takes a facial image of the user using the camera 42 of the headset type terminal 314 and stores the image in the database 24 of the data processing device 12. The recognition unit recognizes the user using facial recognition technology via the specific processing unit 290 of the data processing device 12. The release unit releases the electronic lock via the control unit 46A of the headset type terminal 314. The tracking unit tracks the fixture using object recognition technology via the camera 42 of the headset type terminal 314 and processes the tracking results via the specific processing unit 290 of the data processing device 12. The recording unit saves information about the returned fixture in the database 24 of the data processing device 12. The guide unit provides guidance to the user using voice recognition and natural language processing via the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, recognition unit, release unit, tracking unit, recording unit, and guide unit is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the registration unit takes a facial image of the user using the camera 42 of the robot 414 and stores the image in the database 24 of the data processing device 12. The recognition unit recognizes the user using facial recognition technology via the specific processing unit 290 of the data processing device 12. The release unit unlocks the electronic lock via the control unit 46A of the robot 414. The tracking unit tracks the equipment using object recognition technology via the camera 42 of the robot 414 and processes the tracking results via the specific processing unit 290 of the data processing device 12. The recording unit saves information about the returned equipment in the database 24 of the data processing device 12. The guide unit provides guidance to the user using voice recognition and natural language processing via the control unit 46A of the robot 414.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The registration unit can monitor the user's health condition and adjust the timing of facial image registration based on the health condition. For example, if the user is tired, the registration of a facial image can be postponed. Also, if the user is in good health, the registration of a facial image can be performed immediately. Furthermore, if the user is ill, the registration of a facial image can be postponed. This improves the accuracy of registration by adjusting the timing of facial image registration according to the user's health condition. The health condition is monitored, for example, using a wearable device or a health app. Some or all of the above-mentioned processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the user's health data into the generation AI and cause the generation AI to monitor the health condition.

[0112] The recognition unit can recognize a user's voiceprint and recognize the user based on the voiceprint recognition. For example, when the user speaks to the system, the system can recognize the user using voiceprint recognition technology. Recognition can also be performed by the user pronouncing a specific phrase. Furthermore, the accuracy of recognition can be improved by analyzing the tone and rhythm of the user's voice. Thus, voiceprint recognition improves the accuracy of user recognition. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the recognition unit can input the user's voiceprint data into a generation AI and have the generation AI perform voiceprint recognition.

[0113] The unlocking unit can learn the user's behavioral patterns and adjust the timing of unlocking based on the behavioral patterns. For example, if a user borrows equipment at a specific time every morning, the unlocking unit can automatically unlock the equipment at that time. Also, if a user returns equipment on a specific day of the week, the unlocking unit can prioritize unlocking on that day. Furthermore, if the user's behavioral patterns change, the unlocking unit can adjust the timing of unlocking based on the new pattern. This improves the accuracy of unlocking by adjusting the timing of unlocking according to the user's behavioral patterns. The behavioral patterns are learned using, for example, AI. Some or all of the above-mentioned processing in the unlocking unit may be performed using, for example, AI, or may be performed without using AI. For example, the unlocking unit can input the user's behavioral data into the generation AI and cause the generation AI to learn the behavioral patterns.

[0114] The tracking unit can monitor the usage status of the rented equipment in real time and adjust the tracking accuracy based on the usage status. For example, if the equipment is used frequently, the tracking accuracy can be increased. Also, if the equipment has not been used for a long period of time, the tracking accuracy can be decreased. Furthermore, if the equipment is being used abnormally, the tracking accuracy can be adjusted. In this way, by adjusting the tracking accuracy according to the usage status of the rented equipment, the tracking accuracy can be improved. The usage status is monitored using, for example, sensor technology or IoT devices. Some or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input equipment usage data to the generation AI and cause the generation AI to monitor the usage status.

[0115] The recording unit can inspect the condition of the returned equipment and adjust the accuracy of the recording based on the condition. For example, if the equipment is returned in a normal condition, the accuracy of the recording can be increased. If the equipment is damaged, the accuracy of the recording can be decreased. Furthermore, if the equipment is dirty, the accuracy of the recording can be adjusted. In this way, the accuracy of the recording can be improved by adjusting the accuracy of the recording according to the condition of the returned equipment. The inspection of the equipment condition is performed using, for example, image recognition technology or sensor technology. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input equipment condition data into a generation AI and have the generation AI perform a condition inspection.

[0116] The guide unit can estimate the user's emotions and customize the content of the guide based on the estimated user's emotions. For example, if the user is relaxed, detailed guidance can be provided. If the user is nervous, concise guidance can be provided. Furthermore, if the user is in a hurry, guidance that focuses on the main points can be provided. This customization of the content of the guide according to the user's emotions improves the accuracy of the guide. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the guide unit can be performed using AI, for example, or without AI. For example, the guide unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0117] The registration unit can estimate the user's emotion and adjust the timing of facial image registration based on the estimated user emotion. For example, if the user is relaxed, the facial image can be registered immediately. If the user is nervous, the facial image can be registered after allowing the user time to relax. Furthermore, if the user is in a hurry, the facial image can be registered quickly. This improves the accuracy of registration by adjusting the timing of facial image registration according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the registration unit can be performed using AI, for example, or without AI. For example, the registration unit can input the user's facial expression data into the generation AI and have the generation AI execute emotion estimation.

[0118] The recognition unit can estimate the user's emotions and adjust the accuracy of recognition based on the estimated user emotions. For example, if the user is relaxed, the accuracy of recognition can be increased. Also, if the user is nervous, the accuracy of recognition can be decreased. Furthermore, if the user is in a hurry, the accuracy of recognition can be adjusted. This improves the accuracy of recognition by adjusting the accuracy of recognition according to the user's emotions. The estimation of emotions is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recognition unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0119] The unlocking unit can estimate the user's emotions and adjust the timing of unlocking based on the estimated user emotions. For example, if the user is relaxed, the unlocking unit can immediately unlock the device. If the user is tense, the unlocking unit can unlock the device after giving the user time to relax. If the user is in a hurry, the unlocking unit can quickly unlock the device. This improves the accuracy of unlocking by adjusting the timing of unlocking according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the unlocking unit can be performed using an AI, or can be performed without an AI. For example, the unlocking unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0120] The tracking unit can estimate the user's emotions and adjust the tracking accuracy based on the estimated user's emotions. For example, if the user is relaxed, the tracking accuracy can be increased. Also, if the user is nervous, the tracking accuracy can be decreased. Furthermore, if the user is in a hurry, the tracking accuracy can be adjusted. Thus, by adjusting the tracking accuracy according to the user's emotions, the tracking accuracy can be improved. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the tracking unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the tracking unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The registration unit registers a facial image of the user. For example, the registration unit takes a facial image of the user with a camera and stores it in the system. Step 2: The recognition unit recognizes the user based on the facial image registered by the registration unit. For example, the recognition unit recognizes the user using facial recognition technology. Facial recognition technology includes deep learning-based facial recognition and 2D / 3D facial recognition. Step 3: The unlocking unit unlocks the door of the equipment storage unit for the user recognized by the recognition unit. For example, the unlocking unit unlocks the door using an electronic lock. Step 4: The tracking unit tracks the rented equipment using object recognition technology. For example, the tracking unit tracks the equipment using image recognition algorithms and sensor technology. Step 5: The recording unit records the returned equipment. For example, the recording unit stores information about the returned equipment in a database. Step 6: The guide section uses a virtual assistant to guide the user through the entire equipment rental and return process. For example, the guide section uses voice recognition and natural language processing to provide guidance to the user.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the 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.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0157] 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.

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

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a registration unit for registering a face image of a user; a recognition unit that recognizes a user based on the face image registered by the registration unit; an unlocking unit that unlocks the door of the equipment storage cabinet for the user recognized by the recognition unit; a tracking unit that tracks the rented equipment using specific object recognition techniques; a recording unit for recording returned equipment; A guide section in which a virtual assistant guides users through the entire equipment lending and returning process. A system characterized by:

2. The recognition unit Recognizing users using facial recognition technology 2. The system of claim 1.

3. The release unit is Unlocking the supply cabinet door for authorized users 2. The system of claim 1.

4. The tracking unit Tracking rented equipment using object recognition technology 2. The system of claim 1.

5. The recording unit Record returned equipment 2. The system of claim 1.

6. The guide portion is Guide your staff through the entire equipment checkout and return process with a virtual assistant 2. The system of claim 1.

7. The registration unit Using a specific method for estimating a user's emotion, the timing of registering a face image is adjusted based on the estimated user's emotion.

2. The system of claim 1.

8. The registration unit Analyze the user's past usage history and select the appropriate method for registering a facial image.

2. The system of claim 1.

Images