system

Abstract

The system according to this embodiment aims to enable elderly people to easily operate ATMs. [Solution] The system according to the embodiment comprises an acquisition unit, a discrimination unit, a display unit, and a voice guidance unit. The acquisition unit acquires the user's image. The discrimination unit analyzes the image acquired by the acquisition unit and determines the user's age. The display unit switches the ATM screen to a guide display when the discrimination unit determines that the user is above a certain age. The voice guidance unit provides voice guidance based on the guide display switched by the display unit.

Description

Technical Field

[0003]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] <00T0025>In the conventional technology, the elderly may have difficulty operating an ATM, and there is a need to improve IT literacy. [[ID=3S]]

[0005] The system according to the embodiment aims to enable the elderly to easily operate an ATM.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a discrimination unit, a display unit, and a voice guidance unit. The acquisition unit acquires the user's video. The discrimination unit analyzes the video acquired by the acquisition unit and determines the user's age. The display unit switches the ATM screen to a guide display when the discrimination unit determines that the user is above a certain age. The voice guidance unit provides voice guidance based on the guide display switched by the display unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable elderly people to easily operate an ATM. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An ATM system according to an embodiment of the present invention is an ATM system equipped with an image recognition AI that can determine age from video and a voice generation AI that can engage in interactive conversation. When this ATM system determines that the user is elderly, it switches the ATM screen to a guide display and starts voice guidance. This allows elderly people to easily use various services without having to operate the ATM. For example, this includes "charging electronic payment systems." This ATM system aims to improve the IT literacy of the elderly and make their daily lives more convenient. First, a camera installed in the ATM acquires video of the user. This video is analyzed by the image recognition AI to determine the user's age. For example, the age is estimated based on facial features and the number of wrinkles. Next, when the user is determined to be elderly, the ATM screen switches to a guide display. The guide display shows a friendly character or person to the user and guides them through the operation procedure one step at a time. Furthermore, the voice generation AI engages in interactive conversation with the user and provides voice guidance on the operation procedure. For example, it provides specific instructions by voice, such as "Next, please insert your card." This allows elderly people to not only follow the instructions on the screen but also to proceed with the operation while listening to the voice guidance. This system allows elderly people to easily access various services without having to operate an ATM. For example, when "charging an electronic payment system," the charge is completed simply by following the on-screen and voice guidance. This ATM system aims to improve the IT literacy of the elderly and make their daily lives more convenient. For instance, even elderly people who have difficulty using ATMs can easily operate it, thus promoting the use of electronic payment systems. In addition, the voice guidance clarifies the operating procedures, reducing the risk of errors and improving safety. In this way, the ATM system can improve the IT literacy of the elderly and make their daily lives more convenient.

[0029] The ATM system according to this embodiment includes an acquisition unit, a discrimination unit, a display unit, and a voice guidance unit. The acquisition unit acquires the user's image. The user's image includes, but is not limited to, still images, videos, and resolution. The acquisition unit acquires the user's image using, for example, a camera installed in the ATM. The acquisition unit can also acquire the user's image in real time. For example, the acquisition unit can acquire the user's facial features in detail using a high-resolution camera. The discrimination unit analyzes the image acquired by the acquisition unit and determines the user's age. Specific methods and criteria for determining age include, but are not limited to, facial recognition technology, the number of wrinkles, and skin condition. For example, the discrimination unit estimates the user's age using facial recognition technology. The discrimination unit can also determine age by analyzing the number of wrinkles and skin condition. Furthermore, the discrimination unit can also determine age using AI. For example, the discrimination unit can determine age using an AI model that takes facial features as input and outputs age. The display unit switches the ATM screen to a guide display when the discrimination unit determines that the user is above a certain age. The specific content and format of the guide display include, but are not limited to, characters, animations, and text displays. For example, the display unit may display a friendly character to the user. The display unit may also display a real person to the user. Furthermore, the display unit may use animation to provide a guide display. For example, the display unit may display an animation in which a friendly character guides the user through the operating procedure. The voice guidance unit provides voice guidance based on the guide display switched by the display unit. The specific content and method of the voice guidance include, but are not limited to, the type of voice, the content of the guidance, and the tone of voice. For example, the voice guidance unit may engage in interactive conversation with the user and provide voice guidance on the operating procedure. The voice guidance unit may also provide specific instructions by voice. Furthermore, the voice guidance unit may accept user feedback. For example, the voice guidance unit may provide detailed instructions by voice, such as, "Next, please insert your card."As a result, the ATM system according to this embodiment can simplify the use of the ATM by determining the user's age and providing appropriate guidance to elderly people. Some or all of the above-described processing in the voice guidance unit may be performed using AI, for example, or without using AI. For example, the voice guidance unit can provide voice guidance using an AI model that takes the user's voice data as input and outputs voice guidance.

[0030] The discrimination unit can estimate age based on facial features or the number of wrinkles. Facial features include, but are not limited to, the position of the eyes, the shape of the nose, and the size of the mouth. For example, the discrimination unit can estimate age based on the position of the eyes. It can also estimate age based on the shape of the nose. Furthermore, it can also estimate age based on the size of the mouth. For example, the discrimination unit can analyze the position of the eyes in detail and estimate age. The number of wrinkles includes, but are not limited to, the number of wrinkles in a specific part of the face and the depth of the wrinkles. For example, the discrimination unit can estimate age based on the number of wrinkles in a specific part of the face. It can also estimate age based on the depth of the wrinkles. Furthermore, the discrimination unit can estimate age by combining the number and depth of wrinkles. For example, the discrimination unit can analyze the number of wrinkles in a specific part of the face in detail and estimate age. This improves the accuracy of age determination by estimating age based on facial features and the number of wrinkles. Some or all of the processing described above in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can estimate age using an AI model that takes facial features and the number of wrinkles as input and outputs age.

[0031] The display unit can display characters or people to the user. Characters include, but are not limited to, anime characters or live-action characters. The display unit can, for example, display anime characters. The display unit can also display live-action characters. Furthermore, the display unit can display virtual characters. For example, the display unit can display an anime character guiding the user through the operation procedure. People include, but are not limited to, real people or virtual people. The display unit can, for example, display real people. The display unit can also display virtual people. Furthermore, the display unit can display people using animation. For example, the display unit can display a real person guiding the user through the operation procedure. This allows users to operate with confidence by displaying friendly characters or people. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can perform the display using an AI model that takes the display of characters or people as input and outputs the display content.

[0032] The voice guidance unit can engage in interactive conversation with the user and provide voice guidance on operating procedures. Interactive conversation includes, but is not limited to, speech recognition technology and conversation scenarios. For example, the voice guidance unit can engage in interactive conversation with the user using speech recognition technology. It can also engage in interactive conversation based on conversation scenarios. Furthermore, the voice guidance unit can engage in interactive conversation using AI. For example, the voice guidance unit can analyze the user's voice using speech recognition technology and generate an appropriate response. Guidance on operating procedures includes, but is not limited to, specific instructions and voice tone. For example, the voice guidance unit can provide specific instructions by voice. It can also provide guidance by adjusting the voice tone. Furthermore, the voice guidance unit can accept user feedback. For example, the voice guidance unit can provide specific instructions by voice, such as, "Next, please insert the card." This makes it easier for the user to understand the operating procedures through interactive conversation. Some or all of the above-described processes in the voice guidance unit may be performed using, for example, AI, or without AI. For example, the voice guidance unit can provide voice guidance using an AI model that takes user voice data as input and outputs voice guidance.

[0033] The voice guidance unit can provide detailed instructions by voice, such as "Next, please insert the card." These detailed instructions may include, but are not limited to, instructions on how to insert the card or how to press buttons. For example, the voice guidance unit can provide voice instructions on how to insert the card. It can also provide voice instructions on how to press buttons. Furthermore, the voice guidance unit can accept user feedback. For example, the voice guidance unit can provide specific instructions by voice, such as "Next, please insert the card." By providing specific instructions by voice, users can proceed with the operation without getting lost. Some or all of the above processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can provide voice guidance using an AI model that takes user voice data as input and outputs voice guidance.

[0034] The voice guidance unit can receive user opinions. Specific methods and content for receiving opinions include, but are not limited to, voice input and touch panel input. For example, the voice guidance unit can receive user opinions using voice input. It can also receive user opinions using touch panel input. Furthermore, the voice guidance unit can receive user feedback. For example, the voice guidance unit can receive user opinions via voice and use them to improve the system. This allows for system improvement by receiving user feedback. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or without AI. For example, the voice guidance unit can receive opinions using an AI model that takes user opinions as input and outputs feedback.

[0035] The acquisition unit can analyze the user's past ATM usage history and select the optimal video acquisition method. For example, the acquisition unit can acquire video at times when the user frequently used ATMs in the past. The acquisition unit can also acquire video based on the location information of ATMs the user has used in the past. Furthermore, the acquisition unit can analyze the user's past usage patterns and select the optimal video acquisition method. For example, the acquisition unit can analyze the user's past usage history in detail and select the optimal video acquisition method. Past ATM usage history includes, but is not limited to, transaction history and operation history. For example, the acquisition unit can acquire video based on transaction history. Furthermore, the acquisition unit can also analyze past usage history using AI. For example, the acquisition unit can select a video acquisition method using an AI model that takes transaction history and operation history as input and outputs the optimal video acquisition method. This improves the accuracy of video acquisition by selecting the optimal video acquisition method based on past usage history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can select a video acquisition method using an AI model that takes the user's past usage history data as input and outputs a video acquisition method.

[0036] The acquisition unit can filter video based on the user's current environment and background information during acquisition. For example, if the user is in a bright location, the acquisition unit adjusts the brightness of the video. The acquisition unit can also remove background noise if the user is in a noisy location. Furthermore, if the user is outdoors, the acquisition unit can filter the video considering weather information. For example, the acquisition unit can analyze the user's current environment in detail and adjust the brightness of the video. The current environment includes, but is not limited to, ambient brightness and background objects. The acquisition unit can filter the video based on ambient brightness, for example. It can also filter the video based on background objects. Furthermore, the acquisition unit can analyze the current environment using AI. For example, the acquisition unit can filter the video using an AI model that takes ambient brightness and background objects as input and outputs filtered video. Background information includes, but is not limited to, background color and background movement. The acquisition unit can filter the video based on background color, for example. It can also filter the video based on background movement. Furthermore, the acquisition unit can also analyze background information using AI. For example, the acquisition unit can filter the video using an AI model that takes background color and movement as input and outputs filtered video. This allows for the acquisition of more appropriate video by filtering based on the environment and background information. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can filter the video using an AI model that takes the user's current environment data as input and outputs filtered video.

[0037] The acquisition unit can prioritize acquiring highly relevant videos based on the user's geographical location information when acquiring video. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring videos related to that region. It can also prioritize acquiring videos related to a specific facility if the user is in that facility. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring videos related to that event. For example, the acquisition unit can analyze the user's geographical location information in detail and prioritize acquiring highly relevant videos. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the acquisition unit can acquire video based on GPS data. It can also acquire video based on location information services. Furthermore, the acquisition unit can analyze geographical location information using AI. For example, the acquisition unit can acquire video using an AI model that takes GPS data or location information services as input and outputs highly relevant videos. This allows for the priority acquisition of highly relevant videos by considering geographical location information. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can acquire video using an AI model that takes the user's geographic location data as input and outputs highly relevant video.

[0038] The acquisition unit can analyze the user's social media activity when acquiring video and acquire relevant video. For example, the acquisition unit can acquire video related to places the user has shared on social media. It can also acquire video related to events the user has shown interest in on social media. Furthermore, the acquisition unit can acquire video related to accounts the user follows on social media. For example, the acquisition unit can analyze the user's social media activity in detail and acquire relevant video. Social media activity includes, but is not limited to, posts and the number of likes. For example, the acquisition unit can acquire video based on posts. It can also acquire video based on the number of likes. Furthermore, the acquisition unit can analyze social media activity using AI. For example, the acquisition unit can acquire video using an AI model that takes posts and the number of likes as input and outputs relevant video. This allows for the acquisition of relevant video by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can acquire video using an AI model that takes the user's social media activity data as input and outputs relevant video.

[0039] The discrimination unit can improve its discrimination algorithm based on the user's past video data when determining age. For example, the discrimination unit can analyze facial features based on the user's past video data. It can also analyze the number of wrinkles based on the user's past video data. Furthermore, the discrimination unit can improve the accuracy of age determination based on the user's past video data. For example, the discrimination unit can analyze the user's past video data in detail and improve the discrimination algorithm. Past video data includes, but is not limited to, the retention period and data format. For example, the discrimination unit can analyze video data based on the retention period. It can also analyze video data based on the data format. Furthermore, the discrimination unit can analyze past video data using AI. For example, the discrimination unit can improve its discrimination algorithm using an AI model that takes the retention period and data format as input and outputs a discrimination algorithm. This improves the accuracy of the discrimination algorithm by referring to past video data. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or without using AI. For example, the discrimination unit can improve its discrimination algorithm by using an AI model that takes the user's past video data as input and outputs a discrimination algorithm.

[0040] The discrimination unit can improve the accuracy of age determination based on the user's lifestyle and health status. For example, the discrimination unit can determine age based on the user's lifestyle data. The discrimination unit can also determine age based on the user's health status data. Furthermore, the discrimination unit can improve the accuracy of age determination by comprehensively considering the user's lifestyle and health status. For example, the discrimination unit can determine age by analyzing the user's lifestyle data in detail. Lifestyle habits include, but are not limited to, examples such as frequency of meals and exercise habits. For example, the discrimination unit can determine age based on frequency of meals. The discrimination unit can also determine age based on exercise habits. Furthermore, the discrimination unit can analyze lifestyle habits using AI. For example, the discrimination unit can determine age using an AI model that takes frequency of meals and exercise habits as input and outputs age. Health status includes, but is not limited to, examples such as medical records and self-reports. For example, the discrimination unit can determine age based on medical records. The discrimination unit can also determine age based on self-reports. Furthermore, the discrimination unit can also analyze health status using AI. For example, the discrimination unit can determine age using an AI model that takes medical records or self-reported data as input and outputs age. This improves the accuracy of age determination by taking lifestyle habits and health status into consideration. Some or all of the above-described processes in the discrimination unit may be performed using AI, or not. For example, the discrimination unit can determine age using an AI model that takes user lifestyle habit and health status data as input and outputs age.

[0041] The discrimination unit can determine an age based on the user's geographical distribution. For example, if the user is in a specific region, the discrimination unit will consider the age distribution of that region. Furthermore, if the user is in a specific facility, the discrimination unit can consider the age distribution of that facility. Additionally, if the user is participating in a specific event, the discrimination unit can consider the age distribution of that event. For example, the discrimination unit can analyze the user's geographical distribution in detail to determine their age. Geographical distribution includes, but is not limited to, regional population distribution and differences between urban and rural areas. For example, the discrimination unit can determine age based on regional population distribution. It can also determine age based on differences between urban and rural areas. Furthermore, the discrimination unit can analyze geographical distribution using AI. For example, the discrimination unit can determine age using an AI model that takes regional population distribution or differences between urban and rural areas as input and outputs age. This allows for more accurate age determination by considering geographical distribution. Some or all of the above-described processes in the discrimination unit may be performed using, for example, AI, or without AI. For example, the discrimination unit can determine the user's age using an AI model that takes the user's geographical distribution data as input and outputs their age.

[0042] The discrimination unit can improve the accuracy of age determination based on the user's relevant literature. For example, the discrimination unit can analyze facial features based on the user's relevant literature. The discrimination unit can also analyze the number of wrinkles based on the user's relevant literature. Furthermore, the discrimination unit can improve the accuracy of age determination based on the user's relevant literature. For example, the discrimination unit can determine the age by analyzing the user's relevant literature in detail. Relevant literature includes, but is not limited to, academic papers and technical reports. For example, the discrimination unit can determine the age based on academic papers. The discrimination unit can also determine the age based on technical reports. Furthermore, the discrimination unit can analyze relevant literature using AI. For example, the discrimination unit can determine the age using an AI model that takes academic papers or technical reports as input and outputs the age. This improves the accuracy of age determination by referring to relevant literature. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or without using AI. For example, the discrimination unit can determine the user's age using an AI model that takes the user's relevant literature data as input and outputs the age.

[0043] The display unit can select an appropriate display method by referring to the user's past operation history when displaying a guide. For example, the display unit can select the optimal display method based on display methods previously used by the user. The display unit can also select the most efficient display method from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history to select the optimal display method. For example, the display unit can analyze the user's past operation history in detail to select the optimal display method. Past operation history includes, but is not limited to, the frequency and type of operations. For example, the display unit can select a display method based on the frequency of operations. The display unit can also select a display method based on the type of operation. Furthermore, the display unit can analyze past operation history using AI. For example, the display unit can select a display method using an AI model that takes the frequency and type of operations as input and outputs a display method. This allows the optimal display method to be selected by referring to past operation history. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without using AI. For example, the display unit can select a display method using an AI model that takes the user's past operation history data as input and outputs a display method.

[0044] The display unit can customize the displayed content based on the user's current situation when displaying guidance. For example, if the user is nervous, the display unit can provide content in calm colors. It can also provide content in bright colors if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can provide concise and highly visible content. For example, the display unit can analyze the user's facial expression and provide content in calm colors if the user is nervous. Current situation includes, but is not limited to, the user's location and surrounding environment. For example, the display unit can customize the displayed content based on the user's location. It can also customize the displayed content based on the surrounding environment. Furthermore, the display unit can analyze the current situation using AI. For example, the display unit can customize the displayed content using an AI model that takes the user's location and surrounding environment as input and outputs the displayed content. This allows for optimal display for the user by customizing the displayed content based on the current situation. Some or all of the above processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can customize its display content using an AI model that takes the user's current status data as input and outputs the content to be displayed.

[0045] The display unit can select an appropriate display method based on the user's geographical location information when displaying information. For example, if the user is in a specific region, the display unit can select a display method related to that region. The display unit can also select a display method related to a specific facility if the user is in that facility. Furthermore, if the user is participating in a specific event, the display unit can select a display method related to that event. For example, the display unit can analyze the user's geographical location information in detail and select an appropriate display method. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the display unit can select a display method based on GPS data. The display unit can also select a display method based on location information services. Furthermore, the display unit can analyze geographical location information using AI. For example, the display unit can select a display method using an AI model that takes GPS data or location information services as input and outputs a display method. This allows for the selection of an appropriate display method by considering geographical location information. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can select a display method using an AI model that takes the user's geographic location data as input and outputs a display method.

[0046] The display unit can adjust the displayed content based on the user's social media activity when displaying a guide. For example, the display unit can adjust the displayed content based on information shared by the user on social media. It can also adjust the displayed content based on information the user has shown interest in on social media. Furthermore, the display unit can adjust the displayed content based on accounts the user follows on social media. For example, the display unit can analyze the user's social media activity in detail and adjust the displayed content. Social media activity includes, but is not limited to, posts and the number of likes. For example, the display unit can adjust the displayed content based on posts. It can also adjust the displayed content based on the number of likes. Furthermore, the display unit can analyze social media activity using AI. For example, the display unit can adjust the displayed content using an AI model that takes posts and the number of likes as input and outputs displayed content. This allows for the provision of user-relevant displayed content by analyzing social media activity. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can adjust the displayed content using an AI model that takes the user's social media activity data as input and outputs the content to be displayed.

[0047] The voice guidance unit can select an appropriate guidance method by referring to the user's past operation history when providing voice guidance. For example, the voice guidance unit can select the optimal guidance method based on guidance methods the user has used in the past. The voice guidance unit can also select the most efficient guidance method from the user's past operation history. Furthermore, the voice guidance unit can also select the optimal guidance method by analyzing the user's past operation history. For example, the voice guidance unit can analyze the user's past operation history in detail and select the optimal guidance method. Past operation history includes, but is not limited to, the frequency and type of operations. For example, the voice guidance unit can select a guidance method based on the frequency of operations. The voice guidance unit can also select a guidance method based on the type of operation. Furthermore, the voice guidance unit can also analyze past operation history using AI. For example, the voice guidance unit can select a guidance method using an AI model that takes the frequency and type of operations as input and outputs a guidance method. This allows the optimal guidance method to be selected by referring to past operation history. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or without using AI. For example, the voice guidance unit can select a guidance method using an AI model that takes the user's past operation history data as input and outputs guidance methods.

[0048] The voice guidance unit can customize the guidance content based on the user's current situation when providing voice guidance. For example, if the user is nervous, the voice guidance unit can provide guidance in a calm voice. Conversely, if the user is relaxed, the voice guidance unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the voice guidance unit can provide quick and concise guidance. For example, the voice guidance unit can analyze the user's facial expression and provide guidance in a calm voice if the user is nervous. Current situation includes, but is not limited to, the user's location and surrounding environment. For example, the voice guidance unit can customize the guidance content based on the user's location. It can also customize the guidance content based on the surrounding environment. Furthermore, the voice guidance unit can analyze the current situation using AI. For example, the voice guidance unit can customize the guidance content using an AI model that takes the user's location and surrounding environment as input and outputs guidance content. This allows for optimal guidance for the user by customizing the guidance content based on the current situation. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or without AI. For example, the voice guidance unit can customize the guidance content using an AI model that takes the user's current situation data as input and outputs guidance content.

[0049] The voice guidance unit can select an appropriate guidance method based on the user's geographical location information during voice guidance. For example, if the user is in a specific region, the voice guidance unit can select a guidance method related to that region. It can also select a guidance method related to a specific facility if the user is in that facility. Furthermore, if the user is participating in a specific event, the voice guidance unit can select a guidance method related to that event. For example, the voice guidance unit can analyze the user's geographical location information in detail and select an appropriate guidance method. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the voice guidance unit can select a guidance method based on GPS data. It can also select a guidance method based on location information services. Furthermore, the voice guidance unit can analyze geographical location information using AI. For example, the voice guidance unit can select a guidance method using an AI model that takes GPS data and location information services as input and outputs a guidance method. This allows for the selection of an appropriate guidance method by considering geographical location information. Some or all of the above-described processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can select a guidance method using an AI model that takes the user's geographic location data as input and outputs a guidance method.

[0050] The voice guidance unit can adjust the guidance content based on the user's social media activity during voice guidance. For example, the voice guidance unit can adjust the guidance content based on information the user has shared on social media. It can also adjust the guidance content based on information the user has shown interest in on social media. Furthermore, the voice guidance unit can adjust the guidance content based on accounts the user follows on social media. For example, the voice guidance unit can analyze the user's social media activity in detail and adjust the guidance content. Social media activity includes, but is not limited to, posts and the number of likes. For example, the voice guidance unit can adjust the guidance content based on posts. It can also adjust the guidance content based on the number of likes. Furthermore, the voice guidance unit can analyze social media activity using AI. For example, the voice guidance unit can adjust the guidance content using an AI model that takes posts and the number of likes as input and outputs guidance content. This allows the voice guidance unit to provide guidance content relevant to the user by analyzing social media activity. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or not using AI. For example, the voice guidance unit can adjust the guidance content using an AI model that takes the user's social media activity data as input and outputs guidance content.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The acquisition unit can acquire the user's biometric information and provide it to the discrimination unit. For example, the acquisition unit can acquire the user's heart rate and body temperature. It can also acquire the user's blood pressure. Furthermore, the acquisition unit can acquire the user's respiratory rate. Based on the acquired biometric information, the discrimination unit can estimate the user's health status and improve the accuracy of age discrimination. For example, the discrimination unit can estimate the user's health status based on heart rate and body temperature and adjust the accuracy of age discrimination. This makes it possible to perform age discrimination that takes the user's health status into consideration.

[0053] The display unit can select the optimal display method by referring to the user's past operation history. For example, it can select the optimal display method based on the display methods the user has used in the past. Furthermore, the display unit can select the most efficient display method from the user's past operation history. In addition, the display unit can analyze the user's past operation history to select the optimal display method. This allows the optimal display method to be selected by referring to past operation history.

[0054] The voice guidance unit can select the optimal guidance method by referring to the user's past operation history. For example, it can select the optimal guidance method based on the guidance methods the user has used in the past. Furthermore, the voice guidance unit can select the most efficient guidance method from the user's past operation history. In addition, the voice guidance unit can analyze the user's past operation history to select the optimal guidance method. This allows the system to select the optimal guidance method by referring to past operation history.

[0055] The acquisition unit can analyze the user's past ATM usage history and select the optimal video acquisition method. For example, it can acquire video at times when the user frequently used ATMs in the past. The acquisition unit can also acquire video based on the location information of ATMs the user has used in the past. Furthermore, the acquisition unit can analyze the user's past usage patterns and select the optimal video acquisition method. This improves the accuracy of video acquisition by selecting the optimal video acquisition method based on past usage history.

[0056] The display unit can select an appropriate display method based on the user's geographical location. For example, if the user is in a specific region, it can select a display method related to that region. Furthermore, if the user is in a specific facility, the display unit can select a display method related to that facility. In this way, by considering geographical location information, the display unit can select an appropriate display method.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires the user's video. The user's video includes, but is not limited to, still images, videos, and high resolution. The acquisition unit can acquire the user's video using, for example, a camera installed in an ATM. The acquisition unit can also acquire the user's video in real time. For example, the acquisition unit can acquire the user's facial features in detail using a high-resolution camera. Step 2: The discrimination unit analyzes the video acquired by the acquisition unit to determine the user's age. Specific methods and criteria for determining age include, but are not limited to, facial recognition technology, the number of wrinkles, and skin condition. For example, the discrimination unit can estimate the user's age using facial recognition technology. It can also determine age by analyzing the number of wrinkles and skin condition. Furthermore, the discrimination unit can determine age using AI. For example, the discrimination unit can determine age using an AI model that takes facial features as input and outputs age. Step 3: The display unit switches the ATM screen to a guide display when the detection unit determines that the user is above a certain age. The specific content and format of the guide display may include, but are not limited to, characters, animations, and text displays. For example, the display unit may display a friendly character to the user. The display unit may also display a real person to the user. Furthermore, the display unit may use animation to provide a guide display. For example, the display unit may display an animation in which a friendly character guides the user through the operating procedure. Step 4: The voice guidance unit provides voice guidance based on the guide display switched by the display unit. The specific content and method of voice guidance include, but are not limited to, the type of voice, the content of the guidance, and the tone of voice. For example, the voice guidance unit engages in interactive conversation with the user and provides voice guidance on the operating procedure. The voice guidance unit can also provide specific instructions by voice. Furthermore, the voice guidance unit can accept user feedback. For example, the voice guidance unit provides detailed instructions by voice, such as, "Next, please insert the card."

[0059] (Example of form 2) An ATM system according to an embodiment of the present invention is an ATM system equipped with an image recognition AI that can determine age from video and a voice generation AI that can engage in interactive conversation. When this ATM system determines that the user is elderly, it switches the ATM screen to a guide display and starts voice guidance. This allows elderly people to easily use various services without having to operate the ATM. For example, this includes "charging electronic payment systems." This ATM system aims to improve the IT literacy of the elderly and make their daily lives more convenient. First, a camera installed in the ATM acquires video of the user. This video is analyzed by the image recognition AI to determine the user's age. For example, the age is estimated based on facial features and the number of wrinkles. Next, when the user is determined to be elderly, the ATM screen switches to a guide display. The guide display shows a friendly character or person to the user and guides them through the operation procedure one step at a time. Furthermore, the voice generation AI engages in interactive conversation with the user and provides voice guidance on the operation procedure. For example, it provides specific instructions by voice, such as "Next, please insert your card." This allows elderly people to not only follow the instructions on the screen but also to proceed with the operation while listening to the voice guidance. This system allows elderly people to easily access various services without having to operate an ATM. For example, when "charging an electronic payment system," the charge is completed simply by following the on-screen and voice guidance. This ATM system aims to improve the IT literacy of the elderly and make their daily lives more convenient. For instance, even elderly people who have difficulty using ATMs can easily operate it, thus promoting the use of electronic payment systems. In addition, the voice guidance clarifies the operating procedures, reducing the risk of errors and improving safety. In this way, the ATM system can improve the IT literacy of the elderly and make their daily lives more convenient.

[0060] The ATM system according to this embodiment includes an acquisition unit, a discrimination unit, a display unit, and a voice guidance unit. The acquisition unit acquires the user's image. The user's image includes, but is not limited to, still images, videos, and resolution. The acquisition unit acquires the user's image using, for example, a camera installed in the ATM. The acquisition unit can also acquire the user's image in real time. For example, the acquisition unit can acquire the user's facial features in detail using a high-resolution camera. The discrimination unit analyzes the image acquired by the acquisition unit and determines the user's age. Specific methods and criteria for determining age include, but are not limited to, facial recognition technology, the number of wrinkles, and skin condition. For example, the discrimination unit estimates the user's age using facial recognition technology. The discrimination unit can also determine age by analyzing the number of wrinkles and skin condition. Furthermore, the discrimination unit can also determine age using AI. For example, the discrimination unit can determine age using an AI model that takes facial features as input and outputs age. The display unit switches the ATM screen to a guide display when the discrimination unit determines that the user is above a certain age. The specific content and format of the guide display include, but are not limited to, characters, animations, and text displays. For example, the display unit may display a friendly character to the user. The display unit may also display a real person to the user. Furthermore, the display unit may use animation to provide a guide display. For example, the display unit may display an animation in which a friendly character guides the user through the operating procedure. The voice guidance unit provides voice guidance based on the guide display switched by the display unit. The specific content and method of the voice guidance include, but are not limited to, the type of voice, the content of the guidance, and the tone of voice. For example, the voice guidance unit may engage in interactive conversation with the user and provide voice guidance on the operating procedure. The voice guidance unit may also provide specific instructions by voice. Furthermore, the voice guidance unit may accept user feedback. For example, the voice guidance unit may provide detailed instructions by voice, such as, "Next, please insert your card."As a result, the ATM system according to this embodiment can simplify the use of the ATM by determining the user's age and providing appropriate guidance to elderly people. Some or all of the above-described processing in the voice guidance unit may be performed using AI, for example, or without using AI. For example, the voice guidance unit can provide voice guidance using an AI model that takes the user's voice data as input and outputs voice guidance.

[0061] The discrimination unit can estimate age based on facial features or the number of wrinkles. Facial features include, but are not limited to, the position of the eyes, the shape of the nose, and the size of the mouth. For example, the discrimination unit can estimate age based on the position of the eyes. It can also estimate age based on the shape of the nose. Furthermore, it can also estimate age based on the size of the mouth. For example, the discrimination unit can analyze the position of the eyes in detail and estimate age. The number of wrinkles includes, but are not limited to, the number of wrinkles in a specific part of the face and the depth of the wrinkles. For example, the discrimination unit can estimate age based on the number of wrinkles in a specific part of the face. It can also estimate age based on the depth of the wrinkles. Furthermore, the discrimination unit can estimate age by combining the number and depth of wrinkles. For example, the discrimination unit can analyze the number of wrinkles in a specific part of the face in detail and estimate age. This improves the accuracy of age determination by estimating age based on facial features and the number of wrinkles. Some or all of the processing described above in the discrimination unit may be performed using AI, for example, or without AI. For example, the discrimination unit can estimate age using an AI model that takes facial features and the number of wrinkles as input and outputs age.

[0062] The display unit can display characters or people to the user. Characters include, but are not limited to, anime characters or live-action characters. The display unit can, for example, display anime characters. The display unit can also display live-action characters. Furthermore, the display unit can display virtual characters. For example, the display unit can display an anime character guiding the user through the operation procedure. People include, but are not limited to, real people or virtual people. The display unit can, for example, display real people. The display unit can also display virtual people. Furthermore, the display unit can display people using animation. For example, the display unit can display a real person guiding the user through the operation procedure. This allows users to operate with confidence by displaying friendly characters or people. Some or all of the above processing in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can perform the display using an AI model that takes the display of characters or people as input and outputs the display content.

[0063] The voice guidance unit can engage in interactive conversation with the user and provide voice guidance on operating procedures. Interactive conversation includes, but is not limited to, speech recognition technology and conversation scenarios. For example, the voice guidance unit can engage in interactive conversation with the user using speech recognition technology. It can also engage in interactive conversation based on conversation scenarios. Furthermore, the voice guidance unit can engage in interactive conversation using AI. For example, the voice guidance unit can analyze the user's voice using speech recognition technology and generate an appropriate response. Guidance on operating procedures includes, but is not limited to, specific instructions and voice tone. For example, the voice guidance unit can provide specific instructions by voice. It can also provide guidance by adjusting the voice tone. Furthermore, the voice guidance unit can accept user feedback. For example, the voice guidance unit can provide specific instructions by voice, such as, "Next, please insert the card." This makes it easier for the user to understand the operating procedures through interactive conversation. Some or all of the above-described processes in the voice guidance unit may be performed using, for example, AI, or without AI. For example, the voice guidance unit can provide voice guidance using an AI model that takes user voice data as input and outputs voice guidance.

[0064] The voice guidance unit can provide detailed instructions by voice, such as "Next, please insert the card." These detailed instructions may include, but are not limited to, instructions on how to insert the card or how to press buttons. For example, the voice guidance unit can provide voice instructions on how to insert the card. It can also provide voice instructions on how to press buttons. Furthermore, the voice guidance unit can accept user feedback. For example, the voice guidance unit can provide specific instructions by voice, such as "Next, please insert the card." By providing specific instructions by voice, users can proceed with the operation without getting lost. Some or all of the above processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can provide voice guidance using an AI model that takes user voice data as input and outputs voice guidance.

[0065] The voice guidance unit can receive user opinions. Specific methods and content for receiving opinions include, but are not limited to, voice input and touch panel input. For example, the voice guidance unit can receive user opinions using voice input. It can also receive user opinions using touch panel input. Furthermore, the voice guidance unit can receive user feedback. For example, the voice guidance unit can receive user opinions via voice and use them to improve the system. This allows for system improvement by receiving user feedback. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or without AI. For example, the voice guidance unit can receive opinions using an AI model that takes user opinions as input and outputs feedback.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of video acquisition based on the estimated emotions. For example, if the user is tense, the acquisition unit can delay video acquisition until the user is relaxed. Conversely, if the user is relaxed, the acquisition unit can acquire video immediately. Furthermore, if the user is in a hurry, the acquisition unit can acquire video quickly. For example, the acquisition unit can analyze the user's facial expressions and delay video acquisition if the user is tense. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the acquisition unit can estimate the user's emotions using facial recognition technology. The acquisition unit can also estimate the user's emotions using voice analysis technology. Furthermore, the acquisition unit can also estimate emotions using AI. For example, the acquisition unit can estimate emotions using an AI model that takes facial data as input and outputs emotions. This allows for the acquisition of more appropriate video by adjusting the timing of video acquisition according to the user's emotions. Some or all of the above-described processes in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can adjust the timing of video acquisition using an AI model that takes user emotion data as input and outputs the timing of video acquisition.

[0067] The acquisition unit can analyze the user's past ATM usage history and select the optimal video acquisition method. For example, the acquisition unit can acquire video at times when the user frequently used ATMs in the past. The acquisition unit can also acquire video based on the location information of ATMs the user has used in the past. Furthermore, the acquisition unit can analyze the user's past usage patterns and select the optimal video acquisition method. For example, the acquisition unit can analyze the user's past usage history in detail and select the optimal video acquisition method. Past ATM usage history includes, but is not limited to, transaction history and operation history. For example, the acquisition unit can acquire video based on transaction history. Furthermore, the acquisition unit can also analyze past usage history using AI. For example, the acquisition unit can select a video acquisition method using an AI model that takes transaction history and operation history as input and outputs the optimal video acquisition method. This improves the accuracy of video acquisition by selecting the optimal video acquisition method based on past usage history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without using AI. For example, the acquisition unit can select a video acquisition method using an AI model that takes the user's past usage history data as input and outputs a video acquisition method.

[0068] The acquisition unit can filter video based on the user's current environment and background information during acquisition. For example, if the user is in a bright location, the acquisition unit adjusts the brightness of the video. The acquisition unit can also remove background noise if the user is in a noisy location. Furthermore, if the user is outdoors, the acquisition unit can filter the video considering weather information. For example, the acquisition unit can analyze the user's current environment in detail and adjust the brightness of the video. The current environment includes, but is not limited to, ambient brightness and background objects. The acquisition unit can filter the video based on ambient brightness, for example. It can also filter the video based on background objects. Furthermore, the acquisition unit can analyze the current environment using AI. For example, the acquisition unit can filter the video using an AI model that takes ambient brightness and background objects as input and outputs filtered video. Background information includes, but is not limited to, background color and background movement. The acquisition unit can filter the video based on background color, for example. It can also filter the video based on background movement. Furthermore, the acquisition unit can also analyze background information using AI. For example, the acquisition unit can filter the video using an AI model that takes background color and movement as input and outputs filtered video. This allows for the acquisition of more appropriate video by filtering based on the environment and background information. Some or all of the above processing in the acquisition unit may be performed using AI, or not. For example, the acquisition unit can filter the video using an AI model that takes the user's current environment data as input and outputs filtered video.

[0069] The acquisition unit can estimate the user's emotions and determine the priority of the video to acquire based on the estimated user emotions. For example, if the user is nervous, the acquisition unit will prioritize acquiring facial expressions. The acquisition unit can also acquire the entire video if the user is relaxed. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring only the important parts. For example, the acquisition unit analyzes the user's facial expressions and prioritizes acquiring facial expressions if the user is nervous. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the acquisition unit can estimate the user's emotions using facial recognition technology. The acquisition unit can also estimate the user's emotions using voice analysis technology. Furthermore, the acquisition unit can estimate emotions using AI. For example, the acquisition unit can estimate emotions using an AI model that takes facial data as input and outputs emotions. This allows for the priority of video to be determined according to the user's emotions, thereby prioritizing the acquisition of important video. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can determine the priority of videos using an AI model that takes user emotion data as input and outputs the priority of the videos.

[0070] The acquisition unit can prioritize acquiring highly relevant videos based on the user's geographical location information when acquiring video. For example, if the user is in a specific region, the acquisition unit will prioritize acquiring videos related to that region. It can also prioritize acquiring videos related to a specific facility if the user is in that facility. Furthermore, if the user is participating in a specific event, the acquisition unit can prioritize acquiring videos related to that event. For example, the acquisition unit can analyze the user's geographical location information in detail and prioritize acquiring highly relevant videos. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the acquisition unit can acquire video based on GPS data. It can also acquire video based on location information services. Furthermore, the acquisition unit can analyze geographical location information using AI. For example, the acquisition unit can acquire video using an AI model that takes GPS data or location information services as input and outputs highly relevant videos. This allows for the priority acquisition of highly relevant videos by considering geographical location information. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can acquire video using an AI model that takes the user's geographic location data as input and outputs highly relevant video.

[0071] The acquisition unit can analyze the user's social media activity when acquiring video and acquire relevant video. For example, the acquisition unit can acquire video related to places the user has shared on social media. It can also acquire video related to events the user has shown interest in on social media. Furthermore, the acquisition unit can acquire video related to accounts the user follows on social media. For example, the acquisition unit can analyze the user's social media activity in detail and acquire relevant video. Social media activity includes, but is not limited to, posts and the number of likes. For example, the acquisition unit can acquire video based on posts. It can also acquire video based on the number of likes. Furthermore, the acquisition unit can analyze social media activity using AI. For example, the acquisition unit can acquire video using an AI model that takes posts and the number of likes as input and outputs relevant video. This allows for the acquisition of relevant video by analyzing social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can acquire video using an AI model that takes the user's social media activity data as input and outputs relevant video.

[0072] The discrimination unit can estimate the user's emotions and adjust the accuracy of age determination based on the estimated emotions. For example, if the user is nervous, the discrimination unit will analyze facial features in more detail. The discrimination unit can also determine the user's age based on the overall image if the user is relaxed. Furthermore, if the user is in a hurry, the discrimination unit can quickly determine their age. For example, the discrimination unit will analyze the user's facial expressions and, if nervous, analyze facial features in detail. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the discrimination unit can estimate the user's emotions using facial recognition technology. The discrimination unit can also estimate the user's emotions using voice analysis technology. Furthermore, the discrimination unit can estimate emotions using AI. For example, the discrimination unit can estimate emotions using an AI model that takes facial data as input and outputs emotions. This allows for more accurate age determination by adjusting the accuracy of age determination according to the user's emotions. Some or all of the above-described processes in the discrimination unit may be performed using, for example, AI, or without AI. For example, the discrimination unit can adjust the accuracy of age discrimination using an AI model that takes user emotion data as input and outputs the accuracy of age discrimination.

[0073] The discrimination unit can improve its discrimination algorithm based on the user's past video data when determining age. For example, the discrimination unit can analyze facial features based on the user's past video data. It can also analyze the number of wrinkles based on the user's past video data. Furthermore, the discrimination unit can improve the accuracy of age determination based on the user's past video data. For example, the discrimination unit can analyze the user's past video data in detail and improve the discrimination algorithm. Past video data includes, but is not limited to, the retention period and data format. For example, the discrimination unit can analyze video data based on the retention period. It can also analyze video data based on the data format. Furthermore, the discrimination unit can analyze past video data using AI. For example, the discrimination unit can improve its discrimination algorithm using an AI model that takes the retention period and data format as input and outputs a discrimination algorithm. This improves the accuracy of the discrimination algorithm by referring to past video data. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or without using AI. For example, the discrimination unit can improve its discrimination algorithm by using an AI model that takes the user's past video data as input and outputs a discrimination algorithm.

[0074] The discrimination unit can improve the accuracy of age determination based on the user's lifestyle and health status. For example, the discrimination unit can determine age based on the user's lifestyle data. The discrimination unit can also determine age based on the user's health status data. Furthermore, the discrimination unit can improve the accuracy of age determination by comprehensively considering the user's lifestyle and health status. For example, the discrimination unit can determine age by analyzing the user's lifestyle data in detail. Lifestyle habits include, but are not limited to, examples such as frequency of meals and exercise habits. For example, the discrimination unit can determine age based on frequency of meals. The discrimination unit can also determine age based on exercise habits. Furthermore, the discrimination unit can analyze lifestyle habits using AI. For example, the discrimination unit can determine age using an AI model that takes frequency of meals and exercise habits as input and outputs age. Health status includes, but is not limited to, examples such as medical records and self-reports. For example, the discrimination unit can determine age based on medical records. The discrimination unit can also determine age based on self-reports. Furthermore, the discrimination unit can also analyze health status using AI. For example, the discrimination unit can determine age using an AI model that takes medical records or self-reported data as input and outputs age. This improves the accuracy of age determination by taking lifestyle habits and health status into consideration. Some or all of the above-described processes in the discrimination unit may be performed using AI, or not. For example, the discrimination unit can determine age using an AI model that takes user lifestyle habit and health status data as input and outputs age.

[0075] The discrimination unit can estimate the user's emotions and adjust the order in which it displays the age discrimination results based on the estimated emotions. For example, if the user is nervous, the discrimination unit can display a concise result first. It can also display a detailed result if the user is relaxed. Furthermore, if the user is in a hurry, the discrimination unit can display the results quickly. For example, the discrimination unit can analyze the user's facial expressions and display a concise result first if the user is nervous. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the discrimination unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the discrimination unit can estimate emotions using AI. For example, the discrimination unit can estimate emotions using an AI model that takes facial data as input and outputs emotions. This allows for a display that is easy for the user to understand by adjusting the order in which the results are displayed according to the user's emotions. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or not using AI. For example, the discrimination unit can adjust the display order of the results using an AI model that takes user emotion data as input and outputs the order in which the results are displayed.

[0076] The discrimination unit can determine an age based on the user's geographical distribution. For example, if the user is in a specific region, the discrimination unit will consider the age distribution of that region. Furthermore, if the user is in a specific facility, the discrimination unit can consider the age distribution of that facility. Additionally, if the user is participating in a specific event, the discrimination unit can consider the age distribution of that event. For example, the discrimination unit can analyze the user's geographical distribution in detail to determine their age. Geographical distribution includes, but is not limited to, regional population distribution and differences between urban and rural areas. For example, the discrimination unit can determine age based on regional population distribution. It can also determine age based on differences between urban and rural areas. Furthermore, the discrimination unit can analyze geographical distribution using AI. For example, the discrimination unit can determine age using an AI model that takes regional population distribution or differences between urban and rural areas as input and outputs age. This allows for more accurate age determination by considering geographical distribution. Some or all of the above-described processes in the discrimination unit may be performed using, for example, AI, or without AI. For example, the discrimination unit can determine the user's age using an AI model that takes the user's geographical distribution data as input and outputs their age.

[0077] The discrimination unit can improve the accuracy of age determination based on the user's relevant literature. For example, the discrimination unit can analyze facial features based on the user's relevant literature. The discrimination unit can also analyze the number of wrinkles based on the user's relevant literature. Furthermore, the discrimination unit can improve the accuracy of age determination based on the user's relevant literature. For example, the discrimination unit can determine the age by analyzing the user's relevant literature in detail. Relevant literature includes, but is not limited to, academic papers and technical reports. For example, the discrimination unit can determine the age based on academic papers. The discrimination unit can also determine the age based on technical reports. Furthermore, the discrimination unit can analyze relevant literature using AI. For example, the discrimination unit can determine the age using an AI model that takes academic papers or technical reports as input and outputs the age. This improves the accuracy of age determination by referring to relevant literature. Some or all of the above processing in the discrimination unit may be performed using, for example, AI, or without using AI. For example, the discrimination unit can determine the user's age using an AI model that takes the user's relevant literature data as input and outputs the age.

[0078] The display unit can estimate the user's emotions and adjust the guide display method based on the estimated user emotions. For example, if the user is nervous, the display unit can provide a guide display in calm colors. It can also provide a guide display in bright colors if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can provide a concise and highly visible guide display. For example, the display unit can analyze the user's facial expressions and provide a guide display in calm colors if the user is nervous. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the display unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the display unit can estimate emotions using AI. For example, the display unit can estimate emotions using an AI model that takes facial expression data as input and outputs emotions. This allows for a user-friendly display by adjusting the guide display method according to the user's emotions. Some or all of the above-described processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can adjust the method of displaying the guide using an AI model that takes user emotion data as input and outputs a method for displaying the guide.

[0079] The display unit can select an appropriate display method by referring to the user's past operation history when displaying a guide. For example, the display unit can select the optimal display method based on display methods previously used by the user. The display unit can also select the most efficient display method from the user's past operation history. Furthermore, the display unit can analyze the user's past operation history to select the optimal display method. For example, the display unit can analyze the user's past operation history in detail to select the optimal display method. Past operation history includes, but is not limited to, the frequency and type of operations. For example, the display unit can select a display method based on the frequency of operations. The display unit can also select a display method based on the type of operation. Furthermore, the display unit can analyze past operation history using AI. For example, the display unit can select a display method using an AI model that takes the frequency and type of operations as input and outputs a display method. This allows the optimal display method to be selected by referring to past operation history. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without using AI. For example, the display unit can select a display method using an AI model that takes the user's past operation history data as input and outputs a display method.

[0080] The display unit can customize the displayed content based on the user's current situation when displaying guidance. For example, if the user is nervous, the display unit can provide content in calm colors. It can also provide content in bright colors if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can provide concise and highly visible content. For example, the display unit can analyze the user's facial expression and provide content in calm colors if the user is nervous. Current situation includes, but is not limited to, the user's location and surrounding environment. For example, the display unit can customize the displayed content based on the user's location. It can also customize the displayed content based on the surrounding environment. Furthermore, the display unit can analyze the current situation using AI. For example, the display unit can customize the displayed content using an AI model that takes the user's location and surrounding environment as input and outputs the displayed content. This allows for optimal display for the user by customizing the displayed content based on the current situation. Some or all of the above processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can customize its display content using an AI model that takes the user's current status data as input and outputs the content to be displayed.

[0081] The display unit can estimate the user's emotions and determine the priority of the guide display based on the estimated user emotions. For example, if the user is nervous, the display unit will prioritize displaying important information. The display unit can also prioritize displaying detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the display unit can prioritize displaying concise information. For example, the display unit can analyze the user's facial expressions and prioritize displaying important information if the user is nervous. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the display unit can estimate the user's emotions using facial recognition technology. The display unit can also estimate the user's emotions using voice analysis technology. Furthermore, the display unit can estimate emotions using AI. For example, the display unit can estimate emotions using an AI model that takes facial data as input and outputs emotions. This allows for the priority of the guide display according to the user's emotions, thereby prioritizing the display of important information. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can determine the priority of the guide display using an AI model that takes user emotion data as input and outputs the priority of the guide display.

[0082] The display unit can select an appropriate display method based on the user's geographical location information when displaying information. For example, if the user is in a specific region, the display unit can select a display method related to that region. The display unit can also select a display method related to a specific facility if the user is in that facility. Furthermore, if the user is participating in a specific event, the display unit can select a display method related to that event. For example, the display unit can analyze the user's geographical location information in detail and select an appropriate display method. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the display unit can select a display method based on GPS data. The display unit can also select a display method based on location information services. Furthermore, the display unit can analyze geographical location information using AI. For example, the display unit can select a display method using an AI model that takes GPS data or location information services as input and outputs a display method. This allows for the selection of an appropriate display method by considering geographical location information. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can select a display method using an AI model that takes the user's geographic location data as input and outputs a display method.

[0083] The display unit can adjust the displayed content based on the user's social media activity when displaying a guide. For example, the display unit can adjust the displayed content based on information shared by the user on social media. It can also adjust the displayed content based on information the user has shown interest in on social media. Furthermore, the display unit can adjust the displayed content based on accounts the user follows on social media. For example, the display unit can analyze the user's social media activity in detail and adjust the displayed content. Social media activity includes, but is not limited to, posts and the number of likes. For example, the display unit can adjust the displayed content based on posts. It can also adjust the displayed content based on the number of likes. Furthermore, the display unit can analyze social media activity using AI. For example, the display unit can adjust the displayed content using an AI model that takes posts and the number of likes as input and outputs displayed content. This allows for the provision of user-relevant displayed content by analyzing social media activity. Some or all of the above-described processes in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can adjust the displayed content using an AI model that takes the user's social media activity data as input and outputs the content to be displayed.

[0084] The voice guidance unit can estimate the user's emotions and adjust the way it delivers the voice guidance based on those emotions. For example, if the user is nervous, the voice guidance unit will provide guidance in a calm voice. Conversely, if the user is relaxed, the voice guidance unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the voice guidance unit can provide quick and concise guidance. For example, the voice guidance unit can analyze the user's facial expressions and provide guidance in a calm voice if the user is nervous. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the voice guidance unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the voice guidance unit can estimate emotions using AI. For example, the voice guidance unit can estimate emotions using an AI model that takes facial expression data as input and outputs emotions. This allows for guidance that is easy for the user to understand by adjusting the way it delivers the voice guidance according to the user's emotions. Some or all of the above-described processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can adjust the way the voice guidance is expressed using an AI model that takes user emotion data as input and outputs a method for expressing the voice guidance.

[0085] The voice guidance unit can select an appropriate guidance method by referring to the user's past operation history when providing voice guidance. For example, the voice guidance unit can select the optimal guidance method based on guidance methods the user has used in the past. The voice guidance unit can also select the most efficient guidance method from the user's past operation history. Furthermore, the voice guidance unit can also select the optimal guidance method by analyzing the user's past operation history. For example, the voice guidance unit can analyze the user's past operation history in detail and select the optimal guidance method. Past operation history includes, but is not limited to, the frequency and type of operations. For example, the voice guidance unit can select a guidance method based on the frequency of operations. The voice guidance unit can also select a guidance method based on the type of operation. Furthermore, the voice guidance unit can also analyze past operation history using AI. For example, the voice guidance unit can select a guidance method using an AI model that takes the frequency and type of operations as input and outputs a guidance method. This allows the optimal guidance method to be selected by referring to past operation history. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or without using AI. For example, the voice guidance unit can select a guidance method using an AI model that takes the user's past operation history data as input and outputs guidance methods.

[0086] The voice guidance unit can customize the guidance content based on the user's current situation when providing voice guidance. For example, if the user is nervous, the voice guidance unit can provide guidance in a calm voice. Conversely, if the user is relaxed, the voice guidance unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the voice guidance unit can provide quick and concise guidance. For example, the voice guidance unit can analyze the user's facial expression and provide guidance in a calm voice if the user is nervous. Current situation includes, but is not limited to, the user's location and surrounding environment. For example, the voice guidance unit can customize the guidance content based on the user's location. It can also customize the guidance content based on the surrounding environment. Furthermore, the voice guidance unit can analyze the current situation using AI. For example, the voice guidance unit can customize the guidance content using an AI model that takes the user's location and surrounding environment as input and outputs guidance content. This allows for optimal guidance for the user by customizing the guidance content based on the current situation. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or without AI. For example, the voice guidance unit can customize the guidance content using an AI model that takes the user's current situation data as input and outputs guidance content.

[0087] The voice guidance unit can estimate the user's emotions and determine the priority of voice guidance based on the estimated emotions. For example, if the user is nervous, the voice guidance unit will prioritize providing important information. If the user is relaxed, the voice guidance unit can also prioritize providing detailed information. Furthermore, if the user is in a hurry, the voice guidance unit can prioritize providing concise information. For example, the voice guidance unit can analyze the user's facial expressions and prioritize important information if the user is nervous. Specific methods and criteria for estimating emotions include, but are not limited to, facial recognition and voice analysis. For example, the voice guidance unit can estimate the user's emotions using facial recognition technology. It can also estimate the user's emotions using voice analysis technology. Furthermore, the voice guidance unit can estimate emotions using AI. For example, the voice guidance unit can estimate emotions using an AI model that takes facial expression data as input and outputs emotions. This allows the system to prioritize voice guidance according to the user's emotions, thereby prioritizing important information. Some or all of the above-described processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can determine the priority of voice guidance using an AI model that takes user emotion data as input and outputs the priority of voice guidance.

[0088] The voice guidance unit can select an appropriate guidance method based on the user's geographical location information during voice guidance. For example, if the user is in a specific region, the voice guidance unit can select a guidance method related to that region. It can also select a guidance method related to a specific facility if the user is in that facility. Furthermore, if the user is participating in a specific event, the voice guidance unit can select a guidance method related to that event. For example, the voice guidance unit can analyze the user's geographical location information in detail and select an appropriate guidance method. Geographical location information includes, but is not limited to, GPS data and location information services. For example, the voice guidance unit can select a guidance method based on GPS data. It can also select a guidance method based on location information services. Furthermore, the voice guidance unit can analyze geographical location information using AI. For example, the voice guidance unit can select a guidance method using an AI model that takes GPS data and location information services as input and outputs a guidance method. This allows for the selection of an appropriate guidance method by considering geographical location information. Some or all of the above-described processing in the voice guidance unit may be performed using AI, for example, or without AI. For example, the voice guidance unit can select a guidance method using an AI model that takes the user's geographic location data as input and outputs a guidance method.

[0089] The voice guidance unit can adjust the guidance content based on the user's social media activity during voice guidance. For example, the voice guidance unit can adjust the guidance content based on information the user has shared on social media. It can also adjust the guidance content based on information the user has shown interest in on social media. Furthermore, the voice guidance unit can adjust the guidance content based on accounts the user follows on social media. For example, the voice guidance unit can analyze the user's social media activity in detail and adjust the guidance content. Social media activity includes, but is not limited to, posts and the number of likes. For example, the voice guidance unit can adjust the guidance content based on posts. It can also adjust the guidance content based on the number of likes. Furthermore, the voice guidance unit can analyze social media activity using AI. For example, the voice guidance unit can adjust the guidance content using an AI model that takes posts and the number of likes as input and outputs guidance content. This allows the voice guidance unit to provide guidance content relevant to the user by analyzing social media activity. Some or all of the above processing in the voice guidance unit may be performed using, for example, AI, or not using AI. For example, the voice guidance unit can adjust the guidance content using an AI model that takes the user's social media activity data as input and outputs guidance content.

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

[0091] The acquisition unit can acquire the user's biometric information and provide it to the discrimination unit. For example, the acquisition unit can acquire the user's heart rate and body temperature. It can also acquire the user's blood pressure. Furthermore, the acquisition unit can acquire the user's respiratory rate. Based on the acquired biometric information, the discrimination unit can estimate the user's health status and improve the accuracy of age discrimination. For example, the discrimination unit can estimate the user's health status based on heart rate and body temperature and adjust the accuracy of age discrimination. This makes it possible to perform age discrimination that takes the user's health status into consideration.

[0092] The discrimination unit can estimate the user's emotions and dynamically adjust the age determination algorithm based on those emotions. For example, if the user is nervous, the discrimination unit will analyze facial features in more detail. If the user is relaxed, it can determine their age based on the overall video. Furthermore, if the user is in a hurry, it can determine their age quickly. By dynamically adjusting the accuracy of age determination according to the user's emotions, more accurate age determination becomes possible.

[0093] The display unit can select the optimal display method by referring to the user's past operation history. For example, it can select the optimal display method based on the display methods the user has used in the past. Furthermore, the display unit can select the most efficient display method from the user's past operation history. In addition, the display unit can analyze the user's past operation history to select the optimal display method. This allows the optimal display method to be selected by referring to past operation history.

[0094] The voice guidance unit can estimate the user's emotions and adjust the tone and speed of the voice guidance based on those estimates. For example, if the user is nervous, the voice guidance unit will provide guidance in a calm tone. Conversely, if the user is relaxed, it can provide guidance in a cheerful tone. Furthermore, if the user is in a hurry, it can provide quick and concise voice guidance. By adjusting the tone and speed of the voice guidance according to the user's emotions, it becomes possible to provide guidance that is easy for the user to understand.

[0095] The voice guidance unit can select the optimal guidance method by referring to the user's past operation history. For example, it can select the optimal guidance method based on the guidance methods the user has used in the past. Furthermore, the voice guidance unit can select the most efficient guidance method from the user's past operation history. In addition, the voice guidance unit can analyze the user's past operation history to select the optimal guidance method. This allows the system to select the optimal guidance method by referring to past operation history.

[0096] The acquisition unit can estimate the user's emotions and adjust the timing of video acquisition based on the estimated emotions. For example, if the user is tense, video acquisition can be delayed until the user relaxes. Conversely, if the user is relaxed, video can be acquired immediately. Furthermore, if the user is in a hurry, video can be acquired quickly. In this way, by adjusting the timing of video acquisition according to the user's emotions, more appropriate video can be acquired.

[0097] The acquisition unit can analyze the user's past ATM usage history and select the optimal video acquisition method. For example, it can acquire video at times when the user frequently used ATMs in the past. The acquisition unit can also acquire video based on the location information of ATMs the user has used in the past. Furthermore, the acquisition unit can analyze the user's past usage patterns and select the optimal video acquisition method. This improves the accuracy of video acquisition by selecting the optimal video acquisition method based on past usage history.

[0098] The discrimination unit can estimate the user's emotions and adjust the order in which the age discrimination results are displayed based on the estimated emotions. For example, if the user is nervous, a concise result can be displayed first. If the user is relaxed, a more detailed result can be displayed. Furthermore, if the user is in a hurry, the results can be displayed quickly. By adjusting the display order of results according to the user's emotions, it becomes possible to display information in a way that is easy for the user to understand.

[0099] The display unit can select an appropriate display method based on the user's geographical location. For example, if the user is in a specific region, it can select a display method related to that region. Furthermore, if the user is in a specific facility, the display unit can select a display method related to that facility. In this way, by considering geographical location information, the display unit can select an appropriate display method.

[0100] The voice guidance unit can estimate the user's emotions and prioritize voice guidance based on those emotions. For example, if the user is nervous, it can prioritize important information. If the user is relaxed, it can prioritize detailed information. Furthermore, if the user is in a hurry, it can prioritize concise information. In this way, by prioritizing voice guidance according to the user's emotions, important information can be prioritized.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The acquisition unit acquires the user's video. The user's video includes, but is not limited to, still images, videos, and high resolution. The acquisition unit can acquire the user's video using, for example, a camera installed in an ATM. The acquisition unit can also acquire the user's video in real time. For example, the acquisition unit can acquire the user's facial features in detail using a high-resolution camera. Step 2: The discrimination unit analyzes the video acquired by the acquisition unit to determine the user's age. Specific methods and criteria for determining age include, but are not limited to, facial recognition technology, the number of wrinkles, and skin condition. For example, the discrimination unit can estimate the user's age using facial recognition technology. It can also determine age by analyzing the number of wrinkles and skin condition. Furthermore, the discrimination unit can determine age using AI. For example, the discrimination unit can determine age using an AI model that takes facial features as input and outputs age. Step 3: The display unit switches the ATM screen to a guide display when the detection unit determines that the user is above a certain age. The specific content and format of the guide display may include, but are not limited to, characters, animations, and text displays. For example, the display unit may display a friendly character to the user. The display unit may also display a real person to the user. Furthermore, the display unit may use animation to provide a guide display. For example, the display unit may display an animation in which a friendly character guides the user through the operating procedure. Step 4: The voice guidance unit provides voice guidance based on the guide display switched by the display unit. The specific content and method of voice guidance include, but are not limited to, the type of voice, the content of the guidance, and the tone of voice. For example, the voice guidance unit engages in interactive conversation with the user and provides voice guidance on the operating procedure. The voice guidance unit can also provide specific instructions by voice. Furthermore, the voice guidance unit can accept user feedback. For example, the voice guidance unit provides detailed instructions by voice, such as, "Next, please insert the card."

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

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

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

[0106] Each of the multiple elements described above, including the acquisition unit, discrimination unit, display unit, and voice guidance unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire the user's image using the camera 42 of the smart device 14. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the acquired image to determine the user's age. The display unit can display a guide using the display 40A of the smart device 14. The voice guidance unit can provide voice guidance using the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 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.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the acquisition unit, discrimination unit, display unit, and voice guidance unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire the user's image using the camera 42 of the smart glasses 214. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the acquired image to determine the user's age. The display unit can display a guide using the display of the smart glasses 214. The voice guidance unit can provide voice guidance using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the acquisition unit, discrimination unit, display unit, and voice guidance unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit can acquire the user's image using the camera 42 of the headset terminal 314. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the acquired image to determine the user's age. The display unit can display a guide using the display 343 of the headset terminal 314. The voice guidance unit can provide voice guidance using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the acquisition unit, discrimination unit, display unit, and voice guidance unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit can acquire images of the user using the camera 42 of the robot 414. The discrimination unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the acquired images to determine the user's age. The display unit can display information about the guide using the display of the robot 414. The voice guidance unit can provide voice guidance using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) An acquisition unit that acquires the user's video, A determination unit analyzes the video acquired by the acquisition unit and determines the user's age, A display unit that switches the ATM screen to an informational display when the aforementioned discrimination unit determines that the user is above a certain age, The system includes a voice guidance unit that provides voice guidance based on the guide display switched by the display unit. A system characterized by the following features. (Note 2) The aforementioned discrimination unit is Age is estimated based on facial features or the number of wrinkles. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is Display a character or person to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned voice guidance unit, It engages in interactive conversation with the user and provides voice guidance on operating procedures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned voice guidance unit, Detailed instructions such as "Next, please insert the card" are provided via voice. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned voice guidance unit, We accept user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of video acquisition based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, Analyze the user's past ATM usage history and select the appropriate video acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring video footage, filtering is performed based on the user's current environment or background information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, It estimates the user's emotions and determines the priority of the videos to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring video footage, the system prioritizes acquiring highly relevant footage based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring video footage, the system analyzes the user's social media activity and retrieves relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the accuracy of age determination based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned discrimination unit is When determining age, the age determination algorithm is improved based on the user's past video data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned discrimination unit is When determining age, improve the accuracy of the determination based on the user's lifestyle and health status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned discrimination unit is The system estimates the user's emotions and adjusts the order in which age determination results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned discrimination unit is Age determination is performed based on the user's geographical distribution. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned discrimination unit is When determining age, improve the accuracy of the determination based on the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is The system estimates the user's emotions and adjusts the guide's display method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displaying the guide, the system selects the appropriate display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying the guide, customize the displayed content based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is The system estimates the user's emotions and determines the priority of guide displays based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying guides, the system selects the appropriate display method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying the guide, the displayed content will be adjusted based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned voice guidance unit, The system estimates the user's emotions and adjusts the way voice guidance is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned voice guidance unit, When providing voice guidance, the system selects the appropriate guidance method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned voice guidance unit, When providing voice guidance, the guidance content is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned voice guidance unit, The system estimates the user's emotions and determines the priority of voice guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned voice guidance unit, When providing voice guidance, the system selects the appropriate guidance method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned voice guidance unit, When providing voice guidance, the guidance content will be adjusted based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires the user's video, A determination unit analyzes the video acquired by the acquisition unit and determines the user's age, A display unit that switches the ATM screen to an informational display when the aforementioned discrimination unit determines that the user is above a certain age, The system includes a voice guidance unit that provides voice guidance based on the guide display switched by the display unit. A system characterized by the following features.

2. The aforementioned discrimination unit is Age is estimated based on facial features or the number of wrinkles. The system according to feature 1.

3. The aforementioned display unit is Display a character or person to the user. The system according to feature 1.

4. The aforementioned voice guidance unit, It engages in interactive conversation with the user and provides voice guidance on operating procedures. The system according to feature 1.

5. The aforementioned voice guidance unit, We accept user feedback. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of video acquisition based on those emotions. The system according to feature 1.

7. The acquisition unit is, Analyze the user's past ATM usage history and select the appropriate video acquisition method. The system according to feature 1.

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