System

Abstract

To provide a system allowing an aged person to determine the purpose of a visitor to appropriately meet the requirements.SOLUTION: A system includes an AI response section, a homecoming promotion section, an authentication section, and an intermediation section. The AI response section determines the purpose of a visitor. The homecoming promotion section urges a visitor for the purpose of sale to return home on the basis of the purpose determined by the AI response section. The authentication section has a face authentication function. The intermediation section rapidly intermediates a visitor of a family authenticated by the authentication section.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult for elderly people to determine the purpose of a visitor and respond appropriately.

[0005] The system according to the embodiment aims to enable elderly people to determine the purpose of a visitor and respond appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes an AI response unit, a home promotion unit, an authentication unit, and an intermediation unit. The AI ​​response unit determines the purpose of the visitor. The home promotion unit encourages sales visitors to go home based on the purpose determined by the AI ​​response unit. The authentication unit includes a face recognition function. The intermediation unit promptly intermediates family visitors authenticated by the authentication unit. Effect of the Invention

[0007] The system according to the embodiment allows elderly people to determine the purpose of a visitor and respond appropriately. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The automatic voice response intercom system for the elderly according to the embodiment of the present invention is a system designed to allow the elderly to spend their time at home with peace of mind. This system automatically responds to visitors with voice, and encourages the visitor to go home if the AI ​​judges through conversation that the visitor is not necessary for sales purposes. In addition, it is equipped with a face recognition function, and can quickly transfer family visitors. This system allows the elderly to spend their time at home with peace of mind, and also gives peace of mind to children living far away. For example, when a visitor presses the intercom, an automatic voice response is started. If the AI ​​asks the visitor's purpose and judges that the visitor is for sales purposes, it politely encourages the visitor to go home. For example, it automatically issues a message such as "I'm sorry, we are not currently able to handle sales. Please go home." Next, the face recognition function allows the family visitor to be quickly transferred. If the camera built into the intercom recognizes the visitor's face and matches the face of a family member registered in advance, it automatically opens the door or notifies the elderly. This allows the elderly to receive visits from their family with peace of mind. Furthermore, this system also gives peace of mind to children living far away. For example, a child can check the intercom camera image in real time through a smartphone, and can respond remotely if necessary. This allows children to always be sure of the safety of the elderly and feel safe. In this way, the automatic voice response intercom system for the elderly is a system that allows elderly people to feel safe at home, and also provides peace of mind for children who live far away. In this way, the automatic voice response intercom system for the elderly is a system that allows elderly people to feel safe at home, and also provides peace of mind for children who live far away.

[0029] The automated voice response intercom system for elderly people according to the embodiment includes an AI response unit, a homeward promotion unit, an authentication unit, and an intermediary unit. The AI ​​response unit determines the purpose of the visitor. The purpose of the visitor includes, but is not limited to, sales, visiting, delivery, and the like. For example, the AI ​​response unit asks the visitor, "What is your business?" and analyzes the visitor's answer. The AI ​​response unit can use a machine learning algorithm to determine whether the visitor's answer is for sales. For example, the AI ​​response unit analyzes keywords and phrases included in the visitor's answer and determines whether the visitor's answer is for sales. The homeward promotion unit encourages the visitor who is for sales to go home based on the purpose determined by the AI ​​response unit. The homeward promotion unit can automatically issue a voice message such as, for example, "Sorry, we are not currently accepting sales. Please go home." The homeward promotion unit can also encourage the visitor to go home using a display message. For example, the intercom display displays, "We are not currently accepting sales. Please go home." The authentication unit has a face recognition function and recognizes the face of the visitor. The authentication unit, for example, photographs the face of a visitor using a camera built into the intercom and determines whether it matches the face of a family member registered in advance. The authentication unit has security measures for protecting face authentication data and preventing unauthorized access. For example, the authentication unit encrypts and stores the face authentication data, and performs access control. The relay unit promptly relays a visitor of the family member authenticated by the authentication unit. For example, when a visitor of the family member comes, the relay unit can automatically open the door or notify the elderly person. The relay unit has a function of checking the camera image in real time through a smartphone. For example, a child can check the camera image of the intercom in real time using a smartphone, and can respond remotely as necessary. As a result, the automatic voice response intercom system for the elderly according to the embodiment allows the elderly to spend their time at home with peace of mind, and also provides peace of mind for children living away from the elderly.

[0030] The AI ​​response unit determines the purpose of the visitor. The purpose of the visitor includes, but is not limited to, sales, visiting, delivery, and the like. For example, the AI ​​response unit asks the visitor, "What is your business?" and analyzes the visitor's response. The AI ​​response unit can use a machine learning algorithm to determine whether the visitor's purpose is sales based on the visitor's response. For example, the AI ​​response unit analyzes keywords and phrases contained in the visitor's response to determine whether the visitor's purpose is sales. Specifically, the AI ​​response unit uses natural language processing technology to convert the visitor's remarks into text data and analyzes the text data. For example, if the response contains the phrase "I would like to introduce a new product," it is determined that the visitor's purpose is sales. The AI ​​response unit can also use voice recognition technology to analyze the tone of the visitor's voice and emotions to complementary determine whether the visitor's purpose is sales. Furthermore, the AI ​​response unit learns past data and continuously improves the model to determine the visitor's purpose with higher accuracy. For example, by learning the remarks of visitors who have visited for sales purposes in the past and comparing them with the remarks of new visitors, it is possible to more accurately determine whether the visitor's purpose is sales. In this way, the AI ​​response department can reduce the burden on elderly people of having to respond to unnecessary sales calls, providing an environment where they can feel safe and at home.

[0031] The home promotion unit encourages the visitor who is visiting for sales purposes to go home based on the purpose determined by the AI ​​response unit. The home promotion unit can automatically issue a voice message such as, for example, "I'm sorry, but we are not currently accepting sales. Please go home." The home promotion unit can also encourage the visitor to go home using a display message. For example, the display of the intercom can display "We are not currently accepting sales. Please go home." Furthermore, the home promotion unit can monitor the reaction of the visitor and issue an additional message as necessary. For example, if the visitor does not go home, a reminder message such as "I'll say it again, we are not currently accepting sales. Please go home." can be issued. The home promotion unit can use voice synthesis technology to issue a message in a natural voice, so that it can effectively encourage the visitor to go home. The home promotion unit can also visually emphasize the message by adjusting the font size and color of the message displayed on the intercom display to encourage the visitor to go home. In this way, the home promotion unit reduces the burden on the elderly of responding to unnecessary sales visits and provides an environment where they can spend their time at home with peace of mind.

[0032] The authentication unit has a face recognition function and recognizes the face of a visitor. For example, the authentication unit takes a picture of the visitor's face using a camera built into the intercom and judges whether the face matches the face of a family member registered in advance. The authentication unit has security measures for protecting the face recognition data and preventing unauthorized access. For example, the authentication unit encrypts and stores the face recognition data and performs access control. Specifically, the authentication unit uses a face recognition algorithm to extract the facial feature points of the visitor and compares it with the facial data of the family registered in advance. The facial feature points include the position and shape of the eyes, nose, and mouth, and the facial contour. The authentication unit analyzes these feature points with high accuracy and calculates the degree of match to judge whether the visitor is a family member. In addition, the authentication unit encrypts data and controls access to protect the face recognition data. For example, the face recognition data is encrypted using a powerful encryption algorithm such as AES (Advanced Encryption Standard), and access to the authentication unit is controlled using multi-factor authentication (MFA) to prevent unauthorized access. This enables the authentication unit to quickly and accurately perform facial authentication of visitors while maintaining a high level of security.

[0033] The relay unit promptly relays a visitor of the family authenticated by the authentication unit. For example, when a visitor of the family comes, the relay unit can automatically open the door or notify the elderly person. The relay unit has a function of checking the camera image in real time through a smartphone. For example, a child can check the camera image of the intercom in real time using a smartphone and respond remotely as necessary. Specifically, when a visitor authenticated as a family member by the authentication unit stands in front of the intercom, the relay unit automatically sends a notification to the elderly person's smartphone or tablet. When the elderly person receives the notification, the elderly person can check the camera image of the intercom, confirm that the visitor is a family member, and then remotely open the door through an app on the smartphone or tablet. In addition, even if the family member is in a remote location, the relay unit can check the camera image of the intercom through a smartphone and contact the elderly person as necessary. For example, when a child checks the camera image of the intercom from a remote location and confirms that the visitor is a family member, the child can contact the elderly person by phone or message and instruct him or her to open the door. This allows the referral department to allow elderly people to accept visits from their families with peace of mind, and also provides peace of mind for family members who live far away.

[0034] The AI ​​response unit can be equipped with an algorithm for determining whether the visitor has a sales purpose. The AI ​​response unit can determine whether the visitor has a sales purpose by using, for example, a machine learning algorithm. For example, the AI ​​response unit can analyze keywords and phrases included in the visitor's answer and determine whether the visitor has a sales purpose. The AI ​​response unit can also determine whether the visitor has a sales purpose by using a rule-based algorithm. For example, the AI ​​response unit can determine whether the visitor has a sales purpose when a specific keyword or phrase is included. This makes it possible to more accurately determine whether the visitor has a sales purpose. Some or all of the above-mentioned processing in the AI ​​response unit can be performed, for example, using AI or can be performed without using AI. For example, the AI ​​response unit can input the visitor's answer into the generation AI and have the generation AI execute the judgment of whether the visitor has a sales purpose.

[0035] The authentication unit can be equipped with security measures for protecting the facial recognition data and preventing unauthorized access. The authentication unit, for example, encrypts and stores the facial recognition data. For example, the authentication unit encrypts the facial recognition data using an encryption technology such as AES (Advanced Encryption Standard). The authentication unit can also perform access control. For example, the authentication unit limits access to the facial recognition data to specific users to prevent unauthorized access. This improves the security of the facial recognition data. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can cause the generation AI to perform encryption and access control of the facial recognition data.

[0036] The intermediary unit may include a means for checking the camera image in real time through a smartphone. The intermediary unit may provide, for example, an application for a smartphone, and may check the camera image in real time. For example, the intermediary unit may stream the camera image to the smartphone via the Internet. The intermediary unit may also check the camera image in real time using a communication protocol. For example, the intermediary unit may stream the camera image using RTSP (Real-Time Streaming Protocol). This allows a child living far away to check the safety of an elderly person. A part or all of the above-mentioned processing in the intermediary unit may be performed, for example, using AI, or may be performed without using AI. For example, the intermediary unit may cause a generation AI to execute streaming of the camera image.

[0037] The AI ​​response unit can analyze past response history and select an appropriate response method. For example, the AI ​​response unit selects an optimal response in a similar situation based on a response pattern that was successful in the past. For example, the AI ​​response unit stores past response history in a database and selects an optimal response method when a similar situation occurs. The AI ​​response unit can also analyze past failure examples and improve the response method so as not to repeat the same mistake. For example, the AI ​​response unit analyzes past failure examples, identifies the cause of the failure, and improves the response method. Furthermore, the AI ​​response unit can also select an optimal response method for a specific visitor from the past response history. For example, the AI ​​response unit analyzes the response history of a specific visitor and selects an optimal response method for that visitor. This improves the accuracy of the response based on past data. Part or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input past response history into the generation AI and cause the generation AI to select the optimal response method.

[0038] The AI ​​response unit can analyze the tone of the visitor's voice or the way of speaking to more accurately determine whether the visitor is for sales purposes. For example, if the visitor's voice tone is high and he / she speaks quickly, the AI ​​response unit determines that the visitor is likely to be for sales purposes. For example, the AI ​​response unit uses voice analysis technology to analyze the tone of the visitor's voice and the way of speaking to determine whether the visitor is for sales purposes. In addition, if the visitor's way of speaking is polite, the AI ​​response unit can also determine that the visitor is likely not for sales purposes. Furthermore, if the visitor's tone of voice changes, the AI ​​response unit can reevaluate the possibility that the visitor is for sales purposes. This makes it possible to more accurately determine whether the visitor is for sales purposes. Some or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input the visitor's voice data into the generation AI and have the generation AI execute the judgment of whether the visitor is for sales purposes.

[0039] The AI ​​response unit can respond based on a sales method specific to the region based on the geographical location information of the visitor. For example, if the visitor comes from a specific region, the AI ​​response unit responds to a sales method specific to the region. For example, the AI ​​response unit acquires the geographical location information of the visitor and responds based on a sales method specific to the region. The AI ​​response unit can also respond in consideration of the culture and customs specific to the region based on the geographical location information of the visitor. For example, the AI ​​response unit uses language and expressions specific to the region based on the geographical location information of the visitor. This makes it possible to respond to sales methods specific to the region. A part or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute a response based on a sales method specific to the region.

[0040] The AI ​​response unit can analyze the social media activity of the visitor and determine whether the visit is for sales purposes. For example, if there are many sales-related posts from the visitor's social media activity, the AI ​​response unit determines that the visit is for sales purposes. For example, the AI ​​response unit analyzes the visitor's social media account and determines whether there are many sales-related posts. In addition, the AI ​​response unit can also determine that the visit is for sales purposes if there are many posts about a specific product or service from the visitor's social media activity. Furthermore, the AI ​​response unit can check the visitor's past visit history from the visitor's social media activity and determine whether the visit is for sales purposes. This makes it possible to determine whether the visit is for sales purposes based on social media activities. Some or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input the visitor's social media data into the generation AI and have the generation AI execute the judgment of whether the visit is for sales purposes.

[0041] The home promotion unit can analyze the past home promotion history and select an appropriate home promotion method. The home promotion unit, for example, selects an optimal method in a similar situation based on a home promotion method that was successful in the past. For example, the home promotion unit stores the past home promotion history in a database and selects an optimal method when a similar situation occurs. The home promotion unit can also analyze past failures and improve the home promotion method so as not to repeat the same mistake. For example, the home promotion unit analyzes past failures, identifies the cause of the failure, and improves the home promotion method. Furthermore, the home promotion unit can select an optimal method for a specific visitor from the past home promotion history. For example, the home promotion unit analyzes the home promotion history of a specific visitor and selects an optimal method for that visitor. This improves the accuracy of home promotion based on past data. A part or all of the above-mentioned processing in the home promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the home promotion unit can input the past home promotion history to the generation AI and cause the generation AI to select the optimal home promotion method.

[0042] The home-going promotion unit can apply a home-going promotion method specific to a region based on the geographical location information of the visitor. For example, if the visitor comes from a specific region, the home-going promotion unit applies a home-going promotion method specific to that region. For example, the home-going promotion unit acquires the geographical location information of the visitor and applies a home-going promotion method that takes into account the culture and customs specific to that region. The home-going promotion unit can also use language and expressions specific to the region based on the geographical location information of the visitor. This makes it possible to respond to the home-going promotion method specific to the region. A part or all of the above-mentioned processing in the home-going promotion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the home-going promotion unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute the home-going promotion method specific to the region.

[0043] The home-going promotion unit can analyze the social media activity of the visitor and customize a message encouraging the visitor to go home. For example, if the visitor's social media activity shows many sales-related posts, the home-going promotion unit determines that the visitor has a sales purpose and customizes a message encouraging the visitor to go home. For example, the home-going promotion unit analyzes the visitor's social media account, determines whether there are many sales-related posts, and customizes the message based on the result. In addition, the home-going promotion unit can also determine that the visitor has a sales purpose and customize a message encouraging the visitor to go home if there are many posts about a specific product or service based on the visitor's social media activity. Furthermore, the home-going promotion unit can check the visitor's past visit history from the visitor's social media activity, determine whether the visitor has a sales purpose, and customize the message based on the result. This allows the home-going promotion message to be customized based on the social media activity. A part or all of the above-mentioned processing in the home-going promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the home-going promotion unit can input the visitor's social media data into the generation AI and cause the generation AI to customize the home-going promotion message.

[0044] The authentication unit can analyze past authentication history and select an appropriate authentication method. For example, the authentication unit selects the optimal method in a similar situation based on a past successful authentication method. For example, the authentication unit stores past authentication history in a database and selects the optimal method when a similar situation occurs. The authentication unit can also analyze past failure cases and improve the authentication method so as not to repeat the same mistake. For example, the authentication unit analyzes past failure cases, identifies the cause of failure, and improves the authentication method. Furthermore, the authentication unit can select the optimal method for a specific visitor from the past authentication history. For example, the authentication unit analyzes the authentication history of a specific visitor and selects the optimal method for that visitor. This improves the accuracy of authentication based on past data. Part or all of the above-mentioned processing in the authentication unit may be performed, for example, using AI, or may be performed without using AI. For example, the authentication unit can input past authentication history to the generation AI and cause the generation AI to select the optimal authentication method.

[0045] The authentication unit can analyze the minute features of the visitor's face during authentication to improve the authentication accuracy. The authentication unit, for example, analyzes the minute features of the visitor's face to improve the authentication accuracy. For example, the authentication unit uses a face recognition algorithm to analyze the minute features of the visitor's face to improve the authentication accuracy. The authentication unit can also improve the authentication accuracy by comparing the minute features of the visitor's face with past authentication data based on the minute features of the visitor's face. Furthermore, the authentication unit can also analyze the minute features of the visitor's face in real time to improve the authentication accuracy. As a result, the authentication accuracy is improved by analyzing the minute features of the face. A part or all of the above-mentioned processing in the authentication unit may be performed, for example, using AI or may be performed without using AI. For example, the authentication unit can input the minute feature data of the visitor's face to the generation AI and cause the generation AI to execute the improvement of the authentication accuracy.

[0046] The authentication unit can apply an authentication process specific to a region based on the geographical location information of the visitor at the time of authentication. For example, if the visitor comes from a specific region, the authentication unit applies an authentication process specific to the region. For example, the authentication unit acquires the geographical location information of the visitor and applies an authentication process that takes into account the culture and customs specific to the region. The authentication unit can also use local language and expressions based on the geographical location information of the visitor. This makes it possible to accommodate a local authentication process. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute a local authentication process.

[0047] The authentication unit can analyze the social media activity of the visitor at the time of authentication and improve the authentication accuracy. The authentication unit can, for example, check the past visit history from the visitor's social media activity and improve the authentication accuracy. For example, the authentication unit can analyze the visitor's social media account and check the past visit history. The authentication unit can also check posts related to specific products and services from the visitor's social media activity and improve the authentication accuracy. Furthermore, the authentication unit can improve the authentication accuracy when there are many sales-related posts from the visitor's social media activity. This improves the authentication accuracy based on the social media activity. A part or all of the above-mentioned processing in the authentication unit can be performed, for example, using AI or can be performed without using AI. For example, the authentication unit can input the visitor's social media data to the generation AI and cause the generation AI to execute the improvement of the authentication accuracy.

[0048] The mediation unit can analyze past mediation history and select an appropriate mediation method. The mediation unit, for example, selects the optimal method in a similar situation based on a mediation method that was successful in the past. For example, the mediation unit stores past mediation history in a database and selects the optimal method when a similar situation occurs. The mediation unit can also analyze past failure cases and improve the mediation method so as not to repeat the same mistake. For example, the mediation unit analyzes past failure cases, identifies the cause of the failure, and improves the mediation method. Furthermore, the mediation unit can also select the optimal method for a specific visitor from the past mediation history. For example, the mediation unit analyzes the mediation history of a specific visitor and selects the optimal method for that visitor. This improves the accuracy of mediation based on past data. Some or all of the above-mentioned processing in the mediation unit may be performed, for example, using AI, or may be performed without using AI. For example, the referral unit can input past referral history into the generation AI and have the generation AI select the optimal referral method.

[0049] The intermediation unit can analyze the visitor's tone of voice or speech at the time of intermediation to improve the accuracy of the intermediation. For example, if the visitor's voice tone is high and he / she speaks quickly, the intermediation unit pays attention to improve the accuracy of the intermediation. For example, the intermediation unit can analyze the visitor's tone of voice and speech using voice analysis technology to improve the accuracy of the intermediation. In addition, if the visitor's speech is polite, the intermediation unit can proceed as is to improve the accuracy of the intermediation. Furthermore, if the visitor's tone of voice changes, the intermediation unit can reevaluate to improve the accuracy of the intermediation. In this way, the accuracy of the intermediation is improved by analyzing the tone of voice and speech. Part or all of the above-mentioned processing in the intermediation unit may be performed, for example, using AI or may be performed without using AI. For example, the intermediation unit can input the visitor's voice data into the generation AI and cause the generation AI to execute the improvement of the accuracy of the intermediation.

[0050] The intermediation unit can apply an intermediation process specific to a region based on the geographical location information of the visitor at the time of intermediation. For example, if the visitor comes from a specific region, the intermediation unit applies an intermediation process specific to the region. For example, the intermediation unit acquires the geographical location information of the visitor and applies an intermediation process that takes into account the culture and customs specific to the region. The intermediation unit can also use local language and expressions based on the geographical location information of the visitor. This makes it possible to accommodate an intermediation process specific to the region. Some or all of the above-mentioned processing in the intermediation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the intermediation unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute an intermediation process specific to the region.

[0051] The mediation unit can analyze the visitor's social media activity at the time of mediation and improve the accuracy of the mediation. The mediation unit, for example, checks the visitor's past visit history from the visitor's social media activity and improves the accuracy of the mediation. For example, the mediation unit analyzes the visitor's social media account and checks the visitor's past visit history. The mediation unit can also check posts about specific products and services from the visitor's social media activity and improve the accuracy of the mediation. Furthermore, the mediation unit can improve the accuracy of the mediation when there are many sales-related posts from the visitor's social media activity. This improves the accuracy of the mediation based on the social media activity. A part or all of the above-mentioned processing in the mediation unit may be performed, for example, using AI or may be performed without using AI. For example, the mediation unit can input the visitor's social media data into the generation AI and cause the generation AI to execute the improvement of the accuracy of the mediation.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The automatic voice response intercom system for the elderly can further include a health monitoring unit. The health monitoring unit can monitor the elderly's health condition in real time and notify family members and medical institutions if an abnormality is detected. For example, the health monitoring unit can periodically measure vital signs such as heart rate, blood pressure, and body temperature, and issue an alert if an abnormal value is detected. The health monitoring unit also has a fall detection function and can automatically notify if the elderly falls. Furthermore, the health monitoring unit can also record the amount of daily activity and provide data for health management. This makes it possible to constantly grasp the elderly's health condition and respond quickly.

[0054] The automatic voice response intercom system for the elderly can further include a voice recognition unit. The voice recognition unit can recognize the voice commands of the elderly and assist in operating the system. For example, the voice recognition unit can automatically open the door when the elderly says, "Open the door." Also, the voice recognition unit can call a pre-registered family member when the elderly says, "Contact my family." Furthermore, the voice recognition unit can notify an emergency contact when the elderly says, "This is an emergency." This allows the elderly to easily operate the system using voice commands, improving convenience.

[0055] The automatic voice response intercom system for the elderly can further include an environmental sensor unit. The environmental sensor unit can monitor the temperature, humidity, air quality, etc., of the room, and provide information for maintaining a comfortable environment. For example, the environmental sensor unit can automatically adjust the air conditioner if the temperature in the room is too high. It can also operate a humidifier if the humidity is too low. Furthermore, it can send a notification to encourage ventilation if the air quality deteriorates. This allows the elderly to live in a comfortable environment.

[0056] The automatic voice response intercom system for the elderly can further include a reminder unit. The reminder unit can provide a reminder function to support the daily life of the elderly. For example, the reminder unit can notify the time to take medicine. The reminder unit can also notify of regular health checks and medical appointments. Furthermore, the reminder unit can notify of birthdays and anniversaries of family members. This allows the elderly to live their daily lives smoothly without forgetting important plans.

[0057] The automatic voice response intercom system for the elderly can further include an entertainment unit. The entertainment unit can provide content for the elderly to enjoy. For example, the entertainment unit can play music or radio. The entertainment unit can also provide news or weather forecasts. The entertainment unit can also provide simple games or puzzles. This allows the elderly to spend a fun time at home.

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The AI ​​response unit determines the visitor's purpose. For example, it asks the visitor, "What do you want?" and analyzes the visitor's response. Using a machine learning algorithm, the AI ​​response unit can determine from the visitor's response whether the visitor's purpose is to make a sale. It analyzes keywords and phrases contained in the visitor's response to determine whether the purpose is to make a sale. Step 2: The homeward promotion unit encourages sales visitors to go home based on the purpose determined by the AI ​​response unit. For example, it can automatically issue a voice message such as, "Sorry, but we are not currently accepting sales. Please go home." It can also display a message on the intercom display saying, "We are not currently accepting sales. Please go home." Step 3: The authentication unit is equipped with a facial recognition function and recognizes the visitor's face. For example, the visitor's face is photographed using a camera built into the intercom and a determination is made as to whether it matches the face of a pre-registered family member. The authentication unit is equipped with security measures to protect the facial recognition data and prevent unauthorized access. For example, the facial recognition data is encrypted and stored, and access is controlled. Step 4: The relay unit promptly relays visitors to the family who have been authenticated by the authentication unit. For example, when a visitor arrives, it can automatically open the door or notify the elderly person. The relay unit has a function to check camera footage in real time via a smartphone. For example, a child can use a smartphone to check the intercom camera footage in real time and respond remotely if necessary.

[0060] (Example 2) The automatic voice response intercom system for the elderly according to the embodiment of the present invention is a system designed to allow the elderly to spend their time at home with peace of mind. This system automatically responds to visitors with voice, and encourages the visitor to go home if the AI ​​judges through conversation that the visitor is not necessary for sales purposes. In addition, it is equipped with a face recognition function, and can quickly transfer family visitors. This system allows the elderly to spend their time at home with peace of mind, and also gives peace of mind to children living far away. For example, when a visitor presses the intercom, an automatic voice response is started. If the AI ​​asks the visitor's purpose and judges that the visitor is for sales purposes, it politely encourages the visitor to go home. For example, it automatically issues a message such as "I'm sorry, we are not currently able to handle sales. Please go home." Next, the face recognition function allows the family visitor to be quickly transferred. If the camera built into the intercom recognizes the visitor's face and matches the face of a family member registered in advance, it automatically opens the door or notifies the elderly. This allows the elderly to receive visits from their family with peace of mind. Furthermore, this system also gives peace of mind to children living far away. For example, a child can check the intercom camera image in real time through a smartphone, and can respond remotely if necessary. This allows children to always be sure of the safety of the elderly and feel safe. In this way, the automatic voice response intercom system for the elderly is a system that allows elderly people to feel safe at home, and also provides peace of mind for children who live far away. In this way, the automatic voice response intercom system for the elderly is a system that allows elderly people to feel safe at home, and also provides peace of mind for children who live far away.

[0061] The automated voice response intercom system for elderly people according to the embodiment includes an AI response unit, a homeward promotion unit, an authentication unit, and an intermediary unit. The AI ​​response unit determines the purpose of the visitor. The purpose of the visitor includes, but is not limited to, sales, visiting, delivery, and the like. For example, the AI ​​response unit asks the visitor, "What is your business?" and analyzes the visitor's answer. The AI ​​response unit can use a machine learning algorithm to determine whether the visitor's answer is for sales. For example, the AI ​​response unit analyzes keywords and phrases included in the visitor's answer and determines whether the visitor's answer is for sales. The homeward promotion unit encourages the visitor who is for sales to go home based on the purpose determined by the AI ​​response unit. The homeward promotion unit can automatically issue a voice message such as, for example, "Sorry, we are not currently accepting sales. Please go home." The homeward promotion unit can also encourage the visitor to go home using a display message. For example, the intercom display displays, "We are not currently accepting sales. Please go home." The authentication unit has a face recognition function and recognizes the face of the visitor. The authentication unit, for example, photographs the face of a visitor using a camera built into the intercom and determines whether it matches the face of a family member registered in advance. The authentication unit has security measures for protecting face authentication data and preventing unauthorized access. For example, the authentication unit encrypts and stores the face authentication data, and performs access control. The relay unit promptly relays a visitor of the family member authenticated by the authentication unit. For example, when a visitor of the family member comes, the relay unit can automatically open the door or notify the elderly person. The relay unit has a function of checking the camera image in real time through a smartphone. For example, a child can check the camera image of the intercom in real time using a smartphone, and can respond remotely as necessary. As a result, the automatic voice response intercom system for the elderly according to the embodiment allows the elderly to spend their time at home with peace of mind, and also provides peace of mind for children living away from the elderly.

[0062] The AI ​​response unit determines the purpose of the visitor. The purpose of the visitor includes, but is not limited to, sales, visiting, delivery, and the like. For example, the AI ​​response unit asks the visitor, "What is your business?" and analyzes the visitor's response. The AI ​​response unit can use a machine learning algorithm to determine whether the visitor's purpose is sales based on the visitor's response. For example, the AI ​​response unit analyzes keywords and phrases contained in the visitor's response to determine whether the visitor's purpose is sales. Specifically, the AI ​​response unit uses natural language processing technology to convert the visitor's remarks into text data and analyzes the text data. For example, if the response contains the phrase "I would like to introduce a new product," it is determined that the visitor's purpose is sales. The AI ​​response unit can also use voice recognition technology to analyze the tone of the visitor's voice and emotions to complementary determine whether the visitor's purpose is sales. Furthermore, the AI ​​response unit learns past data and continuously improves the model to determine the visitor's purpose with higher accuracy. For example, by learning the remarks of visitors who have visited for sales purposes in the past and comparing them with the remarks of new visitors, it is possible to more accurately determine whether the visitor's purpose is sales. In this way, the AI ​​response department can reduce the burden on elderly people of having to respond to unnecessary sales calls, providing an environment where they can feel safe and at home.

[0063] The home promotion unit encourages the visitor who is visiting for sales purposes to go home based on the purpose determined by the AI ​​response unit. The home promotion unit can automatically issue a voice message such as, for example, "I'm sorry, but we are not currently accepting sales. Please go home." The home promotion unit can also encourage the visitor to go home using a display message. For example, the display of the intercom can display "We are not currently accepting sales. Please go home." Furthermore, the home promotion unit can monitor the reaction of the visitor and issue an additional message as necessary. For example, if the visitor does not go home, a reminder message such as "I'll say it again, we are not currently accepting sales. Please go home." can be issued. The home promotion unit can use voice synthesis technology to issue a message in a natural voice, so that it can effectively encourage the visitor to go home. The home promotion unit can also visually emphasize the message by adjusting the font size and color of the message displayed on the intercom display to encourage the visitor to go home. In this way, the home promotion unit reduces the burden on the elderly of responding to unnecessary sales visits and provides an environment where they can spend their time at home with peace of mind.

[0064] The authentication unit has a face recognition function and recognizes the face of a visitor. For example, the authentication unit takes a picture of the visitor's face using a camera built into the intercom and judges whether the face matches the face of a family member registered in advance. The authentication unit has security measures for protecting the face recognition data and preventing unauthorized access. For example, the authentication unit encrypts and stores the face recognition data and performs access control. Specifically, the authentication unit uses a face recognition algorithm to extract the facial feature points of the visitor and compares it with the facial data of the family registered in advance. The facial feature points include the position and shape of the eyes, nose, and mouth, and the facial contour. The authentication unit analyzes these feature points with high accuracy and calculates the degree of match to judge whether the visitor is a family member. In addition, the authentication unit encrypts data and controls access to protect the face recognition data. For example, the face recognition data is encrypted using a powerful encryption algorithm such as AES (Advanced Encryption Standard), and access to the authentication unit is controlled using multi-factor authentication (MFA) to prevent unauthorized access. This enables the authentication unit to quickly and accurately perform facial authentication of visitors while maintaining a high level of security.

[0065] The relay unit promptly relays a visitor of the family authenticated by the authentication unit. For example, when a visitor of the family comes, the relay unit can automatically open the door or notify the elderly person. The relay unit has a function of checking the camera image in real time through a smartphone. For example, a child can check the camera image of the intercom in real time using a smartphone and respond remotely as necessary. Specifically, when a visitor authenticated as a family member by the authentication unit stands in front of the intercom, the relay unit automatically sends a notification to the elderly person's smartphone or tablet. When the elderly person receives the notification, the elderly person can check the camera image of the intercom, confirm that the visitor is a family member, and then remotely open the door through an app on the smartphone or tablet. In addition, even if the family member is in a remote location, the relay unit can check the camera image of the intercom through a smartphone and contact the elderly person as necessary. For example, when a child checks the camera image of the intercom from a remote location and confirms that the visitor is a family member, the child can contact the elderly person by phone or message and instruct him or her to open the door. This allows the referral department to allow elderly people to accept visits from their families with peace of mind, and also provides peace of mind for family members who live far away.

[0066] The AI ​​response unit can be equipped with an algorithm for determining whether the visitor has a sales purpose. The AI ​​response unit can determine whether the visitor has a sales purpose by using, for example, a machine learning algorithm. For example, the AI ​​response unit can analyze keywords and phrases included in the visitor's answer and determine whether the visitor has a sales purpose. The AI ​​response unit can also determine whether the visitor has a sales purpose by using a rule-based algorithm. For example, the AI ​​response unit can determine whether the visitor has a sales purpose when a specific keyword or phrase is included. This makes it possible to more accurately determine whether the visitor has a sales purpose. Some or all of the above-mentioned processing in the AI ​​response unit can be performed, for example, using AI or can be performed without using AI. For example, the AI ​​response unit can input the visitor's answer into the generation AI and have the generation AI execute the judgment of whether the visitor has a sales purpose.

[0067] The authentication unit can be equipped with security measures for protecting the facial recognition data and preventing unauthorized access. The authentication unit, for example, encrypts and stores the facial recognition data. For example, the authentication unit encrypts the facial recognition data using an encryption technology such as AES (Advanced Encryption Standard). The authentication unit can also perform access control. For example, the authentication unit limits access to the facial recognition data to specific users to prevent unauthorized access. This improves the security of the facial recognition data. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can cause the generation AI to perform encryption and access control of the facial recognition data.

[0068] The intermediary unit may include a means for checking the camera image in real time through a smartphone. The intermediary unit may provide, for example, an application for a smartphone, and may check the camera image in real time. For example, the intermediary unit may stream the camera image to the smartphone via the Internet. The intermediary unit may also check the camera image in real time using a communication protocol. For example, the intermediary unit may stream the camera image using RTSP (Real-Time Streaming Protocol). This allows a child living far away to check the safety of an elderly person. A part or all of the above-mentioned processing in the intermediary unit may be performed, for example, using AI, or may be performed without using AI. For example, the intermediary unit may cause a generation AI to execute streaming of the camera image.

[0069] The AI ​​response unit can estimate the visitor's emotions and adjust the response content based on the estimated visitor's emotions. For example, the AI ​​response unit analyzes the visitor's facial expression to estimate the emotion. For example, the AI ​​response unit analyzes the visitor's facial expression captured by a camera and estimates emotions such as anger, anxiety, and relaxation. The AI ​​response unit can also analyze the visitor's voice to estimate the emotion. For example, the AI ​​response unit analyzes the tone and speed of the visitor's voice to estimate the emotion. The AI ​​response unit adjusts the response content based on the estimated emotion. For example, if the visitor is angry, the AI ​​response unit responds calmly and politely to calm the situation. Also, if the visitor feels anxious, the AI ​​response unit responds in a way that gives a sense of security. Furthermore, if the visitor is relaxed, the AI ​​response unit responds in a friendly manner to smoothly advance the conversation. This makes it possible to provide an appropriate response according to the visitor's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the AI ​​response unit may input facial expression data of a visitor to the generation AI and cause the generation AI to estimate emotions.

[0070] The AI ​​response unit can analyze past response history and select an appropriate response method. For example, the AI ​​response unit selects an optimal response in a similar situation based on a response pattern that was successful in the past. For example, the AI ​​response unit stores past response history in a database and selects an optimal response method when a similar situation occurs. The AI ​​response unit can also analyze past failure examples and improve the response method so as not to repeat the same mistake. For example, the AI ​​response unit analyzes past failure examples, identifies the cause of the failure, and improves the response method. Furthermore, the AI ​​response unit can also select an optimal response method for a specific visitor from the past response history. For example, the AI ​​response unit analyzes the response history of a specific visitor and selects an optimal response method for that visitor. This improves the accuracy of the response based on past data. Part or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input past response history into the generation AI and cause the generation AI to select the optimal response method.

[0071] The AI ​​response unit can analyze the tone of the visitor's voice or the way of speaking to more accurately determine whether the visitor is for sales purposes. For example, if the visitor's voice tone is high and he / she speaks quickly, the AI ​​response unit determines that the visitor is likely to be for sales purposes. For example, the AI ​​response unit uses voice analysis technology to analyze the tone of the visitor's voice and the way of speaking to determine whether the visitor is for sales purposes. In addition, if the visitor's way of speaking is polite, the AI ​​response unit can also determine that the visitor is likely not for sales purposes. Furthermore, if the visitor's tone of voice changes, the AI ​​response unit can reevaluate the possibility that the visitor is for sales purposes. This makes it possible to more accurately determine whether the visitor is for sales purposes. Some or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input the visitor's voice data into the generation AI and have the generation AI execute the judgment of whether the visitor is for sales purposes.

[0072] The AI ​​response unit can estimate the emotion of the visitor and determine the priority of the response based on the estimated emotion of the visitor. For example, the AI ​​response unit analyzes the facial expression of the visitor to estimate the emotion. For example, the AI ​​response unit analyzes the facial expression of the visitor captured by a camera and estimates the emotion such as anger, anxiety, relaxation, etc. The AI ​​response unit can also analyze the voice of the visitor to estimate the emotion. For example, the AI ​​response unit analyzes the tone and speed of the visitor's voice to estimate the emotion. The AI ​​response unit determines the priority of the response based on the estimated emotion. For example, if the visitor is angry, the AI ​​response unit responds quickly to calm the situation. If the visitor is relaxed, the normal response procedure is followed. Furthermore, if the visitor feels anxious, the AI ​​response unit responds with priority to provide a sense of security. This enables an appropriate response according to the emotion of the visitor. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the AI ​​response unit may be performed using AI, or may be performed without using AI. For example, the AI ​​response unit may input facial expression data of a visitor to the generation AI and have the generation AI estimate the emotion.

[0073] The AI ​​response unit can respond based on a sales method specific to the region based on the geographical location information of the visitor. For example, if the visitor comes from a specific region, the AI ​​response unit responds to a sales method specific to the region. For example, the AI ​​response unit acquires the geographical location information of the visitor and responds based on a sales method specific to the region. The AI ​​response unit can also respond in consideration of the culture and customs specific to the region based on the geographical location information of the visitor. For example, the AI ​​response unit uses language and expressions specific to the region based on the geographical location information of the visitor. This makes it possible to respond to sales methods specific to the region. A part or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute a response based on a sales method specific to the region.

[0074] The AI ​​response unit can analyze the social media activity of the visitor and determine whether the visit is for sales purposes. For example, if there are many sales-related posts from the visitor's social media activity, the AI ​​response unit determines that the visit is for sales purposes. For example, the AI ​​response unit analyzes the visitor's social media account and determines whether there are many sales-related posts. In addition, the AI ​​response unit can also determine that the visit is for sales purposes if there are many posts about a specific product or service from the visitor's social media activity. Furthermore, the AI ​​response unit can check the visitor's past visit history from the visitor's social media activity and determine whether the visit is for sales purposes. This makes it possible to determine whether the visit is for sales purposes based on social media activities. Some or all of the above-mentioned processing in the AI ​​response unit may be performed, for example, using AI, or may be performed without using AI. For example, the AI ​​response unit can input the visitor's social media data into the generation AI and have the generation AI execute the judgment of whether the visit is for sales purposes.

[0075] The home-going promotion unit can estimate the emotion of the visitor and adjust the method of encouraging the visitor to go home based on the estimated emotion of the visitor. The home-going promotion unit, for example, analyzes the facial expression of the visitor to estimate the emotion. For example, the home-going promotion unit analyzes the facial expression of the visitor captured by a camera and estimates the emotion, such as anger, anxiety, or relaxation. The home-going promotion unit can also estimate the emotion by analyzing the voice of the visitor. For example, the home-going promotion unit analyzes the tone and speed of the visitor's voice to estimate the emotion. The home-going promotion unit adjusts the method of encouraging the visitor to go home based on the estimated emotion. For example, if the visitor is angry, the visitor is encouraged to go home using calm and polite words. Also, if the visitor is anxious, the visitor is encouraged to go home using words that give a sense of security. Furthermore, if the visitor is relaxed, the visitor is encouraged to go home using friendly words. This makes it possible to promote the visitor to go home appropriately according to the emotion of the visitor. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the home-going promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the home-going promotion unit may input facial expression data of a visitor to the generation AI and cause the generation AI to estimate emotions.

[0076] The home promotion unit can analyze the past home promotion history and select an appropriate home promotion method. The home promotion unit, for example, selects an optimal method in a similar situation based on a home promotion method that was successful in the past. For example, the home promotion unit stores the past home promotion history in a database and selects an optimal method when a similar situation occurs. The home promotion unit can also analyze past failures and improve the home promotion method so as not to repeat the same mistake. For example, the home promotion unit analyzes past failures, identifies the cause of the failure, and improves the home promotion method. Furthermore, the home promotion unit can select an optimal method for a specific visitor from the past home promotion history. For example, the home promotion unit analyzes the home promotion history of a specific visitor and selects an optimal method for that visitor. This improves the accuracy of home promotion based on past data. A part or all of the above-mentioned processing in the home promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the home promotion unit can input the past home promotion history to the generation AI and cause the generation AI to select the optimal home promotion method.

[0077] The home-going promotion unit can analyze the reaction of the visitor in real time and dynamically adjust the message encouraging the visitor to go home. For example, the home-going promotion unit analyzes the facial expression of the visitor in real time and dynamically changes the message encouraging the visitor to go home. For example, the home-going promotion unit analyzes the facial expression of the visitor captured by a camera in real time, estimates emotions such as anger, anxiety, and relaxation, and changes the message based on the emotions. The home-going promotion unit can also analyze the voice of the visitor in real time and dynamically change the message encouraging the visitor to go home. For example, the home-going promotion unit analyzes the tone and speed of the visitor's voice in real time, estimates the emotions, and changes the message based on the emotions. Furthermore, the home-going promotion unit can analyze the biometric data of the visitor (heart rate and electrodermal activity) in real time and dynamically change the message encouraging the visitor to go home. For example, the home-going promotion unit analyzes the fluctuation of the heart rate in real time, estimates the emotions, and changes the message based on the emotions. This enables appropriate home-going promotion in real time. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the home-going promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the home-going promotion unit may input facial expression data of a visitor to the generation AI and cause the generation AI to estimate emotions.

[0078] The home-going promotion unit can estimate the emotion of the visitor and determine the priority of home-going promotion based on the estimated emotion of the visitor. The home-going promotion unit, for example, analyzes the facial expression of the visitor to estimate the emotion. For example, the home-going promotion unit analyzes the facial expression of the visitor captured by a camera and estimates the emotion, such as anger, anxiety, or relaxation. The home-going promotion unit can also analyze the voice of the visitor to estimate the emotion. For example, the home-going promotion unit analyzes the tone and speed of the visitor's voice to estimate the emotion. The home-going promotion unit determines the priority of home-going promotion based on the estimated emotion. For example, if the visitor is angry, the visitor is urged to go home quickly. Also, if the visitor is relaxed, the visitor is urged to go home in the normal procedure. Furthermore, if the visitor feels anxious, the visitor is urged to go home preferentially. This makes it possible to appropriately promote the visitor to go home according to his or her emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (for example, LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the home-going promotion unit may be performed using, for example, AI, or may be performed without using AI. For example, the home-going promotion unit may input facial expression data of the visitor to the generation AI and cause the generation AI to estimate the emotion.

[0079] The home-going promotion unit can apply a home-going promotion method specific to a region based on the geographical location information of the visitor. For example, if the visitor comes from a specific region, the home-going promotion unit applies a home-going promotion method specific to that region. For example, the home-going promotion unit acquires the geographical location information of the visitor and applies a home-going promotion method that takes into account the culture and customs specific to that region. The home-going promotion unit can also use language and expressions specific to the region based on the geographical location information of the visitor. This makes it possible to respond to the home-going promotion method specific to the region. A part or all of the above-mentioned processing in the home-going promotion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the home-going promotion unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute the home-going promotion method specific to the region.

[0080] The home-going promotion unit can analyze the social media activity of the visitor and customize a message encouraging the visitor to go home. For example, if the visitor's social media activity shows many sales-related posts, the home-going promotion unit determines that the visitor has a sales purpose and customizes a message encouraging the visitor to go home. For example, the home-going promotion unit analyzes the visitor's social media account, determines whether there are many sales-related posts, and customizes the message based on the result. In addition, the home-going promotion unit can also determine that the visitor has a sales purpose and customize a message encouraging the visitor to go home if there are many posts about a specific product or service based on the visitor's social media activity. Furthermore, the home-going promotion unit can check the visitor's past visit history from the visitor's social media activity, determine whether the visitor has a sales purpose, and customize the message based on the result. This allows the home-going promotion message to be customized based on the social media activity. A part or all of the above-mentioned processing in the home-going promotion unit may be performed, for example, using AI, or may be performed without using AI. For example, the home-going promotion unit can input the visitor's social media data into the generation AI and cause the generation AI to customize the home-going promotion message.

[0081] The authentication unit can estimate the emotion of the visitor and adjust the authentication process based on the estimated emotion of the visitor. The authentication unit, for example, analyzes the facial expression of the visitor to estimate the emotion. For example, the authentication unit analyzes the facial expression of the visitor captured by a camera to estimate emotions such as anger, anxiety, and relaxation. The authentication unit can also analyze the voice of the visitor to estimate the emotion. For example, the authentication unit analyzes the tone and speed of the visitor's voice to estimate the emotion. The authentication unit adjusts the authentication process based on the estimated emotion. For example, if the visitor is nervous, the authentication process is simplified and authentication is performed quickly. Also, if the visitor is relaxed, a normal authentication process is applied. Furthermore, if the visitor is anxious, an authentication process that gives a sense of security is applied. This enables appropriate authentication according to the emotion of the visitor. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit may input facial expression data of a visitor to the generation AI and have the generation AI estimate the emotion.

[0082] The authentication unit can analyze past authentication history and select an appropriate authentication method. For example, the authentication unit selects the optimal method in a similar situation based on a past successful authentication method. For example, the authentication unit stores past authentication history in a database and selects the optimal method when a similar situation occurs. The authentication unit can also analyze past failure cases and improve the authentication method so as not to repeat the same mistake. For example, the authentication unit analyzes past failure cases, identifies the cause of failure, and improves the authentication method. Furthermore, the authentication unit can select the optimal method for a specific visitor from the past authentication history. For example, the authentication unit analyzes the authentication history of a specific visitor and selects the optimal method for that visitor. This improves the accuracy of authentication based on past data. Part or all of the above-mentioned processing in the authentication unit may be performed, for example, using AI, or may be performed without using AI. For example, the authentication unit can input past authentication history to the generation AI and cause the generation AI to select the optimal authentication method.

[0083] The authentication unit can analyze the minute features of the visitor's face during authentication to improve the authentication accuracy. The authentication unit, for example, analyzes the minute features of the visitor's face to improve the authentication accuracy. For example, the authentication unit uses a face recognition algorithm to analyze the minute features of the visitor's face to improve the authentication accuracy. The authentication unit can also improve the authentication accuracy by comparing the minute features of the visitor's face with past authentication data based on the minute features of the visitor's face. Furthermore, the authentication unit can also analyze the minute features of the visitor's face in real time to improve the authentication accuracy. As a result, the authentication accuracy is improved by analyzing the minute features of the face. A part or all of the above-mentioned processing in the authentication unit may be performed, for example, using AI or may be performed without using AI. For example, the authentication unit can input the minute feature data of the visitor's face to the generation AI and cause the generation AI to execute the improvement of the authentication accuracy.

[0084] The authentication unit can estimate the emotion of the visitor and determine the priority of authentication based on the estimated emotion of the visitor. The authentication unit, for example, analyzes the facial expression of the visitor to estimate the emotion. For example, the authentication unit analyzes the facial expression of the visitor captured by a camera and estimates the emotion such as anger, anxiety, relaxation, etc. The authentication unit can also analyze the voice of the visitor to estimate the emotion. For example, the authentication unit analyzes the tone and speed of the visitor's voice to estimate the emotion. The authentication unit determines the priority of authentication based on the estimated emotion. For example, if the visitor is nervous, authentication is performed quickly. Also, if the visitor is relaxed, normal authentication procedures are performed. Furthermore, if the visitor feels anxious, authentication is performed preferentially. This enables appropriate authentication according to the emotion of the visitor. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processes in the authentication unit may be performed using AI, or may be performed without using AI. For example, the authentication unit may input facial expression data of a visitor to the generation AI and have the generation AI estimate the emotion.

[0085] The authentication unit can apply an authentication process specific to a region based on the geographical location information of the visitor at the time of authentication. For example, if the visitor comes from a specific region, the authentication unit applies an authentication process specific to the region. For example, the authentication unit acquires the geographical location information of the visitor and applies an authentication process that takes into account the culture and customs specific to the region. The authentication unit can also use local language and expressions based on the geographical location information of the visitor. This makes it possible to accommodate a local authentication process. A part or all of the above-mentioned processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute a local authentication process.

[0086] The authentication unit can analyze the social media activity of the visitor at the time of authentication and improve the authentication accuracy. The authentication unit can, for example, check the past visit history from the visitor's social media activity and improve the authentication accuracy. For example, the authentication unit can analyze the visitor's social media account and check the past visit history. The authentication unit can also check posts related to specific products and services from the visitor's social media activity and improve the authentication accuracy. Furthermore, the authentication unit can improve the authentication accuracy when there are many sales-related posts from the visitor's social media activity. This improves the authentication accuracy based on the social media activity. A part or all of the above-mentioned processing in the authentication unit can be performed, for example, using AI or can be performed without using AI. For example, the authentication unit can input the visitor's social media data to the generation AI and cause the generation AI to execute the improvement of the authentication accuracy.

[0087] The relay unit can estimate the emotion of the visitor and adjust the relay method based on the estimated emotion of the visitor. For example, the relay unit can analyze the facial expression of the visitor to estimate the emotion. For example, the relay unit can analyze the facial expression of the visitor captured by a camera to estimate the emotion such as anger, anxiety, relaxation, etc. The relay unit can also analyze the voice of the visitor to estimate the emotion. For example, the relay unit can analyze the tone and speed of the visitor's voice to estimate the emotion. The relay unit adjusts the relay method based on the estimated emotion. For example, if the visitor is nervous, the relay method is simplified and the call is quickly relayed. Also, if the visitor is relaxed, a normal relay method is applied. Furthermore, if the visitor is feeling anxious, a relay method that gives a sense of security is applied. This makes it possible to relay appropriately according to the emotion of the visitor. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the relay unit may be performed, for example, using AI, or may be performed without using AI. For example, the relay unit may input facial expression data of a visitor to the generation AI and cause the generation AI to estimate emotions.

[0088] The mediation unit can analyze past mediation history and select an appropriate mediation method. The mediation unit, for example, selects the optimal method in a similar situation based on a mediation method that was successful in the past. For example, the mediation unit stores past mediation history in a database and selects the optimal method when a similar situation occurs. The mediation unit can also analyze past failure cases and improve the mediation method so as not to repeat the same mistake. For example, the mediation unit analyzes past failure cases, identifies the cause of the failure, and improves the mediation method. Furthermore, the mediation unit can also select the optimal method for a specific visitor from the past mediation history. For example, the mediation unit analyzes the mediation history of a specific visitor and selects the optimal method for that visitor. This improves the accuracy of mediation based on past data. Some or all of the above-mentioned processing in the mediation unit may be performed, for example, using AI, or may be performed without using AI. For example, the referral unit can input past referral history into the generation AI and have the generation AI select the optimal referral method.

[0089] The intermediation unit can analyze the visitor's tone of voice or speech at the time of intermediation to improve the accuracy of the intermediation. For example, if the visitor's voice tone is high and he / she speaks quickly, the intermediation unit pays attention to improve the accuracy of the intermediation. For example, the intermediation unit can analyze the visitor's tone of voice and speech using voice analysis technology to improve the accuracy of the intermediation. In addition, if the visitor's speech is polite, the intermediation unit can proceed as is to improve the accuracy of the intermediation. Furthermore, if the visitor's tone of voice changes, the intermediation unit can reevaluate to improve the accuracy of the intermediation. In this way, the accuracy of the intermediation is improved by analyzing the tone of voice and speech. Part or all of the above-mentioned processing in the intermediation unit may be performed, for example, using AI or may be performed without using AI. For example, the intermediation unit can input the visitor's voice data into the generation AI and cause the generation AI to execute the improvement of the accuracy of the intermediation.

[0090] The relay unit can estimate the emotion of the visitor and determine the priority of the transfer based on the estimated emotion of the visitor. For example, the relay unit estimates the emotion by analyzing the facial expression of the visitor. For example, the relay unit analyzes the facial expression of the visitor captured by a camera and estimates the emotion such as anger, anxiety, relaxation, etc. The relay unit can also estimate the emotion by analyzing the voice of the visitor. For example, the relay unit analyzes the tone and speed of the visitor's voice and estimates the emotion. The relay unit determines the priority of the transfer based on the estimated emotion. For example, if the visitor is nervous, the transfer is made quickly. Also, if the visitor is relaxed, the transfer is made in the normal procedure. Furthermore, if the visitor feels anxious, the transfer is made preferentially. This makes it possible to make an appropriate transfer according to the emotion of the visitor. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the relay unit may be performed, for example, using AI, or may be performed without using AI. For example, the relay unit may input facial expression data of a visitor to the generation AI and cause the generation AI to estimate emotions.

[0091] The intermediation unit can apply an intermediation process specific to a region based on the geographical location information of the visitor at the time of intermediation. For example, if the visitor comes from a specific region, the intermediation unit applies an intermediation process specific to the region. For example, the intermediation unit acquires the geographical location information of the visitor and applies an intermediation process that takes into account the culture and customs specific to the region. The intermediation unit can also use local language and expressions based on the geographical location information of the visitor. This makes it possible to accommodate an intermediation process specific to the region. Some or all of the above-mentioned processing in the intermediation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the intermediation unit can input the geographical location information of the visitor to the generation AI and cause the generation AI to execute an intermediation process specific to the region.

[0092] The mediation unit can analyze the visitor's social media activity at the time of mediation and improve the accuracy of the mediation. The mediation unit, for example, checks the visitor's past visit history from the visitor's social media activity and improves the accuracy of the mediation. For example, the mediation unit analyzes the visitor's social media account and checks the visitor's past visit history. The mediation unit can also check posts about specific products and services from the visitor's social media activity and improve the accuracy of the mediation. Furthermore, the mediation unit can improve the accuracy of the mediation when there are many sales-related posts from the visitor's social media activity. This improves the accuracy of the mediation based on the social media activity. A part or all of the above-mentioned processing in the mediation unit may be performed, for example, using AI or may be performed without using AI. For example, the mediation unit can input the visitor's social media data into the generation AI and cause the generation AI to execute the improvement of the accuracy of the mediation.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The automatic voice response intercom system for the elderly can further include a health monitoring unit. The health monitoring unit can monitor the elderly's health condition in real time and notify family members and medical institutions if an abnormality is detected. For example, the health monitoring unit can periodically measure vital signs such as heart rate, blood pressure, and body temperature, and issue an alert if an abnormal value is detected. The health monitoring unit also has a fall detection function and can automatically notify if the elderly falls. Furthermore, the health monitoring unit can also record the amount of daily activity and provide data for health management. This makes it possible to constantly grasp the elderly's health condition and respond quickly.

[0095] The automatic voice response intercom system for the elderly can further include a voice recognition unit. The voice recognition unit can recognize the voice commands of the elderly and assist in operating the system. For example, the voice recognition unit can automatically open the door when the elderly says, "Open the door." Also, the voice recognition unit can call a pre-registered family member when the elderly says, "Contact my family." Furthermore, the voice recognition unit can notify an emergency contact when the elderly says, "This is an emergency." This allows the elderly to easily operate the system using voice commands, improving convenience.

[0096] The automatic voice response intercom system for the elderly can further include an environmental sensor unit. The environmental sensor unit can monitor the temperature, humidity, air quality, etc., of the room, and provide information for maintaining a comfortable environment. For example, the environmental sensor unit can automatically adjust the air conditioner if the temperature in the room is too high. It can also operate a humidifier if the humidity is too low. Furthermore, it can send a notification to encourage ventilation if the air quality deteriorates. This allows the elderly to live in a comfortable environment.

[0097] The automatic voice response intercom system for the elderly can further include a reminder unit. The reminder unit can provide a reminder function to support the daily life of the elderly. For example, the reminder unit can notify the time to take medicine. The reminder unit can also notify of regular health checks and medical appointments. Furthermore, the reminder unit can notify of birthdays and anniversaries of family members. This allows the elderly to live their daily lives smoothly without forgetting important plans.

[0098] The automatic voice response intercom system for the elderly can further include an entertainment unit. The entertainment unit can provide content for the elderly to enjoy. For example, the entertainment unit can play music or radio. The entertainment unit can also provide news or weather forecasts. The entertainment unit can also provide simple games or puzzles. This allows the elderly to spend a fun time at home.

[0099] The automatic voice response intercom system for the elderly can further estimate the emotions of the elderly using an emotion estimation function and adjust the operation of the system based on the estimated emotions. For example, the system analyzes the facial expressions of the elderly to estimate the emotions. If the elderly feels anxious, the system can provide a voice message that gives a sense of security. If the elderly feels relaxed, the system can play music to help the elderly maintain a relaxed state. If the elderly feels angry, the system can respond calmly and politely. This makes it possible to respond appropriately according to the emotions of the elderly.

[0100] The automatic voice response intercom system for the elderly can further estimate the visitor's emotions using an emotion estimation function and adjust the response content based on the estimated emotion. For example, the system analyzes the visitor's facial expression to estimate the emotion. If the visitor is angry, the system can respond calmly and politely to calm the situation. If the visitor is anxious, the system can also respond in a reassuring way. Furthermore, if the visitor is relaxed, the system can respond in a friendly way to smoothly advance the conversation. This makes it possible to provide an appropriate response according to the visitor's emotions.

[0101] The automatic voice response intercom system for the elderly can further estimate the emotions of the elderly using an emotion estimation function and notify the family based on the estimated emotion. For example, the system analyzes the facial expression of the elderly to estimate the emotion. If the elderly feels anxious, the system can notify the family and encourage a prompt response. Also, if the elderly feels relaxed, the system can notify the family that they feel relieved. Furthermore, if the elderly feels angry, the system can report the situation to the family and request an appropriate response. This enables a prompt response according to the elderly's emotions.

[0102] The automated voice response intercom system for the elderly can further estimate the emotions of the elderly using an emotion estimation function and adjust the contents of the reminder based on the estimated emotions. For example, the system analyzes the facial expressions of the elderly to estimate the emotions. If the elderly feels anxious, the system can provide a reassuring reminder. If the elderly feels relaxed, the system can also provide a reminder to maintain a relaxed state. Furthermore, if the elderly feels angry, the system can provide a calm and polite reminder. This enables appropriate reminders according to the elderly's emotions.

[0103] The automated voice response intercom system for the elderly can further estimate the emotions of visitors using an emotion estimation function and adjust the method of encouraging them to go home based on the estimated emotions. For example, the system analyzes the visitor's facial expressions to estimate the emotions. If the visitor is angry, the system can encourage them to go home using calm and polite words. If the visitor is feeling anxious, the system can encourage them to go home using words that give a sense of security. Furthermore, if the visitor is relaxed, the system can encourage them to go home using friendly words. This makes it possible to encourage visitors to go home appropriately according to their emotions.

[0104] The process flow of the second embodiment will be briefly described below.

[0105] Step 1: The AI ​​response unit determines the visitor's purpose. For example, it asks the visitor, "What do you want?" and analyzes the visitor's response. Using a machine learning algorithm, the AI ​​response unit can determine from the visitor's response whether the visitor's purpose is to make a sale. It analyzes keywords and phrases contained in the visitor's response to determine whether the purpose is to make a sale. Step 2: The homeward promotion unit encourages sales visitors to go home based on the purpose determined by the AI ​​response unit. For example, it can automatically issue a voice message such as, "Sorry, but we are not currently accepting sales. Please go home." It can also display a message on the intercom display saying, "We are not currently accepting sales. Please go home." Step 3: The authentication unit is equipped with a facial recognition function and recognizes the visitor's face. For example, the visitor's face is photographed using a camera built into the intercom and a determination is made as to whether it matches the face of a pre-registered family member. The authentication unit is equipped with security measures to protect the facial recognition data and prevent unauthorized access. For example, the facial recognition data is encrypted and stored, and access is controlled. Step 4: The relay unit promptly relays visitors to the family who have been authenticated by the authentication unit. For example, when a visitor arrives, it can automatically open the door or notify the elderly person. The relay unit has a function to check camera footage in real time via a smartphone. For example, a child can use a smartphone to check the intercom camera footage in real time and respond remotely if necessary.

[0106] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0108] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements including the AI ​​response unit, the home promotion unit, the authentication unit, and the relay unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the AI ​​response unit is realized by the control unit 46A of the smart device 14, and determines the purpose of the visitor. The home promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and urges the visitor who is for sales purposes to go home. The authentication unit recognizes the face of the visitor using, for example, the camera 42 of the smart device 14, and performs face authentication by the specific processing unit 290 of the data processing device 12. The relay unit is realized, for example, by the control unit 46A of the smart device 14, and promptly relays the authenticated family visitor. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0117] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0120] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0122] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements including the AI ​​response unit, the home promotion unit, the authentication unit, and the relay unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the AI ​​response unit is realized by the control unit 46A of the smart glasses 214, and determines the purpose of the visitor. The home promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and urges the visitor who is for sales purposes to go home. The authentication unit recognizes the face of the visitor using, for example, the camera 42 of the smart glasses 214, and performs face authentication by the specific processing unit 290 of the data processing device 12. The relay unit is realized, for example, by the control unit 46A of the smart glasses 214, and promptly relays the authenticated visitor of the family. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0128] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0136] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements including the AI ​​response unit, the home promotion unit, the authentication unit, and the intermediation unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the AI ​​response unit is realized by the control unit 46A of the headset terminal 314, and determines the purpose of the visitor. The home promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encourages visitors who are for sales purposes to go home. The authentication unit recognizes the face of the visitor using, for example, the camera 42 of the headset terminal 314, and performs face authentication by the specific processing unit 290 of the data processing device 12. The intermediation unit is realized, for example, by the control unit 46A of the headset terminal 314, and promptly intermediates authenticated family visitors. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0153] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] Each of the multiple elements including the AI ​​response unit, the home promotion unit, the authentication unit, and the intermediation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the AI ​​response unit is realized by the control unit 46A of the robot 414, and determines the purpose of the visitor. The home promotion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and encourages the visitor who is for sales purposes to go home. The authentication unit recognizes the face of the visitor using, for example, the camera 42 of the robot 414, and performs face authentication by the specific processing unit 290 of the data processing device 12. The intermediation unit is realized, for example, by the control unit 46A of the robot 414, and promptly intermediates the authenticated family visitor. The correspondence relationship between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0159] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0160] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0161] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0162] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0163] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0164] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0167] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0168] In addition, 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 upon request from the data processing device 12.

[0169] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0170] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0171] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0172] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0173] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0174] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0175] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0176] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0177] (Appendix 1) An AI response section that determines the visitor's purpose, A home promotion unit that encourages sales visitors to go home based on the purpose determined by the AI ​​response unit; An authentication unit equipped with a face recognition function; A unit for promptly receiving visitors to the family authenticated by the authentication unit. A system characterized by: (Appendix 2) The AI ​​response unit is Equipped with an algorithm to determine sales intent 2. The system of claim 1, (Appendix 3) The authentication unit Equipping the system with security measures to protect facial recognition data and prevent unauthorized access 2. The system of claim 1, (Appendix 4) The intermediary unit is Equipped with a means to check camera footage in real time via smartphone 2. The system of claim 1, (Appendix 5) The AI ​​response unit is Estimate the visitor's emotions and adjust the response based on the estimated visitor's emotions 2. The system of claim 1, (Appendix 6) The AI ​​response unit is Analyze past response history and choose the appropriate response method 2. The system of claim 1, (Appendix 7) The AI ​​response unit is Analyze the tone of a visitor's voice or manner of speaking to more accurately determine whether or not they are seeking sales 2. The system of claim 1, (Appendix 8) The AI ​​response unit is Estimate visitor emotions and prioritize responses based on the estimated visitor emotions 2. The system of claim 1, (Appendix 9) The AI ​​response unit is Use geographic location information to respond to visitors with local sales techniques 2. The system of claim 1, (Appendix 10) The AI ​​response unit is Analyze visitors' social media activity to determine if they are seeking sales 2. The system of claim 1, (Appendix 11) The return home promotion unit includes: Estimate the visitor's emotions and adjust the way you encourage them to go home based on the estimated emotions. 2. The system of claim 1, (Appendix 12) The return home promotion unit includes: Analyze past records of encouraging people to return home and select the most appropriate method to encourage people to return home 2. The system of claim 1, (Appendix 13) The return home promotion unit includes: Analyze visitors' reactions in real time and dynamically adjust messages encouraging them to go home 2. The system of claim 1, (Appendix 14) The return home promotion unit includes: Estimating the emotions of visitors and determining the priority of encouraging them to return home based on the estimated emotions of the visitors 2. The system of claim 1, (Appendix 15) The return home promotion unit includes: Applying localized methods to encourage visitors to return home based on their geographic location 2. The system of claim 1, (Appendix 16) The return home promotion unit includes: Analyze visitors' social media activity to customize messages encouraging them to leave 2. The system of claim 1, (Appendix 17) The authentication unit Estimating visitor emotions and adjusting the authentication process based on the estimated visitor emotions 2. The system of claim 1, (Appendix 18) The authentication unit Analyze past authentication history and select the appropriate authentication method 2. The system of claim 1, (Appendix 19) The authentication unit During authentication, minute facial features of the visitor are analyzed to improve authentication accuracy. 2. The system of claim 1, (Appendix 20) The authentication unit Estimating the emotion of a visitor and determining the priority of authentication based on the estimated emotion of the visitor 2. The system of claim 1, (Appendix 21) The authentication unit During authentication, a region-specific authentication process is applied based on the visitor's geographic location. 2. The system of claim 1, (Appendix 22) The authentication unit Analyze visitors' social media activity during authentication to improve authentication accuracy 2. The system of claim 1, (Appendix 23) The intermediary unit is Estimate the visitor's emotions and adjust the way the visitor is handled based on the estimated emotions. 2. The system of claim 1, (Appendix 24) The intermediary unit is Analyze past call history and choose the appropriate call forwarding method 2. The system of claim 1, (Appendix 25) The intermediary unit is When transferring a call, the tone of the visitor's voice or speaking style is analyzed to improve the accuracy of the transfer. 2. The system of claim 1, (Appendix 26) The intermediary unit is Estimate the visitor's emotions and determine the priority of the call based on the estimated emotions of the visitor. 2. The system of claim 1, (Appendix 27) The intermediary unit is At the time of referral, apply localized referral processes based on the visitor's geographic location 2. The system of claim 1, (Appendix 28) The intermediary unit is When transferring a visitor, analyze the visitor's social media activity to improve the accuracy of the transfer. 2. The system of claim 1, [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An AI response unit that determines the visitor's purpose; A home promotion unit that encourages sales visitors to go home based on the purpose determined by the AI ​​response unit; An authentication unit equipped with a face recognition function; A unit for promptly receiving visitors to the family authenticated by the authentication unit. A system characterized by:

2. The AI ​​response unit, Equipped with an algorithm to determine sales intent 2. The system of claim 1 .

3. The authentication unit Equipping the system with security measures to protect facial recognition data and prevent unauthorized access 2. The system of claim 1 .

4. The intermediary unit is Equipped with a means to check camera footage in real time via smartphone 2. The system of claim 1 .

5. The AI ​​response unit, Estimate the visitor's emotions and adjust the response based on the estimated visitor's emotions 2. The system of claim 1 .

6. The AI ​​response unit, Analyze past response history and choose the appropriate response method 2. The system of claim 1 .

7. The AI ​​response unit, Analyze the tone of a visitor's voice or manner of speaking to more accurately determine whether or not they are seeking sales 2. The system of claim 1 .

8. The AI ​​response unit, Estimate visitor emotions and prioritize responses based on the estimated visitor emotions 2. The system of claim 1 .

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