system

Abstract

The system according to the embodiment aims to automatically determine the requirements of visitors and appropriately introduce them to residents. [Solution] A system according to an embodiment includes an analysis unit, an understanding unit, a recognition unit, and an intermediation unit. The analysis unit analyzes the visitor's voice. The understanding unit understands the visitor's requirements based on the voice analyzed by the analysis unit. The recognition unit recognizes the visitor's face. The intermediation unit intermediates the visitor to a resident if the requirements understood by the understanding unit are acceptable.

Description

[Technical Field]

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

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

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

[0004] Existing technologies do not adequately automate the process of properly determining visitor requirements and connecting them with residents, leaving room for improvement.

[0005] The system according to the embodiment aims to automatically determine the requirements of visitors and appropriately introduce them to residents. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an understanding unit, a recognition unit, and an intermediation unit. The analysis unit analyzes the visitor's voice. The understanding unit understands the visitor's requirements based on the voice analyzed by the analysis unit. The recognition unit recognizes the visitor's face. The intermediation unit intermediates the visitor to a resident if the requirements understood by the understanding unit are acceptable. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine the visitor's requirements and transfer them to the resident appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The intercom system according to an embodiment of the present invention analyzes the visitor's voice and face, understands their needs, and then connects them to the resident. When a visitor presses the intercom, an AI responds and confirms the visitor's needs. The AI ​​analyzes the visitor's voice and understands their needs. For example, if a visitor says, "This is a parcel," the AI ​​analyzes the content and confirms that it is a parcel. If the AI ​​determines that the visitor's needs are met, it connects the call to the resident. For example, if the AI ​​determines that the call is a parcel, it notifies the resident and allows them to choose whether to answer the call. This helps to prevent fraud and unwanted visitors. The AI ​​can also record the visitor's voice and review it later. The AI ​​can also recognize the visitor's face and determine whether they have visited before. For example, if a visitor has been registered as a unwanted visitor in the past, the AI ​​can automatically reject that visitor. Furthermore, the AI ​​analyzes the visitor's behavior and facial expressions to understand their needs. This system allows residents to live with peace of mind. This allows the intercom system to repel fraudulent and unwanted visitors by analyzing the visitor's voice and face, understanding their requirements, and connecting them to the resident.

[0029] An intercom system according to an embodiment includes an analysis unit, an understanding unit, a recognition unit, and an intermediary unit. The analysis unit analyzes the visitor's voice. The analysis unit analyzes the visitor's voice, for example, using voice recognition technology. For example, if the visitor says, "This is a delivery," the analysis unit analyzes the content of the visitor's voice and confirms that it is a delivery. The analysis unit can also understand the visitor's requirements using natural language processing technology. For example, the analysis unit extracts keywords from the visitor's voice and understands the requirements based on the keywords. The understanding unit understands the visitor's requirements based on the voice analyzed by the analysis unit. The understanding unit understands the visitor's requirements using, for example, keyword extraction technology. For example, if the visitor says, "This is a delivery," the understanding unit understands the content of the visitor's voice and confirms that it is a delivery. The understanding unit can also understand the visitor's requirements using context analysis technology. For example, the understanding unit analyzes the context from the visitor's voice and understands the requirements based on the context. The recognition unit recognizes the visitor's face. For example, the recognition unit recognizes the visitor's face using a facial recognition algorithm. For example, the recognition unit may capture a visitor's face with a camera and analyze the image to recognize the visitor's face. The recognition unit may also recognize the visitor's face using image processing technology. For example, the recognition unit may extract facial features of the visitor and recognize the visitor's face based on the features. The relay unit may then connect the call to a resident if the requirements understood by the understanding unit are acceptable. For example, the relay unit may notify the resident and allow the resident to choose whether to respond. For example, the relay unit may send a notification to the resident's smartphone and allow the resident to choose whether to respond. The relay unit may also send a notification to the resident's tablet and allow the resident to choose whether to respond. This allows the intercom system according to the embodiment to analyze the visitor's voice and face, understand the visitor's requirements, and then connect the call to the resident, thereby repelling fraudulent and nuisance visitors.

[0030] The intercom system includes an analysis unit that analyzes the behavior or facial expression of a visitor. The analysis unit analyzes the behavior and facial expression of a visitor. The analysis unit analyzes the behavior of a visitor using, for example, motion analysis technology. For example, the analysis unit captures the behavior of a visitor with a camera and analyzes the video to understand the behavior of the visitor. The analysis unit can also analyze the behavior of a visitor using gesture recognition technology. For example, the analysis unit analyzes the gestures of a visitor and understands the behavior of the visitor based on the gestures. The analysis unit can also analyze the facial expression of a visitor using facial expression recognition technology. For example, the analysis unit captures the facial expression of a visitor with a camera and analyzes the video to understand the facial expression of the visitor. The analysis unit can also analyze the facial expression of a visitor using emotion analysis technology. For example, the analysis unit infers emotions from the facial expression of a visitor and understands the facial expression based on the emotions. In this way, by analyzing the behavior and facial expression of a visitor, the intention of the visitor can be understood more accurately.

[0031] The analysis unit can record the visitor's voice. The analysis unit records the visitor's voice. The analysis unit, for example, saves the visitor's voice as digital data. For example, the analysis unit records the visitor's voice and saves the voice data in digital format. The analysis unit can also record the visitor's voice in real time. For example, the analysis unit records the visitor's voice in real time and saves the voice data in digital format. The analysis unit can also save the visitor's voice in the cloud. For example, the analysis unit uploads the visitor's voice data to cloud storage so that it can be checked later. In this way, by recording the visitor's voice, it can be checked later.

[0032] The recognition unit can automatically reject a visitor if the visitor has been registered as a nuisance visitor in the past. The recognition unit automatically rejects a visitor if the visitor has been registered as a nuisance visitor in the past. The recognition unit, for example, stores past visit history in a database and identifies nuisance visitors by comparing the image with the database. For example, the recognition unit may take a photo of the visitor's face with a camera and compare the image with the database to identify nuisance visitors. The recognition unit can also identify nuisance visitors based on specific behavioral patterns. For example, the recognition unit may analyze the visitor's behavioral pattern and automatically reject the visitor if the pattern matches the pattern of a nuisance visitor. This makes it possible to ensure the safety of residents by automatically rejecting nuisance visitors in the past.

[0033] The relay unit notifies the resident and allows the resident to choose whether or not to respond to the call. The relay unit notifies the resident and allows the resident to choose whether or not to respond to the call. The relay unit, for example, sends a notification to the resident's smartphone and allows the resident to choose whether or not to respond to the call. For example, the relay unit sends a push notification to the resident's smartphone and allows the resident to choose whether or not to respond to the call. The relay unit can also send a notification to the resident's tablet and allows the resident to choose whether or not to respond to the call. For example, the relay unit can send a push notification to the resident's tablet and allows the resident to choose whether or not to respond to the call. The relay unit can also send a notification to the resident's smartwatch and allows the resident to choose whether or not to respond to the call. For example, the relay unit can send a push notification to the resident's smartwatch and allows the resident to choose whether or not to respond to the call. This allows the resident to choose whether or not to respond, thereby reducing the burden on the resident.

[0034] The analysis unit can remove background noise from the visitor's voice to improve the accuracy of the analysis. The analysis unit can remove background noise from the visitor's voice to improve the accuracy of the analysis. The analysis unit can remove background noise from the visitor's voice, for example, using noise filtering technology. For example, the analysis unit can filter the visitor's voice in real time to remove background noise. The analysis unit can also record the visitor's voice and perform noise removal processing later. For example, the analysis unit can record the visitor's voice and process the voice data later using noise filtering technology. Furthermore, the analysis unit can collect the visitor's voice using multiple microphones and remove background noise using noise canceling technology. For example, the analysis unit can analyze voice data collected by multiple microphones and remove background noise using noise canceling technology. This removes background noise, improving the accuracy of the voice analysis.

[0035] The analysis unit can analyze the tone and speed of the caller's voice to determine the caller's level of urgency. The analysis unit can analyze the tone and speed of the caller's voice to determine the caller's level of urgency. The analysis unit can analyze the tone of the caller's voice using, for example, voice pitch analysis technology. For example, the analysis unit can analyze the pitch of the caller's voice and determine the caller's level of urgency based on the pitch. The analysis unit can also analyze the speed of the caller's voice using speech rate analysis technology. For example, the analysis unit can analyze the speed of the caller's voice and determine the caller's level of urgency based on the speed. The analysis unit can also analyze changes in the tone and speed of the caller's voice to determine changes in the level of urgency in real time. For example, the analysis unit can analyze changes in the tone and speed of the caller's voice and determine changes in the level of urgency in real time based on the changes. In this way, the caller's level of urgency can be determined by analyzing the tone and speed of the voice.

[0036] The analysis unit analyzes the visitor's voice in multiple languages ​​and can accommodate visitors who speak different languages. The analysis unit analyzes the visitor's voice in multiple languages ​​and can accommodate visitors who speak different languages. The analysis unit analyzes the visitor's voice in multiple languages ​​using, for example, real-time translation technology. For example, the analysis unit translates the visitor's voice in real time and analyzes the translation result. The analysis unit can also record the visitor's voice and perform multilingual analysis later. For example, the analysis unit records the visitor's voice and later analyzes the voice data using multilingual translation technology. Furthermore, the analysis unit can analyze the visitor's voice in multiple languages ​​and respond in the most appropriate language. For example, the analysis unit analyzes the visitor's voice in multiple languages ​​and selects the most appropriate language from among them to respond. In this way, multilingual analysis can accommodate visitors who speak different languages.

[0037] The analysis unit can translate the visitor's voice in real time and notify the resident. The analysis unit translates the visitor's voice in real time and notifies the resident. The analysis unit translates the visitor's voice using, for example, real-time translation technology. For example, the analysis unit translates the visitor's voice in real time and notifies the resident of the translation result. The analysis unit can also translate the visitor's voice in real time and notify the resident of the translation result as a text message. For example, the analysis unit translates the visitor's voice in real time and sends the translation result as a text message to the resident's smartphone. The analysis unit can also translate the visitor's voice in real time and notify the resident of the translation result as a voice message. For example, the analysis unit translates the visitor's voice in real time and sends the translation result as a voice message to the resident's smartphone. This allows the resident to instantly understand the visitor's requirements by translating in real time.

[0038] The understanding unit can classify the visitor's requirements by category to improve the accuracy of understanding. The understanding unit classifies the visitor's requirements by category to improve the accuracy of understanding. The understanding unit classifies the visitor's requirements using, for example, a category classification algorithm. For example, the understanding unit classifies the visitor's requirements into categories such as "delivery service," "sales," and "door-to-door sales." The understanding unit can also classify the visitor's requirements into categories such as "urgent" and "non-urgent." For example, the understanding unit classifies the visitor's requirements into categories such as "urgent" and "non-urgent," and understands the requirements based on the categories. The understanding unit can also classify the visitor's requirements into categories such as "individual" and "corporation." For example, the understanding unit classifies the visitor's requirements into categories such as "individual" and "corporation," and understands the requirements based on the categories. In this way, by classifying the requirements by category, the accuracy of understanding is improved.

[0039] The understanding unit can compare the visitor's requirements with past data to identify similar requirements. The understanding unit compares the visitor's requirements with past data to identify similar requirements. The understanding unit compares with past data, for example, using database search technology. For example, the understanding unit compares the visitor's requirements with past requirements stored in a database to identify similar requirements. The understanding unit can also propose an optimal response method by comparison with past data. For example, the understanding unit compares the visitor's requirements with past data and proposes an optimal response method based on the result. Furthermore, the understanding unit can also identify problematic requirements by comparison with past data. For example, the understanding unit compares the visitor's requirements with past data and identify problematic requirements based on the result. In this way, similar requirements can be identified by comparing with past data.

[0040] The understanding unit can convert the visitor's requirements into text and notify the resident. The understanding unit converts the visitor's requirements into text and notifies the resident. The understanding unit converts the visitor's requirements into text, for example, using voice recognition technology. For example, the understanding unit converts the visitor's voice into text in real time and notifies the resident of the text. The understanding unit can also record the visitor's voice, convert it to text later, and notify the resident. For example, the understanding unit records the visitor's voice, converts the voice data later into text, and sends it to the resident's smartphone. The understanding unit can also convert the visitor's requirements into text and notify the resident of the text to their device. For example, the understanding unit converts the visitor's requirements into text and sends the text to the resident's smartphone or tablet. In this way, by converting the requirements into text, the resident can instantly understand the visitor's requirements.

[0041] The understanding unit can understand the visitor's requirements not only from their voice but also from their gestures and facial expressions. The understanding unit can understand the visitor's requirements not only from their voice but also from their gestures and facial expressions. The understanding unit can, for example, analyze the visitor's gestures using motion analysis technology. For example, the understanding unit can capture the visitor's gestures with a camera and analyze the video to understand the visitor's requirements. The understanding unit can also analyze the visitor's facial expressions using facial expression recognition technology. For example, the understanding unit can capture the visitor's facial expressions with a camera and analyze the video to understand the visitor's requirements. The understanding unit can also understand the visitor's requirements by comprehensively analyzing the visitor's voice, gestures, and facial expressions. For example, the understanding unit can comprehensively analyze the visitor's voice, gestures, and facial expressions and understand the requirements based on the results. This enables more accurate responses by understanding the requirements not only from their voice but also from their gestures and facial expressions.

[0042] The recognition unit can improve the accuracy of recognition by photographing the visitor's face from multiple angles. The recognition unit can improve the accuracy of recognition by photographing the visitor's face from multiple angles. The recognition unit, for example, arranges multiple cameras to photograph the visitor's face. For example, the recognition unit photographs the visitor's face from the front and analyzes the image to recognize it. The recognition unit can also photograph the visitor's face from left and right angles and analyze the image to recognize it. Furthermore, the recognition unit can photograph the visitor's face from above and below angles and analyze the image to recognize it. For example, the recognition unit photographs the visitor's face from above and below angles and analyze the image to recognize it. In this way, by photographing the visitor's face from multiple angles, the accuracy of facial recognition is improved.

[0043] The recognition unit can check the visitor's face against a past database to confirm the visitor's history. The recognition unit can check the visitor's face against a past database to confirm the visitor's history. The recognition unit can check against the past database using, for example, database search technology. For example, the recognition unit can take a picture of the visitor's face with a camera and check the image against a database to confirm the visitor's history. The recognition unit can also identify nuisance visitors based on the past visit history. For example, the recognition unit can check the visitor's face against a database and identify nuisance visitors based on the result. Furthermore, the recognition unit can also check the visitor's frequency of visits. For example, the recognition unit can check the visitor's face against a database and check the visitor's frequency of visits based on the result. In this way, the visitor's history can be confirmed by checking against the past database.

[0044] The recognition unit can recognize the visitor's face in real time and notify the resident. The recognition unit recognizes the visitor's face in real time and notifies the resident. The recognition unit recognizes the visitor's face using, for example, real-time facial recognition technology. For example, the recognition unit captures the visitor's face with a camera and analyzes and recognizes the image in real time. The recognition unit can also notify the resident of the recognition result as a text message. For example, the recognition unit recognizes the visitor's face in real time and sends the recognition result as a text message to the resident's smartphone. The recognition unit can also notify the resident of the recognition result as a voice message. For example, the recognition unit recognizes the visitor's face in real time and sends the recognition result as a voice message to the resident's smartphone. This allows the resident to immediately understand the visitor's requirements through real-time recognition.

[0045] The recognition unit can recognize not only the visitor's face but also their clothing and belongings, which can be useful in identifying the visitor. The recognition unit can recognize not only the visitor's face but also their clothing and belongings, which can be useful in identifying the visitor. The recognition unit, for example, uses object recognition technology to recognize the visitor's clothing. For example, the recognition unit photographs the visitor's clothing with a camera and recognizes the image by analyzing it. The recognition unit can also recognize the visitor's belongings using object recognition technology. For example, the recognition unit photographs the visitor's belongings with a camera and recognizes the image by analyzing it. The recognition unit can also comprehensively recognize the visitor's face, clothing, and belongings, which can be useful in identifying the visitor. For example, the recognition unit comprehensively recognizes the visitor's face, clothing, and belongings, and identifies the visitor based on the results. In this way, by recognizing not only the face but also the clothing and belongings, the visitor can be identified more accurately.

[0046] When transferring a call, the relay unit can select an appropriate timing by taking into consideration the resident's schedule. When transferring a call, the relay unit selects an appropriate timing by taking into consideration the resident's schedule. The relay unit, for example, checks the resident's schedule using calendar information. For example, the relay unit references the resident's calendar information and transfers the call at the optimal timing. The relay unit can also adjust the timing of the transfer by taking into consideration the resident's schedule. For example, the relay unit checks the resident's schedule and adjusts the timing of the transfer based on that schedule. Furthermore, the relay unit can check the resident's schedule in real time and transfer the call at the optimal timing. For example, the relay unit checks the resident's schedule in real time and transfers the call at the optimal timing based on that schedule. In this way, the relay unit can transfer the call at the optimal timing by taking the resident's schedule into consideration.

[0047] The mediation unit can determine the priority level based on the urgency of the visitor's requirements when forwarding the request. The mediation unit determines the priority level based on the urgency of the visitor's requirements when forwarding the request. The mediation unit, for example, analyzes the content of the requirements to evaluate the urgency. For example, the mediation unit analyzes the visitor's requirements and evaluates the urgency based on the content. The mediation unit can also evaluate the urgency by taking time constraints into consideration. For example, the mediation unit analyzes the time constraints of the visitor's requirements and evaluates the urgency based on the constraints. Furthermore, the mediation unit determines the priority level based on the urgency of the visitor's requirements. For example, if the visitor's requirements are urgent, the mediation unit prioritizes the request. Furthermore, if the visitor's requirements are non-urgent, the mediation unit prioritizes the request. Furthermore, the mediation unit can analyze the urgency of the visitor's requirements and forward the request at the optimal priority level. This enables a quick response by determining the priority level based on the urgency of the visitor's requirements.

[0048] When transferring a call, the relay unit can send a notification to the resident's device and provide options for how to respond. When transferring a call, the relay unit can send a notification to the resident's device and provide options for how to respond. The relay unit can, for example, send a notification to the resident's smartphone and provide options for how to respond. For example, the relay unit can send a push notification to the resident's smartphone and provide options for how to respond. The relay unit can also send a notification to the resident's tablet and provide options for how to respond. For example, the relay unit can send a push notification to the resident's tablet and provide options for how to respond. The relay unit can also send a notification to the resident's smartwatch and provide options for how to respond. For example, the relay unit can send a push notification to the resident's smartwatch and provide options for how to respond. In this way, by sending a notification to the resident's device, the resident can have options for how to respond.

[0049] The mediation unit can transmit the visitor's requirements to the resident as a text message when transferring the visitor. The mediation unit transmits the visitor's requirements to the resident as a text message when transferring the visitor. The mediation unit, for example, transmits the visitor's requirements as a text message to the resident's smartphone. For example, the mediation unit transmits the visitor's requirements as a text message to the resident's smartphone, allowing the resident to immediately understand the requirements. The mediation unit can also transmit the visitor's requirements as a text message to the resident's tablet. For example, the mediation unit transmits the visitor's requirements as a text message to the resident's tablet, allowing the resident to immediately understand the requirements. The mediation unit can also transmit the visitor's requirements as a text message to the resident's smartwatch. For example, the mediation unit transmits the visitor's requirements as a text message to the resident's smartwatch, allowing the resident to immediately understand the requirements. In this way, by transmitting the visitor's requirements as a text message, the resident can immediately understand the requirements.

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

[0051] The analysis unit not only analyzes the visitor's voice, but also analyzes the visitor's background sounds to estimate the visitor's environment. For example, the analysis unit analyzes the sounds of cars and people talking in the background of the visitor's voice to estimate whether the visitor is outdoors or indoors. The analysis unit can also estimate the weather from the visitor's background sounds. For example, if the sound of rain is heard, it estimates that the visitor is in the rain. Furthermore, the analysis unit can also estimate the visitor's activity from the visitor's background sounds. For example, if the sound of exercise is heard in the background of the visitor, it estimates that the visitor is exercising. This allows for more appropriate response by estimating the visitor's environment.

[0052] The analysis unit not only analyzes the visitor's behavior and facial expressions, but also analyzes the visitor's clothing and belongings to infer the visitor's attributes. For example, the analysis unit can infer the visitor's occupation from the visitor's clothing. For example, if the visitor is wearing a suit, it can infer that the visitor is a businessman. The analysis unit can also infer the visitor's purpose from the visitor's belongings. For example, if the visitor is carrying a parcel, it can infer that the visitor is a delivery person. Furthermore, the analysis unit can infer the visitor's age group from the visitor's clothing and belongings. For example, if the visitor is wearing a school uniform, it can infer that the visitor is a student. In this way, by inferring the visitor's attributes, more appropriate responses can be made.

[0053] The analysis unit not only records the visitor's voice, but also analyzes the visitor's voice and extracts characteristics of the visitor's speaking style. For example, the analysis unit analyzes the speed and rhythm of the visitor's speech and extracts these characteristics. The analysis unit can also analyze the accent and intonation of the visitor's speech and extract these characteristics. Furthermore, the analysis unit can save the characteristics of the visitor's speaking style in a database and check them later. For example, saving the characteristics of the visitor's speaking style in a database and comparing it with past visitors can help identify the visitor. In this way, extracting the characteristics of the visitor's speaking style enables more accurate response.

[0054] The recognition unit not only automatically rejects visitors who have been registered as nuisance visitors in the past, but also analyzes the visitor's behavioral patterns to detect signs of a nuisance visitor. For example, the recognition unit analyzes the visitor's behavioral patterns and, if those patterns match those of past nuisance visitors, becomes alert to the visitor. The recognition unit can also analyze the visitor's behavioral patterns in real time to detect abnormal behavior. For example, if a visitor stands in front of the intercom for a long period of time, it determines that behavior to be abnormal. Furthermore, the recognition unit can save the visitor's behavioral patterns in a database and check them later. In this way, by analyzing the visitor's behavioral patterns, signs of a nuisance visitor can be detected and the safety of residents can be ensured.

[0055] The relay unit not only notifies the resident and allows the resident to decide whether or not to respond, but also refers to the resident's past response history and suggests the optimal response method. For example, the relay unit stores the resident's past response history in a database and refers to that history to suggest the optimal response method. The relay unit can also analyze the resident's past response history and learn the resident's response patterns. For example, if a resident tends to respond in a specific way to certain visitors, the relay unit can learn that pattern. Furthermore, the relay unit can provide the resident with response advice based on the resident's past response history. This makes it possible to respond more appropriately by referring to the resident's past response history.

[0056] The analysis unit not only removes background noise from the visitor's voice and improves the accuracy of the analysis, but also removes echoes from the visitor's voice. For example, the analysis unit may use echo cancellation technology to remove echoes from the visitor's voice. The analysis unit may also record the visitor's voice and later perform echo removal processing. For example, the analysis unit may record the visitor's voice and later process the audio data using echo cancellation technology. The analysis unit may also collect the visitor's voice using multiple microphones and remove echoes using echo cancellation technology. This removes echoes, thereby improving the accuracy of the voice analysis.

[0057] The analysis unit not only analyzes the tone and speed of the caller's voice to determine the caller's level of urgency, but also analyzes the content of the caller's voice to understand the details of the caller's requirements. For example, the analysis unit can extract keywords from the caller's voice and understand the details of the requirements based on those keywords. The analysis unit can also analyze the context of the caller's voice and understand the details of the requirements based on that context. Furthermore, the analysis unit can save the content of the caller's voice in a database and check it later. This makes it possible to understand the details of the requirements by analyzing the content of the caller's voice, enabling more appropriate responses.

[0058] The analysis unit not only analyzes the visitor's voice in multiple languages ​​and can accommodate visitors who speak different languages, but can also translate the visitor's voice and notify the resident. For example, the analysis unit can translate the visitor's voice in real time and notify the resident of the translation result. The analysis unit can also record the visitor's voice, translate it later, and notify the resident. Furthermore, the analysis unit can translate the visitor's voice into multiple languages ​​and notify the resident of the translation result. This allows the resident to immediately understand the visitor's requirements by translating the visitor's voice.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The analysis unit analyzes the visitor's voice. The analysis unit uses voice recognition technology and natural language processing technology to analyze the visitor's voice, and analyzes and confirms the content, such as "This is a delivery." Step 2: The understanding unit understands the visitor's requirements based on the speech analyzed by the analysis unit. The understanding unit uses keyword extraction and context analysis techniques to understand the visitor's requirements, for example, "This is a delivery service," and confirms them. Step 3: The recognition unit recognizes the visitor's face. The recognition unit uses a facial recognition algorithm and image processing technology to take a picture of the visitor's face with a camera, analyze the image, and recognize the visitor's face. Step 4: The relay unit relays the call to the resident if the requirements understood by the understanding unit are acceptable. The relay unit sends a notification to the resident, allowing the resident to choose whether or not to respond.

[0061] (Example 2) The intercom system according to an embodiment of the present invention analyzes the visitor's voice and face, understands their needs, and then connects them to the resident. When a visitor presses the intercom, an AI responds and confirms the visitor's needs. The AI ​​analyzes the visitor's voice and understands their needs. For example, if a visitor says, "This is a parcel," the AI ​​analyzes the content and confirms that it is a parcel. If the AI ​​determines that the visitor's needs are met, it connects the call to the resident. For example, if the AI ​​determines that the call is a parcel, it notifies the resident and allows them to choose whether to answer the call. This helps to prevent fraud and unwanted visitors. The AI ​​can also record the visitor's voice and review it later. The AI ​​can also recognize the visitor's face and determine whether they have visited before. For example, if a visitor has been registered as a unwanted visitor in the past, the AI ​​can automatically reject that visitor. Furthermore, the AI ​​analyzes the visitor's behavior and facial expressions to understand their needs. This system allows residents to live with peace of mind. This allows the intercom system to repel fraudulent and unwanted visitors by analyzing the visitor's voice and face, understanding their requirements, and connecting them to the resident.

[0062] An intercom system according to an embodiment includes an analysis unit, an understanding unit, a recognition unit, and an intermediary unit. The analysis unit analyzes the visitor's voice. The analysis unit analyzes the visitor's voice, for example, using voice recognition technology. For example, if the visitor says, "This is a delivery," the analysis unit analyzes the content of the visitor's voice and confirms that it is a delivery. The analysis unit can also understand the visitor's requirements using natural language processing technology. For example, the analysis unit extracts keywords from the visitor's voice and understands the requirements based on the keywords. The understanding unit understands the visitor's requirements based on the voice analyzed by the analysis unit. The understanding unit understands the visitor's requirements using, for example, keyword extraction technology. For example, if the visitor says, "This is a delivery," the understanding unit understands the content of the visitor's voice and confirms that it is a delivery. The understanding unit can also understand the visitor's requirements using context analysis technology. For example, the understanding unit analyzes the context from the visitor's voice and understands the requirements based on the context. The recognition unit recognizes the visitor's face. For example, the recognition unit recognizes the visitor's face using a facial recognition algorithm. For example, the recognition unit may capture a visitor's face with a camera and analyze the image to recognize the visitor's face. The recognition unit may also recognize the visitor's face using image processing technology. For example, the recognition unit may extract facial features of the visitor and recognize the visitor's face based on the features. The relay unit may then connect the call to a resident if the requirements understood by the understanding unit are acceptable. For example, the relay unit may notify the resident and allow the resident to choose whether to respond. For example, the relay unit may send a notification to the resident's smartphone and allow the resident to choose whether to respond. The relay unit may also send a notification to the resident's tablet and allow the resident to choose whether to respond. This allows the intercom system according to the embodiment to analyze the visitor's voice and face, understand the visitor's requirements, and then connect the call to the resident, thereby repelling fraudulent and nuisance visitors.

[0063] The intercom system includes an analysis unit that analyzes the behavior or facial expression of a visitor. The analysis unit analyzes the behavior and facial expression of a visitor. The analysis unit analyzes the behavior of a visitor using, for example, motion analysis technology. For example, the analysis unit captures the behavior of a visitor with a camera and analyzes the video to understand the behavior of the visitor. The analysis unit can also analyze the behavior of a visitor using gesture recognition technology. For example, the analysis unit analyzes the gestures of a visitor and understands the behavior of the visitor based on the gestures. The analysis unit can also analyze the facial expression of a visitor using facial expression recognition technology. For example, the analysis unit captures the facial expression of a visitor with a camera and analyzes the video to understand the facial expression of the visitor. The analysis unit can also analyze the facial expression of a visitor using emotion analysis technology. For example, the analysis unit infers emotions from the facial expression of a visitor and understands the facial expression based on the emotions. In this way, by analyzing the behavior and facial expression of a visitor, the intention of the visitor can be understood more accurately.

[0064] The analysis unit can record the visitor's voice. The analysis unit records the visitor's voice. The analysis unit, for example, saves the visitor's voice as digital data. For example, the analysis unit records the visitor's voice and saves the voice data in digital format. The analysis unit can also record the visitor's voice in real time. For example, the analysis unit records the visitor's voice in real time and saves the voice data in digital format. The analysis unit can also save the visitor's voice in the cloud. For example, the analysis unit uploads the visitor's voice data to cloud storage so that it can be checked later. In this way, by recording the visitor's voice, it can be checked later.

[0065] The recognition unit can automatically reject a visitor if the visitor has been registered as a nuisance visitor in the past. The recognition unit automatically rejects a visitor if the visitor has been registered as a nuisance visitor in the past. The recognition unit, for example, stores past visit history in a database and identifies nuisance visitors by comparing the image with the database. For example, the recognition unit may take a photo of the visitor's face with a camera and compare the image with the database to identify nuisance visitors. The recognition unit can also identify nuisance visitors based on specific behavioral patterns. For example, the recognition unit may analyze the visitor's behavioral pattern and automatically reject the visitor if the pattern matches the pattern of a nuisance visitor. This makes it possible to ensure the safety of residents by automatically rejecting nuisance visitors in the past.

[0066] The relay unit notifies the resident and allows the resident to choose whether or not to respond to the call. The relay unit notifies the resident and allows the resident to choose whether or not to respond to the call. The relay unit, for example, sends a notification to the resident's smartphone and allows the resident to choose whether or not to respond to the call. For example, the relay unit sends a push notification to the resident's smartphone and allows the resident to choose whether or not to respond to the call. The relay unit can also send a notification to the resident's tablet and allows the resident to choose whether or not to respond to the call. For example, the relay unit can send a push notification to the resident's tablet and allows the resident to choose whether or not to respond to the call. The relay unit can also send a notification to the resident's smartwatch and allows the resident to choose whether or not to respond to the call. For example, the relay unit can send a push notification to the resident's smartwatch and allows the resident to choose whether or not to respond to the call. This allows the resident to choose whether or not to respond, thereby reducing the burden on the resident.

[0067] The analysis unit can estimate the visitor's emotions and adjust the accuracy of the voice analysis based on the estimated visitor's emotions. The analysis unit estimates the visitor's emotions and adjusts the accuracy of the voice analysis based on the estimated visitor's emotions. The analysis unit estimates the visitor's emotions using, for example, voice tone analysis technology. For example, the analysis unit analyzes the visitor's voice tone and estimates the visitor's emotions based on the tone. The analysis unit can also estimate the visitor's emotions using facial expression analysis technology. For example, the analysis unit captures the visitor's facial expressions with a camera and analyzes the video to estimate the visitor's emotions. Furthermore, the analysis unit adjusts the accuracy of the voice analysis based on the estimated visitor's emotions. For example, if the visitor is nervous, the accuracy of the voice analysis is increased to capture even subtle changes in voice. If the visitor is relaxed, the accuracy of the voice analysis is kept normal, emphasizing natural conversation. Furthermore, if the visitor is angry, the accuracy of the voice analysis is increased to capture changes in emotions in detail. In this way, the accuracy of the voice analysis is adjusted according to the visitor's emotions, thereby improving the accuracy of the analysis.

[0068] The analysis unit can remove background noise from the visitor's voice to improve the accuracy of the analysis. The analysis unit can remove background noise from the visitor's voice to improve the accuracy of the analysis. The analysis unit can remove background noise from the visitor's voice, for example, using noise filtering technology. For example, the analysis unit can filter the visitor's voice in real time to remove background noise. The analysis unit can also record the visitor's voice and perform noise removal processing later. For example, the analysis unit can record the visitor's voice and process the voice data later using noise filtering technology. Furthermore, the analysis unit can collect the visitor's voice using multiple microphones and remove background noise using noise canceling technology. For example, the analysis unit can analyze voice data collected by multiple microphones and remove background noise using noise canceling technology. This removes background noise, improving the accuracy of the voice analysis.

[0069] The analysis unit can analyze the tone and speed of the caller's voice to determine the caller's level of urgency. The analysis unit can analyze the tone and speed of the caller's voice to determine the caller's level of urgency. The analysis unit can analyze the tone of the caller's voice using, for example, voice pitch analysis technology. For example, the analysis unit can analyze the pitch of the caller's voice and determine the caller's level of urgency based on the pitch. The analysis unit can also analyze the speed of the caller's voice using speech rate analysis technology. For example, the analysis unit can analyze the speed of the caller's voice and determine the caller's level of urgency based on the speed. The analysis unit can also analyze changes in the tone and speed of the caller's voice to determine changes in the level of urgency in real time. For example, the analysis unit can analyze changes in the tone and speed of the caller's voice and determine changes in the level of urgency in real time based on the changes. In this way, the caller's level of urgency can be determined by analyzing the tone and speed of the voice.

[0070] The analysis unit can estimate the visitor's emotion and determine the priority of the analysis results based on the estimated visitor's emotion. The analysis unit estimates the visitor's emotion and determines the priority of the analysis results based on the estimated visitor's emotion. The analysis unit estimates the visitor's emotion using, for example, voice tone analysis technology. For example, the analysis unit analyzes the visitor's voice tone and estimates the visitor's emotion based on the tone. The analysis unit can also estimate the visitor's emotion using facial expression analysis technology. For example, the analysis unit captures the visitor's facial expression with a camera and analyzes the video to estimate the visitor's emotion. Furthermore, the analysis unit determines the priority of the analysis results based on the estimated visitor's emotion. For example, if the visitor is nervous, the analysis result can be set to a high priority and a quick response can be made. Furthermore, if the visitor is relaxed, the analysis result can be set to a normal priority. Furthermore, if the visitor is angry, the analysis result can be set to a high priority and a quick response can be made. In this way, by determining the priority of the analysis results according to the visitor's emotion, a quick response can be made.

[0071] The analysis unit analyzes the visitor's voice in multiple languages ​​and can accommodate visitors who speak different languages. The analysis unit analyzes the visitor's voice in multiple languages ​​and can accommodate visitors who speak different languages. The analysis unit analyzes the visitor's voice in multiple languages ​​using, for example, real-time translation technology. For example, the analysis unit translates the visitor's voice in real time and analyzes the translation result. The analysis unit can also record the visitor's voice and perform multilingual analysis later. For example, the analysis unit records the visitor's voice and later analyzes the voice data using multilingual translation technology. Furthermore, the analysis unit can analyze the visitor's voice in multiple languages ​​and respond in the most appropriate language. For example, the analysis unit analyzes the visitor's voice in multiple languages ​​and selects the most appropriate language from among them to respond. In this way, multilingual analysis can accommodate visitors who speak different languages.

[0072] The analysis unit can translate the visitor's voice in real time and notify the resident. The analysis unit translates the visitor's voice in real time and notifies the resident. The analysis unit translates the visitor's voice using, for example, real-time translation technology. For example, the analysis unit translates the visitor's voice in real time and notifies the resident of the translation result. The analysis unit can also translate the visitor's voice in real time and notify the resident of the translation result as a text message. For example, the analysis unit translates the visitor's voice in real time and sends the translation result as a text message to the resident's smartphone. The analysis unit can also translate the visitor's voice in real time and notify the resident of the translation result as a voice message. For example, the analysis unit translates the visitor's voice in real time and sends the translation result as a voice message to the resident's smartphone. This allows the resident to instantly understand the visitor's requirements by translating in real time.

[0073] The understanding unit can estimate the visitor's emotions and adjust the level of understanding of the requirements based on the estimated visitor's emotions. The understanding unit can estimate the visitor's emotions and adjust the level of understanding of the requirements based on the estimated visitor's emotions. The understanding unit estimates the visitor's emotions using, for example, voice tone analysis technology. For example, the understanding unit analyzes the visitor's voice tone and estimates the visitor's emotions based on the tone. The understanding unit can also estimate the visitor's emotions using facial expression analysis technology. For example, the understanding unit captures the visitor's facial expressions with a camera and analyzes the video to estimate the visitor's emotions. Furthermore, the understanding unit adjusts the level of understanding of the requirements based on the estimated visitor's emotions. For example, if the visitor is nervous, the level of understanding of the requirements is increased and detailed information is collected. Also, if the visitor is relaxed, the level of understanding of the requirements is kept normal. Furthermore, if the visitor is angry, the level of understanding of the requirements is increased and detailed information is collected. In this way, detailed information can be collected by adjusting the level of understanding of the requirements according to the visitor's emotions.

[0074] The understanding unit can classify the visitor's requirements by category to improve the accuracy of understanding. The understanding unit classifies the visitor's requirements by category to improve the accuracy of understanding. The understanding unit classifies the visitor's requirements using, for example, a category classification algorithm. For example, the understanding unit classifies the visitor's requirements into categories such as "delivery service," "sales," and "door-to-door sales." The understanding unit can also classify the visitor's requirements into categories such as "urgent" and "non-urgent." For example, the understanding unit classifies the visitor's requirements into categories such as "urgent" and "non-urgent," and understands the requirements based on the categories. The understanding unit can also classify the visitor's requirements into categories such as "individual" and "corporation." For example, the understanding unit classifies the visitor's requirements into categories such as "individual" and "corporation," and understands the requirements based on the categories. In this way, by classifying the requirements by category, the accuracy of understanding is improved.

[0075] The understanding unit can compare the visitor's requirements with past data to identify similar requirements. The understanding unit compares the visitor's requirements with past data to identify similar requirements. The understanding unit compares with past data, for example, using database search technology. For example, the understanding unit compares the visitor's requirements with past requirements stored in a database to identify similar requirements. The understanding unit can also propose an optimal response method by comparison with past data. For example, the understanding unit compares the visitor's requirements with past data and proposes an optimal response method based on the result. Furthermore, the understanding unit can also identify problematic requirements by comparison with past data. For example, the understanding unit compares the visitor's requirements with past data and identify problematic requirements based on the result. In this way, similar requirements can be identified by comparing with past data.

[0076] The understanding unit can estimate the visitor's emotions and determine the priority of requirements based on the estimated visitor's emotions. The understanding unit estimates the visitor's emotions and determines the priority of requirements based on the estimated visitor's emotions. The understanding unit estimates the visitor's emotions using, for example, voice tone analysis technology. For example, the understanding unit analyzes the visitor's voice tone and estimates the visitor's emotions based on the tone. The understanding unit can also estimate the visitor's emotions using facial expression analysis technology. For example, the understanding unit captures the visitor's facial expressions with a camera and analyzes the video to estimate the visitor's emotions. Furthermore, the understanding unit determines the priority of requirements based on the estimated visitor's emotions. For example, if the visitor is nervous, the priority of the requirement is set high. Also, if the visitor is relaxed, the priority of the requirement is set normal. Furthermore, if the visitor is angry, the priority of the requirement is set high. In this way, by determining the priority of requirements according to the visitor's emotions, a quick response is possible.

[0077] The understanding unit can convert the visitor's requirements into text and notify the resident. The understanding unit converts the visitor's requirements into text and notifies the resident. The understanding unit converts the visitor's requirements into text, for example, using voice recognition technology. For example, the understanding unit converts the visitor's voice into text in real time and notifies the resident of the text. The understanding unit can also record the visitor's voice, convert it to text later, and notify the resident. For example, the understanding unit records the visitor's voice, converts the voice data later into text, and sends it to the resident's smartphone. The understanding unit can also convert the visitor's requirements into text and notify the resident of the text to their device. For example, the understanding unit converts the visitor's requirements into text and sends the text to the resident's smartphone or tablet. In this way, by converting the requirements into text, the resident can instantly understand the visitor's requirements.

[0078] The understanding unit can understand the visitor's requirements not only from their voice but also from their gestures and facial expressions. The understanding unit can understand the visitor's requirements not only from their voice but also from their gestures and facial expressions. The understanding unit can, for example, analyze the visitor's gestures using motion analysis technology. For example, the understanding unit can capture the visitor's gestures with a camera and analyze the video to understand the visitor's requirements. The understanding unit can also analyze the visitor's facial expressions using facial expression recognition technology. For example, the understanding unit can capture the visitor's facial expressions with a camera and analyze the video to understand the visitor's requirements. The understanding unit can also understand the visitor's requirements by comprehensively analyzing the visitor's voice, gestures, and facial expressions. For example, the understanding unit can comprehensively analyze the visitor's voice, gestures, and facial expressions and understand the requirements based on the results. This enables more accurate responses by understanding the requirements not only from their voice but also from their gestures and facial expressions.

[0079] The recognition unit can estimate the visitor's emotion and adjust the accuracy of facial recognition based on the estimated visitor's emotion. The recognition unit estimates the visitor's emotion and adjusts the accuracy of facial recognition based on the estimated visitor's emotion. The recognition unit estimates the visitor's emotion using, for example, voice tone analysis technology. For example, the recognition unit analyzes the visitor's voice tone and estimates the visitor's emotion based on the tone. The recognition unit can also estimate the visitor's emotion using facial expression analysis technology. For example, the recognition unit captures the visitor's facial expression with a camera and analyzes the video to estimate the visitor's emotion. The recognition unit further adjusts the accuracy of facial recognition based on the estimated visitor's emotion. For example, if the visitor is nervous, the accuracy of facial recognition is increased to capture even subtle changes in facial expression. If the visitor is relaxed, the accuracy of facial recognition is kept normal and natural expressions are emphasized. If the visitor is angry, the accuracy of facial recognition is increased to capture changes in emotion in detail. In this way, the accuracy of facial recognition is improved by adjusting the accuracy of facial recognition according to the visitor's emotion.

[0080] The recognition unit can improve the accuracy of recognition by photographing the visitor's face from multiple angles. The recognition unit can improve the accuracy of recognition by photographing the visitor's face from multiple angles. The recognition unit, for example, arranges multiple cameras to photograph the visitor's face. For example, the recognition unit photographs the visitor's face from the front and analyzes the image to recognize it. The recognition unit can also photograph the visitor's face from left and right angles and analyze the image to recognize it. Furthermore, the recognition unit can photograph the visitor's face from above and below angles and analyze the image to recognize it. For example, the recognition unit photographs the visitor's face from above and below angles and analyze the image to recognize it. In this way, by photographing the visitor's face from multiple angles, the accuracy of facial recognition is improved.

[0081] The recognition unit can check the visitor's face against a past database to confirm the visitor's history. The recognition unit can check the visitor's face against a past database to confirm the visitor's history. The recognition unit can check against the past database using, for example, database search technology. For example, the recognition unit can take a picture of the visitor's face with a camera and check the image against a database to confirm the visitor's history. The recognition unit can also identify nuisance visitors based on the past visit history. For example, the recognition unit can check the visitor's face against a database and identify nuisance visitors based on the result. Furthermore, the recognition unit can also check the visitor's frequency of visits. For example, the recognition unit can check the visitor's face against a database and check the visitor's frequency of visits based on the result. In this way, the visitor's history can be confirmed by checking against the past database.

[0082] The recognition unit can estimate the visitor's emotion and determine the priority of the recognition results based on the estimated visitor's emotion. The recognition unit estimates the visitor's emotion and determines the priority of the recognition results based on the estimated visitor's emotion. The recognition unit estimates the visitor's emotion using, for example, voice tone analysis technology. For example, the recognition unit analyzes the visitor's voice tone and estimates the visitor's emotion based on the tone. The recognition unit can also estimate the visitor's emotion using facial expression analysis technology. For example, the recognition unit captures the visitor's facial expression with a camera and analyzes the video to estimate the visitor's emotion. Furthermore, the recognition unit determines the priority of the recognition results based on the estimated visitor's emotion. For example, if the visitor is nervous, the recognition result is set to a high priority. Also, if the visitor is relaxed, the recognition result is set to a normal priority. Furthermore, if the visitor is angry, the recognition result is set to a high priority. This enables a quick response by determining the priority of the recognition results according to the visitor's emotion.

[0083] The recognition unit can recognize the visitor's face in real time and notify the resident. The recognition unit recognizes the visitor's face in real time and notifies the resident. The recognition unit recognizes the visitor's face using, for example, real-time facial recognition technology. For example, the recognition unit captures the visitor's face with a camera and analyzes and recognizes the image in real time. The recognition unit can also notify the resident of the recognition result as a text message. For example, the recognition unit recognizes the visitor's face in real time and sends the recognition result as a text message to the resident's smartphone. The recognition unit can also notify the resident of the recognition result as a voice message. For example, the recognition unit recognizes the visitor's face in real time and sends the recognition result as a voice message to the resident's smartphone. This allows the resident to immediately understand the visitor's requirements through real-time recognition.

[0084] The recognition unit can recognize not only the visitor's face but also their clothing and belongings, which can be useful in identifying the visitor. The recognition unit can recognize not only the visitor's face but also their clothing and belongings, which can be useful in identifying the visitor. The recognition unit, for example, uses object recognition technology to recognize the visitor's clothing. For example, the recognition unit photographs the visitor's clothing with a camera and recognizes the image by analyzing it. The recognition unit can also recognize the visitor's belongings using object recognition technology. For example, the recognition unit photographs the visitor's belongings with a camera and recognizes the image by analyzing it. The recognition unit can also comprehensively recognize the visitor's face, clothing, and belongings, which can be useful in identifying the visitor. For example, the recognition unit comprehensively recognizes the visitor's face, clothing, and belongings, and identifies the visitor based on the results. In this way, by recognizing not only the face but also the clothing and belongings, the visitor can be identified more accurately.

[0085] The mediation unit can estimate the visitor's emotions and adjust the mediation method based on the estimated visitor's emotions. The mediation unit estimates the visitor's emotions and adjusts the mediation method based on the estimated visitor's emotions. The mediation unit estimates the visitor's emotions using, for example, voice tone analysis technology. For example, the mediation unit analyzes the visitor's voice tone and estimates the visitor's emotions based on the tone. The mediation unit can also estimate the visitor's emotions using facial expression analysis technology. For example, the mediation unit captures the visitor's facial expressions with a camera and analyzes the video to estimate the visitor's emotions. Furthermore, the mediation unit adjusts the mediation method based on the estimated visitor's emotions. For example, if the visitor is nervous, the mediation method is made polite and the visitor feels at ease. Furthermore, if the visitor is relaxed, the mediation method is kept normal. Furthermore, if the visitor is angry, the mediation method is made quicker and the problem is resolved early. This allows for more appropriate responses by adjusting the transfer method depending on the visitor's emotions.

[0086] When transferring a call, the relay unit can select an appropriate timing by taking into consideration the resident's schedule. When transferring a call, the relay unit selects an appropriate timing by taking into consideration the resident's schedule. The relay unit, for example, checks the resident's schedule using calendar information. For example, the relay unit references the resident's calendar information and transfers the call at the optimal timing. The relay unit can also adjust the timing of the transfer by taking into consideration the resident's schedule. For example, the relay unit checks the resident's schedule and adjusts the timing of the transfer based on that schedule. Furthermore, the relay unit can check the resident's schedule in real time and transfer the call at the optimal timing. For example, the relay unit checks the resident's schedule in real time and transfers the call at the optimal timing based on that schedule. In this way, the relay unit can transfer the call at the optimal timing by taking the resident's schedule into consideration.

[0087] The mediation unit can determine the priority level based on the urgency of the visitor's requirements when forwarding the request. The mediation unit determines the priority level based on the urgency of the visitor's requirements when forwarding the request. The mediation unit, for example, analyzes the content of the requirements to evaluate the urgency. For example, the mediation unit analyzes the visitor's requirements and evaluates the urgency based on the content. The mediation unit can also evaluate the urgency by taking time constraints into consideration. For example, the mediation unit analyzes the time constraints of the visitor's requirements and evaluates the urgency based on the constraints. Furthermore, the mediation unit determines the priority level based on the urgency of the visitor's requirements. For example, if the visitor's requirements are urgent, the mediation unit prioritizes the request. Furthermore, if the visitor's requirements are non-urgent, the mediation unit prioritizes the request. Furthermore, the mediation unit can analyze the urgency of the visitor's requirements and forward the request at the optimal priority level. This enables a quick response by determining the priority level based on the urgency of the visitor's requirements.

[0088] The mediation unit can estimate the visitor's emotions and determine the priority of the call based on the estimated emotion of the visitor. The mediation unit estimates the visitor's emotions and determines the priority of the call based on the estimated emotion of the visitor. The mediation unit estimates the visitor's emotions using, for example, voice tone analysis technology. For example, the mediation unit analyzes the visitor's voice tone and estimates the visitor's emotions based on the tone. The mediation unit can also estimate the visitor's emotions using facial expression analysis technology. For example, the mediation unit captures the visitor's facial expressions with a camera and analyzes the video to estimate the visitor's emotions. Furthermore, the mediation unit determines the priority of the call based on the estimated emotion of the visitor. For example, if the visitor is nervous, the mediation priority is set to high. Also, if the visitor is relaxed, the mediation priority is set to normal. Furthermore, if the visitor is angry, the mediation priority is set to high. In this way, by determining the priority of the call based on the visitor's emotions, a quick response is possible.

[0089] When transferring a call, the relay unit can send a notification to the resident's device and provide options for how to respond. When transferring a call, the relay unit can send a notification to the resident's device and provide options for how to respond. The relay unit can, for example, send a notification to the resident's smartphone and provide options for how to respond. For example, the relay unit can send a push notification to the resident's smartphone and provide options for how to respond. The relay unit can also send a notification to the resident's tablet and provide options for how to respond. For example, the relay unit can send a push notification to the resident's tablet and provide options for how to respond. The relay unit can also send a notification to the resident's smartwatch and provide options for how to respond. For example, the relay unit can send a push notification to the resident's smartwatch and provide options for how to respond. In this way, by sending a notification to the resident's device, the resident can have options for how to respond.

[0090] The mediation unit can transmit the visitor's requirements to the resident as a text message when transferring the visitor. The mediation unit transmits the visitor's requirements to the resident as a text message when transferring the visitor. The mediation unit, for example, transmits the visitor's requirements as a text message to the resident's smartphone. For example, the mediation unit transmits the visitor's requirements as a text message to the resident's smartphone, allowing the resident to immediately understand the requirements. The mediation unit can also transmit the visitor's requirements as a text message to the resident's tablet. For example, the mediation unit transmits the visitor's requirements as a text message to the resident's tablet, allowing the resident to immediately understand the requirements. The mediation unit can also transmit the visitor's requirements as a text message to the resident's smartwatch. For example, the mediation unit transmits the visitor's requirements as a text message to the resident's smartwatch, allowing the resident to immediately understand the requirements. In this way, by transmitting the visitor's requirements as a text message, the resident can immediately understand the requirements. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, understanding unit, recognition unit, and relay unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the visitor's voice. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and understands the visitor's requirements based on the analyzed voice. The recognition unit recognizes the visitor's face using the camera 42 of the smart device 14. The relay unit is realized by the control unit 46A of the smart device 14 and notifies the resident if the understood requirements are acceptable. The analysis unit analyzes the visitor's behavior and facial expression using the camera 42 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned analysis unit, understanding unit, recognition unit, and intermediation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the visitor's voice. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and understands the visitor's requirements based on the analyzed voice. The recognition unit recognizes the visitor's face using the camera 42 of the smart glasses 214. The intermediation unit is realized by the control unit 46A of the smart glasses 214 and notifies the resident if the understood requirements are acceptable. The analysis unit analyzes the visitor's behavior and facial expressions using the camera 42 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, understanding unit, recognition unit, and intermediation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset terminal 314 and analyzes the visitor's voice. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and understands the visitor's requirements based on the analyzed voice. The recognition unit recognizes the visitor's face using the camera 42 of the headset terminal 314. The intermediation unit is realized by the control unit 46A of the headset terminal 314 and notifies the resident if the understood requirements are acceptable. The analysis unit analyzes the visitor's behavior and facial expressions using the camera 42 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, understanding unit, recognition unit, and mediation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the visitor's voice. The understanding unit is realized by the specific processing unit 290 of the data processing device 12 and understands the visitor's requirements based on the analyzed voice. The recognition unit recognizes the visitor's face using the camera 42 of the robot 414. The mediation unit is realized by the control unit 46A of the robot 414 and notifies the resident if the understood requirements are acceptable. The analysis unit analyzes the visitor's behavior and facial expressions using the camera 42 of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit not only analyzes the visitor's voice, but also analyzes the visitor's background sounds to estimate the visitor's environment. For example, the analysis unit analyzes the sounds of cars and people talking in the background of the visitor's voice to estimate whether the visitor is outdoors or indoors. The analysis unit can also estimate the weather from the visitor's background sounds. For example, if the sound of rain is heard, it estimates that the visitor is in the rain. Furthermore, the analysis unit can also estimate the visitor's activity from the visitor's background sounds. For example, if the sound of exercise is heard in the background of the visitor, it estimates that the visitor is exercising. This allows for more appropriate response by estimating the visitor's environment.

[0093] The analysis unit not only analyzes the visitor's behavior and facial expressions, but also analyzes the visitor's clothing and belongings to infer the visitor's attributes. For example, the analysis unit can infer the visitor's occupation from the visitor's clothing. For example, if the visitor is wearing a suit, it can infer that the visitor is a businessman. The analysis unit can also infer the visitor's purpose from the visitor's belongings. For example, if the visitor is carrying a parcel, it can infer that the visitor is a delivery person. Furthermore, the analysis unit can infer the visitor's age group from the visitor's clothing and belongings. For example, if the visitor is wearing a school uniform, it can infer that the visitor is a student. In this way, by inferring the visitor's attributes, more appropriate responses can be made.

[0094] The analysis unit not only records the visitor's voice, but also analyzes the visitor's voice and extracts characteristics of the visitor's speaking style. For example, the analysis unit analyzes the speed and rhythm of the visitor's speech and extracts these characteristics. The analysis unit can also analyze the accent and intonation of the visitor's speech and extract these characteristics. Furthermore, the analysis unit can save the characteristics of the visitor's speaking style in a database and check them later. For example, saving the characteristics of the visitor's speaking style in a database and comparing it with past visitors can help identify the visitor. In this way, extracting the characteristics of the visitor's speaking style enables more accurate response.

[0095] The recognition unit not only automatically rejects visitors who have been registered as nuisance visitors in the past, but also analyzes the visitor's behavioral patterns to detect signs of a nuisance visitor. For example, the recognition unit analyzes the visitor's behavioral patterns and, if those patterns match those of past nuisance visitors, becomes alert to the visitor. The recognition unit can also analyze the visitor's behavioral patterns in real time to detect abnormal behavior. For example, if a visitor stands in front of the intercom for a long period of time, it determines that behavior to be abnormal. Furthermore, the recognition unit can save the visitor's behavioral patterns in a database and check them later. In this way, by analyzing the visitor's behavioral patterns, signs of a nuisance visitor can be detected and the safety of residents can be ensured.

[0096] The relay unit not only notifies the resident and allows the resident to decide whether or not to respond, but also refers to the resident's past response history and suggests the optimal response method. For example, the relay unit stores the resident's past response history in a database and refers to that history to suggest the optimal response method. The relay unit can also analyze the resident's past response history and learn the resident's response patterns. For example, if a resident tends to respond in a specific way to certain visitors, the relay unit can learn that pattern. Furthermore, the relay unit can provide the resident with response advice based on the resident's past response history. This makes it possible to respond more appropriately by referring to the resident's past response history.

[0097] The analysis unit not only estimates the visitor's emotions and adjusts the accuracy of the voice analysis based on the estimated visitor's emotions, but also adjusts the tone of the response based on the visitor's emotions. For example, if the visitor is nervous, the analysis unit will soften the tone of the response to give a sense of security. If the visitor is relaxed, the analysis unit will keep the tone of the response normal. Furthermore, if the visitor is angry, the analysis unit will calm the tone of the response to quickly resolve the problem. This makes it possible to respond more appropriately by adjusting the tone of the response according to the visitor's emotions.

[0098] The analysis unit not only removes background noise from the visitor's voice and improves the accuracy of the analysis, but also removes echoes from the visitor's voice. For example, the analysis unit may use echo cancellation technology to remove echoes from the visitor's voice. The analysis unit may also record the visitor's voice and later perform echo removal processing. For example, the analysis unit may record the visitor's voice and later process the audio data using echo cancellation technology. The analysis unit may also collect the visitor's voice using multiple microphones and remove echoes using echo cancellation technology. This removes echoes, thereby improving the accuracy of the voice analysis.

[0099] The analysis unit not only analyzes the tone and speed of the caller's voice to determine the caller's level of urgency, but also analyzes the content of the caller's voice to understand the details of the caller's requirements. For example, the analysis unit can extract keywords from the caller's voice and understand the details of the requirements based on those keywords. The analysis unit can also analyze the context of the caller's voice and understand the details of the requirements based on that context. Furthermore, the analysis unit can save the content of the caller's voice in a database and check it later. This makes it possible to understand the details of the requirements by analyzing the content of the caller's voice, enabling more appropriate responses.

[0100] The analysis unit not only estimates the visitor's emotions and determines the priority of the analysis results based on the estimated visitor's emotions, but also determines the priority of responses based on the visitor's emotions. For example, if the visitor is nervous, the analysis unit sets the priority of responses to be high and responds quickly. If the visitor is relaxed, the analysis unit sets the priority of responses to be normal. Furthermore, if the visitor is angry, the analysis unit sets the priority of responses to be high and responds quickly. In this way, by determining the priority of responses according to the visitor's emotions, a quick response is possible.

[0101] The analysis unit not only analyzes the visitor's voice in multiple languages ​​and can accommodate visitors who speak different languages, but can also translate the visitor's voice and notify the resident. For example, the analysis unit can translate the visitor's voice in real time and notify the resident of the translation result. The analysis unit can also record the visitor's voice, translate it later, and notify the resident. Furthermore, the analysis unit can translate the visitor's voice into multiple languages ​​and notify the resident of the translation result. This allows the resident to immediately understand the visitor's requirements by translating the visitor's voice.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The analysis unit analyzes the visitor's voice. The analysis unit uses voice recognition technology and natural language processing technology to analyze the visitor's voice, and analyzes and confirms the content, such as "This is a delivery." Step 2: The understanding unit understands the visitor's requirements based on the speech analyzed by the analysis unit. The understanding unit uses keyword extraction and context analysis techniques to understand the visitor's requirements, for example, "This is a delivery service," and confirms them. Step 3: The recognition unit recognizes the visitor's face. The recognition unit uses a facial recognition algorithm and image processing technology to take a picture of the visitor's face with a camera, analyze the image, and recognize the visitor's face. Step 4: The relay unit relays the call to the resident if the requirements understood by the understanding unit are acceptable. The relay unit sends a notification to the resident, allowing the resident to choose whether or not to respond.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] 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.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] 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.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] 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.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] 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.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 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 analysis unit that analyzes the visitor's voice; an understanding unit that understands the requirements of the visitor based on the voice analyzed by the analysis unit; a recognition unit that recognizes the face of a visitor; an intermediary unit that relays the information to the resident if there is no problem with the requirements understood by the understanding unit; Equipped with A system characterized by:

2. Equipped with an analysis unit that analyzes visitors' behavior or facial expressions 2. The system of claim 1.

3. The analysis unit Record visitor audio 2. The system of claim 1.

4. The recognition unit Automatically reject visitors if they have been registered as unwanted visitors in the past 2. The system of claim 1.

5. The intermediary unit Notify the resident and let them choose whether to respond 2. The system of claim 1.

6. The analysis unit Estimate visitor sentiment and adjust the accuracy of speech analysis based on the estimated visitor sentiment 2. The system of claim 1.

7. The analysis unit Eliminate background noise from visitor audio to improve analysis accuracy 2. The system of claim 1.

8. The analysis unit Analyze the caller's tone and speed to determine the caller's urgency 2. The system of claim 1.

9. The analysis unit Estimate visitor sentiment and prioritize analysis results based on the estimated visitor sentiment 2. The system of claim 1.

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