System

Abstract

An object of a system according to an embodiment is to quickly and accurately determine whether or not a visitor is a recipient and prevent special fraud damage.SOLUTION: A system according to an embodiment includes an intercom, an interaction unit, an analysis unit, and a reporting unit. The interphone has a built-in generation AI. The conversation part performs conversation with the visitor by a generation AI incorporated in the interphone. The analysis part analyzes the answer of the visitor obtained by the interaction part and determines whether or not the visitor is a recipient. The reporting part reports to the police when it is determined to be the recipient by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to quickly and accurately determine whether a visitor was a receiver, making it difficult to prevent damage from special frauds.

[0005] The system according to the embodiment aims to quickly and accurately determine whether a visitor is a receiver, thereby preventing damage from special fraud. [Means for solving the problem]

[0006] The system according to the embodiment comprises an intercom, a dialogue unit, an analysis unit, and a reporting unit. The intercom has a built-in generation AI. The dialogue unit dialogues with the visitor using the built-in generation AI. The analysis unit analyzes the visitor's response obtained by the dialogue unit and determines whether or not the visitor is a receiver. The reporting unit reports to the police if the analysis unit determines that the visitor is a receiver. [Effects of the Invention]

[0007] The system according to the embodiment can quickly and accurately determine whether a visitor is a receiver, thereby preventing damage from special fraud. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 the embodiment of the present invention is a system that has a built-in generation AI and determines whether a visitor is a receiver through dialogue with the visitor, thereby eradicating special frauds. As a result, the intercom system analyzes the visitor's dialogue and reports to the police if there is a possibility that the visitor is a receiver, thereby preventing special frauds before they occur.

[0029] An intercom system according to an embodiment includes an intercom incorporating a generation AI, a dialogue unit, an analysis unit, and a reporting unit. The intercom incorporating a generation AI automatically responds when a visitor uses the intercom. For example, the generation AI responds to the visitor by saying, "Hello, this is the automated answering system. How can I help you?" The dialogue unit uses the generation AI to converse with the visitor. For example, the dialogue unit understands what the visitor is saying and generates appropriate questions. The dialogue unit can also convert the visitor's response into audio and convey it to the visitor. The analysis unit analyzes the visitor's response obtained by the dialogue unit and determines whether the visitor is a receiver. For example, if the visitor responds, "I'm here to pick up my package," the analysis unit analyzes the response and determines whether the visitor is likely a receiver. The analysis unit can also learn the characteristics of a receiver based on the visitor's response. The reporting unit notifies the police if the analysis unit determines that the visitor is a receiver. For example, the reporting unit notifies the police via a text message or voice message generated by the generation AI. The reporting unit can also record a photo of the visitor's face and voice data and provide them to the police. This allows the intercom system according to the embodiment to identify the visitor as a receiver through dialogue with the visitor and report the information to the police, thereby eradicating special frauds. For example, if a visitor attempts to commit fraud by pretending to receive a package, the generation AI can identify the visitor as a receiver and report the information to the police, preventing the fraud. Furthermore, recording visitor information and updating the database can also help prevent future frauds.

[0030] The dialogue unit can analyze the background sounds of the visitor and understand the surrounding environment and situation to help make a judgment. The dialogue unit, for example, analyzes the background sounds of the visitor to understand the surrounding environment. For example, if the sound of cars or people talking can be heard, it determines that the visitor is outside. The dialogue unit also analyzes the background sounds of the visitor to understand the situation. For example, it determines whether the visitor is in a quiet place or a noisy place. In this way, by analyzing the background sounds of the visitor, the receiver's judgment accuracy can be improved.

[0031] The dialogue unit can make the content of visitor dialogue multilingual, making it possible to accommodate foreign visitors as well. The dialogue unit is equipped with a real-time translation function, for example, to make the content of visitor dialogue multilingual. For example, it can accommodate multiple languages ​​such as English and Chinese. The dialogue unit can also accommodate foreign visitors. For example, if a visitor speaks in a foreign language, it can translate the content and respond appropriately. This multilingual support makes it possible to accommodate foreign visitors as well.

[0032] The dialogue unit can add a function to convert the visitor's conversation into text and notify the landlord in real time. The dialogue unit can add a function to convert the visitor's conversation into text in real time and notify the landlord in real time. For example, the dialogue unit can send what the visitor said as a text message to the landlord's smartphone. The dialogue unit can also convert the visitor's conversation into text and notify the landlord, allowing the landlord to respond quickly. By notifying the landlord of the visitor's conversation in real time, the landlord can respond quickly.

[0033] The analysis unit can compare the content of the visitor's response with past data and learn the receiver's patterns to improve the accuracy of judgment. The analysis unit, for example, compares the content of the visitor's response with past data and learns the receiver's patterns. For example, the analysis unit stores the past receiver's response patterns in a database and compares them with the responses of new visitors. The analysis unit also improves the accuracy of judgment by learning the receiver's patterns. For example, the analysis unit learns the receiver's characteristics based on past data and analyzes the responses of new visitors. In this way, the receiver's judgment accuracy is improved by comparing it with past data.

[0034] The analysis unit can analyze keywords included in the visitor's response and identify phrases that are likely to be fraudulent. The analysis unit, for example, analyzes keywords included in the visitor's response and identifies phrases that are likely to be fraudulent. For example, it detects phrases such as "receiving the package" or "I'm here on your behalf." The analysis unit also improves the accuracy of the receiver's judgment by identifying phrases that are likely to be fraudulent. For example, it compares phrases with past fraud cases and identifies phrases that are likely to be fraudulent. In this way, by analyzing keywords included in the visitor's response, it is possible to identify phrases that are likely to be fraudulent.

[0035] The analysis unit can analyze the visitor's answers not only through voice but also through a video call, and can also add facial expressions and gestures to the information used to make the judgment. For example, the analysis unit can analyze the visitor's answers through a video call, and add facial expressions and gestures to the information used to make the judgment. For example, if the visitor is nervous, the analysis unit can make a judgment based on the visitor's facial expressions and hand movements. The analysis unit can also analyze the visitor's facial expressions and gestures to improve the accuracy of the judgment of the receiver. For example, the analysis unit can analyze the visitor's facial expressions and hand movements to determine whether the visitor is a receiver. In this way, the accuracy of the judgment of the receiver can be improved by analyzing the visitor's facial expressions and gestures.

[0036] The analysis unit can compare the visitor's answers with a database on the cloud and make a judgment by comparing them with fraud cases in other regions. For example, the analysis unit compares the visitor's answers with a database on the cloud and compares them with fraud cases in other regions. For example, if the same phrases or keywords are used, it determines that there is a high possibility of fraud. The analysis unit also improves the accuracy of the receiver's judgment by comparing with fraud cases in other regions. For example, it uses a database on the cloud to compare with past fraud cases. This improves the accuracy of the receiver's judgment by comparing with fraud cases in other regions.

[0037] The reporting unit can include the visitor's voice data and conversation log in the report content, allowing the police to respond quickly. The reporting unit, for example, includes the visitor's voice data in the report content, allowing the police to respond quickly. For example, the reporting unit analyzes the visitor's tone of voice and speaking style and provides the results to the police. The reporting unit also includes a conversation log in the report content, allowing the police to respond quickly. For example, the conversation with the visitor is converted into text and provided to the police. In this way, the visitor's voice data and conversation log are included in the report content, allowing the police to respond quickly.

[0038] The reporting unit can automatically attach a photo of the visitor's face or video footage when a report is made, to help with police investigations. The reporting unit, for example, automatically attaches a photo of the visitor's face when a report is made, to help with police investigations. For example, a photo of the visitor's face taken with an intercom camera is included in the report. The reporting unit can also automatically attach video footage of the visitor when a report is made, to help with police investigations. For example, video footage taken with an intercom camera is included in the report. In this way, a photo of the visitor's face or video footage is automatically attached when a report is made, to help with police investigations.

[0039] The reporting unit can link the reporting function to the local crime prevention network and urge neighboring residents to be vigilant. For example, the reporting unit can link the reporting function to the local crime prevention network and urge neighboring residents to be vigilant. For example, the report content can be notified to a local crime prevention app. The reporting unit can also urge neighboring residents to be vigilant by linking with the local crime prevention network. For example, the report content can be displayed on a local bulletin board. In this way, by linking with the local crime prevention network, neighboring residents can be vigilant.

[0040] The reporting department can automatically translate the contents of the report so that it can also be handled by foreign police officers. The reporting department can, for example, automatically translate the contents of the report so that it can also be handled by foreign police officers. For example, the contents of the report can be translated into English or Chinese. The reporting department can also be made to handle foreign police officers. For example, the contents of the report can be provided in multiple languages. In this way, the contents of the report can be automatically translated so that it can also be handled by foreign police officers.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The intercom system can further include an ID card analysis unit that scans a visitor's ID card and analyzes the information. For example, when a visitor holds their ID card over the intercom camera, the ID card analysis unit reads the information and confirms the visitor's identity. The ID card analysis unit can also determine whether the visitor's ID card is authentic. This allows for quick and accurate visitor identity verification.

[0043] The intercom system can also be equipped with a temperature measurement unit that measures the visitor's body temperature. For example, when a visitor stands in front of the intercom, the temperature measurement unit automatically measures their temperature and issues an alarm if an abnormal temperature is detected. The temperature measurement unit can also record the visitor's temperature data and analyze it later. This allows the visitor's health status to be monitored and the spread of infectious diseases to be prevented.

[0044] The intercom system can also be equipped with a luggage analysis unit that scans visitors' luggage and analyzes its contents. For example, when a visitor holds their luggage over the intercom camera, the luggage analysis unit scans the contents to check whether it contains any dangerous items. The luggage analysis unit can also record the contents of the luggage and analyze them later. This makes it possible to confirm the safety of the luggage brought in by visitors.

[0045] The dialogue unit can make the content of visitor dialogue multilingual, making it possible to accommodate foreign visitors as well. For example, a real-time translation function can be installed to make the content of visitor dialogue multilingual. For example, it can accommodate multiple languages ​​such as English and Chinese. The dialogue unit can also accommodate foreign visitors. For example, if a visitor speaks in a foreign language, it can translate the content and respond appropriately. This multilingual support makes it possible to accommodate foreign visitors as well.

[0046] The intercom system can also include a vehicle analysis unit that acquires and analyzes the visitor's vehicle information. For example, if a visitor arrives by car, the vehicle analysis unit reads the vehicle's license plate number and records that information. The vehicle analysis unit can also check the vehicle's registration information to confirm the visitor's identity. This allows for more accurate identification of the visitor by analyzing the visitor's vehicle information.

[0047] The analysis unit can analyze keywords included in the visitor's responses and identify phrases that are likely to be fraudulent. For example, it analyzes keywords included in the visitor's responses and identifies phrases that are likely to be fraudulent. For example, it detects phrases such as "receiving a package" or "I'm here on your behalf." The analysis unit also improves the accuracy of the receiver's judgment by identifying phrases that are likely to be fraudulent. For example, it compares phrases with past fraud cases and identifies phrases that are likely to be fraudulent. In this way, by analyzing keywords included in the visitor's responses, it is possible to identify phrases that are likely to be fraudulent.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The intercom with built-in generative AI automatically responds when a visitor uses the intercom. For example, the generative AI might respond to the visitor by saying, "Hello, this is the automated answering system. How can I help you?" Step 2: The dialogue unit uses the generation AI to have a dialogue with the visitor. For example, the dialogue unit can understand what the visitor is saying and generate appropriate questions. The dialogue unit can also convert the visitor's answers into audio and convey them to the visitor. Step 3: The analysis unit analyzes the visitor's response obtained by the dialogue unit and determines whether or not the visitor is a receiver. For example, if the visitor responds, "I'm here to pick up my package," the analysis unit analyzes the response and determines whether or not the visitor is likely to be a receiver. The analysis unit can also learn the characteristics of receivers based on the visitor's response. Step 4: If the analysis unit determines that the visitor is a receiver, the reporting unit will notify the police. For example, the reporting unit may notify the police through a text message or voice message generated by the generation AI. The reporting unit may also record a photo of the visitor's face and voice data and provide them to the police.

[0050] (Example 2) The intercom system according to the embodiment of the present invention is a system that has a built-in generation AI and determines whether a visitor is a receiver through dialogue with the visitor, thereby eradicating special frauds. As a result, the intercom system analyzes the visitor's dialogue and reports to the police if there is a possibility that the visitor is a receiver, thereby preventing special frauds before they occur.

[0051] An intercom system according to an embodiment includes an intercom incorporating a generation AI, a dialogue unit, an analysis unit, and a reporting unit. The intercom incorporating a generation AI automatically responds when a visitor uses the intercom. For example, the generation AI responds to the visitor by saying, "Hello, this is the automated answering system. How can I help you?" The dialogue unit uses the generation AI to converse with the visitor. For example, the dialogue unit understands what the visitor is saying and generates appropriate questions. The dialogue unit can also convert the visitor's response into audio and convey it to the visitor. The analysis unit analyzes the visitor's response obtained by the dialogue unit and determines whether the visitor is a receiver. For example, if the visitor responds, "I'm here to pick up my package," the analysis unit analyzes the response and determines whether the visitor is likely a receiver. The analysis unit can also learn the characteristics of a receiver based on the visitor's response. The reporting unit notifies the police if the analysis unit determines that the visitor is a receiver. For example, the reporting unit notifies the police via a text message or voice message generated by the generation AI. The reporting unit can also record a photo of the visitor's face and voice data and provide them to the police. This allows the intercom system according to the embodiment to identify the visitor as a receiver through dialogue with the visitor and report the information to the police, thereby eradicating special frauds. For example, if a visitor attempts to commit fraud by pretending to receive a package, the generation AI can identify the visitor as a receiver and report the information to the police, preventing the fraud. Furthermore, recording visitor information and updating the database can also help prevent future frauds.

[0052] The dialogue unit can analyze the visitor's tone of voice and speaking patterns to detect feelings of tension or impatience and use the results in making judgments. The dialogue unit, for example, analyzes the visitor's tone of voice to detect signs of tension or impatience. For example, it monitors changes in voice pitch and speed in real time to detect abnormal patterns. The dialogue unit also analyzes the visitor's speaking patterns to detect feelings of tension or impatience. For example, it analyzes changes in speech speed and hesitation to determine changes in emotion. In this way, analyzing the visitor's emotions improves the receiver's judgment accuracy.

[0053] The dialogue unit can analyze the background sounds of the visitor and understand the surrounding environment and situation to help make a judgment. The dialogue unit, for example, analyzes the background sounds of the visitor to understand the surrounding environment. For example, if the sound of cars or people talking can be heard, it determines that the visitor is outside. The dialogue unit also analyzes the background sounds of the visitor to understand the situation. For example, it determines whether the visitor is in a quiet place or a noisy place. In this way, by analyzing the background sounds of the visitor, the receiver's judgment accuracy can be improved.

[0054] The dialogue unit can use the emotion estimation function to estimate the emotion of the visitor in real time and determine whether or not the visitor is a receiver based on changes in emotion. The dialogue unit, for example, uses the emotion estimation function to estimate the emotion of the visitor in real time. For example, it analyzes the tone of voice and speaking style of the visitor and calculates an emotion score. The dialogue unit also determines the possibility of the visitor being a receiver based on changes in emotion. For example, if the visitor is nervous, it makes a determination based on the change in emotion. In this way, by analyzing changes in the visitor's emotion in real time, the accuracy of determining whether or not the visitor is a receiver is improved.

[0055] The dialogue unit can make the content of visitor dialogue multilingual, making it possible to accommodate foreign visitors as well. The dialogue unit is equipped with a real-time translation function, for example, to make the content of visitor dialogue multilingual. For example, it can accommodate multiple languages ​​such as English and Chinese. The dialogue unit can also accommodate foreign visitors. For example, if a visitor speaks in a foreign language, it can translate the content and respond appropriately. This multilingual support makes it possible to accommodate foreign visitors as well.

[0056] The dialogue unit can add a function to convert the visitor's conversation into text and notify the landlord in real time. The dialogue unit can add a function to convert the visitor's conversation into text in real time and notify the landlord in real time. For example, the dialogue unit can send what the visitor said as a text message to the landlord's smartphone. The dialogue unit can also convert the visitor's conversation into text and notify the landlord, allowing the landlord to respond quickly. By notifying the landlord of the visitor's conversation in real time, the landlord can respond quickly.

[0057] The dialogue unit uses the emotion estimation function to generate dialogue content that corresponds to the emotion of the visitor, thereby enabling a more natural dialogue. The dialogue unit, for example, uses the emotion estimation function to generate dialogue content that corresponds to the emotion of the visitor. For example, if the visitor is nervous, the dialogue unit generates dialogue content that relaxes the visitor. The dialogue unit also generates dialogue content that corresponds to the emotion of the visitor, thereby enabling a more natural dialogue. For example, if the visitor is happy, the dialogue unit generates dialogue content that corresponds to that emotion. In this way, by generating dialogue content that corresponds to the emotion of the visitor, a more natural dialogue can be achieved.

[0058] The analysis unit can compare the content of the visitor's response with past data and learn the receiver's patterns to improve the accuracy of judgment. The analysis unit, for example, compares the content of the visitor's response with past data and learns the receiver's patterns. For example, the analysis unit stores the past receiver's response patterns in a database and compares them with the responses of new visitors. The analysis unit also improves the accuracy of judgment by learning the receiver's patterns. For example, the analysis unit learns the receiver's characteristics based on past data and analyzes the responses of new visitors. In this way, the receiver's judgment accuracy is improved by comparing it with past data.

[0059] The analysis unit can analyze keywords included in the visitor's response and identify phrases that are likely to be fraudulent. The analysis unit, for example, analyzes keywords included in the visitor's response and identifies phrases that are likely to be fraudulent. For example, it detects phrases such as "receiving the package" or "I'm here on your behalf." The analysis unit also improves the accuracy of the receiver's judgment by identifying phrases that are likely to be fraudulent. For example, it compares phrases with past fraud cases and identifies phrases that are likely to be fraudulent. In this way, by analyzing keywords included in the visitor's response, it is possible to identify phrases that are likely to be fraudulent.

[0060] The analysis unit uses the emotion estimation function to analyze the emotion of the visitor when answering, and can determine whether or not the visitor is a receiver based on the discrepancy in emotion. The analysis unit, for example, uses the emotion estimation function to analyze the emotion of the visitor when answering. For example, it determines whether the visitor is relaxed or nervous. The analysis unit also determines the possibility of a receiver based on the discrepancy in emotion. For example, if the visitor's words and actions do not match their emotion, the determination is made based on that discrepancy. In this way, by analyzing the emotion of the visitor when answering, it is possible to determine the possibility of a receiver based on the discrepancy in emotion.

[0061] The analysis unit can analyze the visitor's answers not only through voice but also through a video call, and can also add facial expressions and gestures to the information used to make the judgment. For example, the analysis unit can analyze the visitor's answers through a video call, and add facial expressions and gestures to the information used to make the judgment. For example, if the visitor is nervous, the analysis unit can make a judgment based on the visitor's facial expressions and hand movements. The analysis unit can also analyze the visitor's facial expressions and gestures to improve the accuracy of the judgment of the receiver. For example, the analysis unit can analyze the visitor's facial expressions and hand movements to determine whether the visitor is a receiver. In this way, the accuracy of the judgment of the receiver can be improved by analyzing the visitor's facial expressions and gestures.

[0062] The analysis unit can compare the visitor's answers with a database on the cloud and make a judgment by comparing them with fraud cases in other regions. For example, the analysis unit compares the visitor's answers with a database on the cloud and compares them with fraud cases in other regions. For example, if the same phrases or keywords are used, it determines that there is a high possibility of fraud. The analysis unit also improves the accuracy of the receiver's judgment by comparing with fraud cases in other regions. For example, it uses a database on the cloud to compare with past fraud cases. This improves the accuracy of the receiver's judgment by comparing with fraud cases in other regions.

[0063] The analysis unit can use the emotion estimation function to collect the homeowner's emotional reactions to the visitor's answers and use them as information for making a decision. For example, the analysis unit uses the emotion estimation function to collect the homeowner's emotional reactions to the visitor's answers. For example, if the homeowner feels anxious, that emotion is added to the information for making a decision. Furthermore, by collecting the homeowner's emotional reactions, the analysis unit improves the accuracy of the receiver's judgment. For example, the receiver's possibility is determined based on the homeowner's emotional reactions. In this way, by collecting the homeowner's emotional reactions, the receiver's judgment accuracy is improved.

[0064] The reporting unit can include the visitor's voice data and conversation log in the report content, allowing the police to respond quickly. The reporting unit, for example, includes the visitor's voice data in the report content, allowing the police to respond quickly. For example, the reporting unit analyzes the visitor's tone of voice and speaking style and provides the results to the police. The reporting unit also includes a conversation log in the report content, allowing the police to respond quickly. For example, the conversation with the visitor is converted into text and provided to the police. In this way, the visitor's voice data and conversation log are included in the report content, allowing the police to respond quickly.

[0065] The reporting unit can automatically attach a photo of the visitor's face or video footage when a report is made, to help with police investigations. The reporting unit, for example, automatically attaches a photo of the visitor's face when a report is made, to help with police investigations. For example, a photo of the visitor's face taken with an intercom camera is included in the report. The reporting unit can also automatically attach video footage of the visitor when a report is made, to help with police investigations. For example, video footage taken with an intercom camera is included in the report. In this way, a photo of the visitor's face or video footage is automatically attached when a report is made, to help with police investigations.

[0066] The reporting unit can use the emotion estimation function to convey the emotional state of the landlord to the police at the time of the report, and use it as information for determining the level of urgency. The reporting unit, for example, uses the emotion estimation function to convey the emotional state of the landlord to the police at the time of the report. For example, if the landlord is feeling anxious or scared, the reporting unit reports that emotional state to the police. The reporting unit also conveys the emotional state of the landlord to the police, and uses it as information for determining the level of urgency. For example, based on the emotional state of the landlord, the police can determine whether a prompt response is required. In this way, the emotional state of the landlord can be conveyed to the police, and used as information for determining the level of urgency.

[0067] The reporting unit can link the reporting function to the local crime prevention network and urge neighboring residents to be vigilant. For example, the reporting unit can link the reporting function to the local crime prevention network and urge neighboring residents to be vigilant. For example, the report content can be notified to a local crime prevention app. The reporting unit can also urge neighboring residents to be vigilant by linking with the local crime prevention network. For example, the report content can be displayed on a local bulletin board. In this way, by linking with the local crime prevention network, neighboring residents can be vigilant.

[0068] The reporting department can automatically translate the contents of the report so that it can also be handled by foreign police officers. The reporting department can, for example, automatically translate the contents of the report so that it can also be handled by foreign police officers. For example, the contents of the report can be translated into English or Chinese. The reporting department can also be made to handle foreign police officers. For example, the contents of the report can be provided in multiple languages. In this way, the contents of the report can be automatically translated so that it can also be handled by foreign police officers.

[0069] The reporting unit can use the emotion estimation function to provide advice to stabilize the homeowner's emotions when making a report. The reporting unit, for example, uses the emotion estimation function to provide advice to stabilize the homeowner's emotions when making a report. For example, if the homeowner is feeling anxious, the reporting unit provides advice on how to relax. Furthermore, the reporting unit provides advice to stabilize the homeowner's emotions, thereby reducing the homeowner's anxiety when making a report. For example, if the homeowner is feeling fear, the reporting unit provides advice on how to alleviate those emotions. In this way, by providing advice to stabilize the homeowner's emotions, the homeowner's anxiety when making a report is reduced.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The intercom system can further include an ID card analysis unit that scans a visitor's ID card and analyzes the information. For example, when a visitor holds their ID card over the intercom camera, the ID card analysis unit reads the information and confirms the visitor's identity. The ID card analysis unit can also determine whether the visitor's ID card is authentic. This allows for quick and accurate visitor identity verification.

[0072] The dialogue unit can analyze the visitor's tone of voice and speaking patterns to detect feelings of tension or impatience and use this information in making judgments. For example, it can analyze the visitor's tone of voice to detect signs of tension or impatience. For example, it can monitor changes in voice pitch and speed in real time to detect abnormal patterns. The dialogue unit can also analyze the visitor's speaking patterns to detect feelings of tension or impatience. For example, it can analyze changes in speech speed and hesitation to determine changes in emotion. In this way, analyzing the visitor's emotions improves the receiver's judgment accuracy.

[0073] The intercom system can also be equipped with a temperature measurement unit that measures the visitor's body temperature. For example, when a visitor stands in front of the intercom, the temperature measurement unit automatically measures their temperature and issues an alarm if an abnormal temperature is detected. The temperature measurement unit can also record the visitor's temperature data and analyze it later. This allows the visitor's health status to be monitored and the spread of infectious diseases to be prevented.

[0074] The dialogue unit uses an emotion estimation function to estimate the visitor's emotions in real time and can determine whether or not they are a receiver based on changes in their emotions. For example, it analyzes the visitor's tone of voice and speaking style to calculate an emotion score. The dialogue unit also determines the possibility of a receiver based on changes in emotion. For example, if the visitor is nervous, it makes a determination based on those changes in emotion. This improves the accuracy of determining whether or not a visitor is a receiver by analyzing changes in the visitor's emotions in real time.

[0075] The intercom system can also be equipped with a luggage analysis unit that scans visitors' luggage and analyzes its contents. For example, when a visitor holds their luggage over the intercom camera, the luggage analysis unit scans the contents to check whether it contains any dangerous items. The luggage analysis unit can also record the contents of the luggage and analyze them later. This makes it possible to confirm the safety of the luggage brought in by visitors.

[0076] The dialogue unit can make the content of visitor dialogue multilingual, making it possible to accommodate foreign visitors as well. For example, a real-time translation function can be installed to make the content of visitor dialogue multilingual. For example, it can accommodate multiple languages ​​such as English and Chinese. The dialogue unit can also accommodate foreign visitors. For example, if a visitor speaks in a foreign language, it can translate the content and respond appropriately. This multilingual support makes it possible to accommodate foreign visitors as well.

[0077] The dialogue unit uses the emotion estimation function to generate dialogue content that corresponds to the visitor's emotions, thereby enabling a more natural dialogue. For example, if the visitor is nervous, dialogue content that relaxes the visitor is generated. The dialogue unit also generates dialogue content that corresponds to the visitor's emotions, thereby enabling a more natural dialogue. For example, if the visitor is happy, dialogue content that corresponds to that emotion is generated. In this way, by generating dialogue content that corresponds to the visitor's emotions, a more natural dialogue can be achieved.

[0078] The intercom system can also include a vehicle analysis unit that acquires and analyzes the visitor's vehicle information. For example, if a visitor arrives by car, the vehicle analysis unit reads the vehicle's license plate number and records that information. The vehicle analysis unit can also check the vehicle's registration information to confirm the visitor's identity. This allows for more accurate identification of the visitor by analyzing the visitor's vehicle information.

[0079] The analysis unit can analyze keywords included in the visitor's responses and identify phrases that are likely to be fraudulent. For example, it analyzes keywords included in the visitor's responses and identifies phrases that are likely to be fraudulent. For example, it detects phrases such as "receiving a package" or "I'm here on your behalf." The analysis unit also improves the accuracy of the receiver's judgment by identifying phrases that are likely to be fraudulent. For example, it compares phrases with past fraud cases and identifies phrases that are likely to be fraudulent. In this way, by analyzing keywords included in the visitor's responses, it is possible to identify phrases that are likely to be fraudulent.

[0080] The analysis unit uses the emotion estimation function to analyze the emotion of the visitor when answering, and can determine whether or not the visitor is a receiver based on the discrepancy in emotion. For example, the emotion estimation function is used to analyze the emotion of the visitor when answering. For example, it determines whether the visitor is relaxed or nervous. The analysis unit also determines the possibility of the visitor being a receiver based on the discrepancy in emotion. For example, if the visitor's words and actions do not match their emotion, the determination is made based on that discrepancy. In this way, by analyzing the emotion of the visitor when answering, it is possible to determine the possibility of the visitor being a receiver based on the discrepancy in emotion.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The intercom with built-in generative AI automatically responds when a visitor uses the intercom. For example, the generative AI might respond to the visitor by saying, "Hello, this is the automated answering system. How can I help you?" Step 2: The dialogue unit uses the generation AI to have a dialogue with the visitor. For example, the dialogue unit can understand what the visitor is saying and generate appropriate questions. The dialogue unit can also convert the visitor's answers into audio and convey them to the visitor. Step 3: The analysis unit analyzes the visitor's response obtained by the dialogue unit and determines whether or not the visitor is a receiver. For example, if the visitor responds, "I'm here to pick up my package," the analysis unit analyzes the response and determines whether or not the visitor is likely to be a receiver. The analysis unit can also learn the characteristics of receivers based on the visitor's response. Step 4: If the analysis unit determines that the visitor is a receiver, the reporting unit will notify the police. For example, the reporting unit may notify the police through a text message or voice message generated by the generation AI. The reporting unit may also record a photo of the visitor's face and voice data and provide them to the police.

[0083] 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.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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. [Explanation of symbols]

[0150] 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 intercom with built-in AI generation, a dialogue unit that uses a generation AI built into the intercom to dialogue with visitors; an analysis unit that analyzes the visitor's response obtained by the dialogue unit and determines whether or not the visitor is a receiver; a reporting unit that reports to the police when the analyzing unit determines that the person is a receiver. A system characterized by:

2. The dialogue unit Analyzing the visitor's tone of voice and speaking patterns to detect feelings of nervousness or impatience and use this information to make decisions 2. The system of claim 1.

3. The dialogue unit Make visitor dialogue multilingual to accommodate foreign visitors 2. The system of claim 1.

4. The analysis unit The visitor's responses are compared with past data, and the recipient's patterns are learned to improve the accuracy of the judgment.

2. The system of claim 1.

5. The reporting unit The report will include the visitor's voice recording and conversation log, allowing police to respond quickly.

2. The system of claim 1.

6. The dialogue unit Using the emotion estimation function, the emotion of the visitor is estimated in real time, and based on the change in emotion, it is determined whether or not the visitor is a receiver.

2. The system of claim 1.

7. The analysis unit Using the emotion estimation function, the emotion of the visitor when answering is analyzed, and based on the emotion discrepancy, it is determined whether or not the visitor is a receiver.

2. The system of claim 1.

8. The reporting unit Using the emotion estimation function, the emotional state of the landlord is conveyed to the police when a call is made, and this information is used to determine the level of urgency.

2. The system of claim 1.

Images