system

Abstract

The system according to the embodiment aims to ensure the safety of one's home while one is away. [Solution] A system according to an embodiment includes a visitor response unit, a face recognition unit, a face recognition unit, a voice recognition unit, and a notification unit. The visitor response unit collects visitor information. The face recognition unit analyzes the information collected by the visitor response unit. The face recognition unit recognizes the visitor's face based on the information analyzed by the face recognition unit. The voice recognition unit analyzes the voice collected by the visitor response unit. The notification unit issues a notification based on the information obtained by the face recognition unit and the voice recognition unit.

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] Conventional technology does not adequately address the issue of ensuring the safety of your home while you are away, and there is room for improvement.

[0005] The system according to the embodiment aims to ensure the safety of one's home while one is away. [Means for solving the problem]

[0006] The system according to the embodiment includes a visitor correspondence unit, a face recognition unit, a face recognition unit, a voice recognition unit, and a notification unit. The visitor correspondence unit collects information about visitors. The face recognition unit analyzes the information collected by the visitor correspondence unit. The face recognition unit recognizes the face of the visitor based on the information analyzed by the face recognition unit. The voice recognition unit analyzes the voice collected by the visitor correspondence unit. The notification unit issues a notification based on the information obtained by the face recognition unit and the voice recognition unit. [Effects of the Invention]

[0007] The system according to the embodiment can ensure the safety of your home while you are away. [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) A home safety system according to an embodiment of the present invention is a system for ensuring the safety of a home while the resident is away from home. This system utilizes AI to recognize the face and voice of visitors and automatically respond appropriately. Specifically, the system comprises the following steps: First, when a visitor arrives, the visitor response unit automatically responds via the intercom and confirms the purpose of the visit. Next, the face recognition unit captures the visitor's face with a camera, and the voice recognition unit analyzes the visitor's voice. If necessary, the notification unit sends a notification to a smartphone. In addition, a package receiving device receives packages from couriers, and an alarm unit sounds an alarm if it detects a suspicious person. Furthermore, a neighborhood association response unit automatically receives circular notices, confirms the next distribution destination, and moves the notices. By linking with a smartphone, this system allows users to check the status of their home and take necessary action even when they are away from home. For example, the visitor response unit automatically responds via the intercom and confirms the purpose of the visit. The face recognition unit captures the visitor's face with a camera, and the voice recognition unit analyzes the visitor's voice. If necessary, the notification unit sends a notification to a smartphone. The package receiving device receives packages from delivery companies, and the alarm device sounds an alarm if it detects a suspicious person. The neighborhood association response department automatically receives circulars, checks the next distribution destination, and moves the circular. This allows the home safety system to ensure the safety of your home while you are away, and efficiently collect, analyze, recognize, and notify visitors' information.

[0029] A home safety system according to an embodiment includes a visitor response unit, a face recognition unit, a voice recognition unit, and a notification unit. The visitor response unit collects visitor information. The visitor information includes, but is not limited to, a name, a facial photo, and audio data. The visitor response unit collects visitor information, for example, through an intercom. The visitor response unit can also use a camera or a microphone to collect visitor information. The face recognition unit analyzes the information collected by the visitor response unit. The face recognition unit can analyze the visitor's face, for example, using an image analysis algorithm. The face recognition unit can also use deep learning technology to recognize the visitor's face. The voice recognition unit analyzes the audio collected by the visitor response unit. The voice recognition unit can analyze the visitor's voice, for example, using voice analysis technology. The voice recognition unit can also use natural language processing technology to recognize the visitor's voice. The notification unit provides a notification based on the information obtained by the face recognition unit and the voice recognition unit. The notification unit can provide a push notification to a smartphone, for example. The notification unit can also notify the user by email or by sound alert, thereby enabling the home safety system according to the embodiment to efficiently collect, analyze, recognize, and notify the user of visitor information.

[0030] The home security system includes a parcel receiving device. The parcel receiving device can automatically receive parcels from a delivery company. The parcel receiving device may include, but is not limited to, a function to accommodate the size and weight of the parcel. The parcel receiving device receives parcels, for example, using a parcel locker. The parcel receiving device can also instruct the delivery company to place the parcel in a specified location. For example, the parcel receiving device guides the delivery company to place the parcel in the specified location. This allows the parcel receiving device to automatically receive the parcel from the delivery company.

[0031] The home safety system includes an alarm device. The alarm device can sound an alarm when a suspicious person is detected. The alarm device includes, for example, but is not limited to, a type of alarm sound and a trigger condition for the alarm. For example, the alarm device can sound an alarm when a suspicious person is detected. The alarm device can also change the type of alarm sound. For example, the alarm device can adjust the volume of the alarm sound. This allows the alarm device to sound an alarm when a suspicious person is detected.

[0032] The home security system includes a camera device. The camera device can capture a visitor's face. The camera device has, for example, but is not limited to, a resolution, a capture range, and a nighttime capture function. For example, the camera device captures a visitor's face with high resolution. The camera device also has a function for capturing images at night. For example, the camera device can capture images at night using an infrared camera. This allows the camera device to capture a visitor's face.

[0033] The home safety assurance system includes a neighborhood association response unit. The neighborhood association response unit can automatically receive neighborhood association circulars, confirm the next distribution destination, and move the circulars. The neighborhood association response unit includes, for example, a method for receiving a circular and a method for confirming the next distribution destination, but is not limited to these examples. For example, the neighborhood association response unit automatically receives a circular. Furthermore, the neighborhood association response unit can confirm the next distribution destination and move the circular. For example, the neighborhood association response unit specifies a location to receive the circular and confirm the next distribution destination. This allows the neighborhood association response unit to automatically receive neighborhood association circulars, confirm the next distribution destination, and move the circular.

[0034] The visitor handling unit can analyze the visitor's past visit history and select a response method. For example, if the visitor has visited frequently in the past, the visitor handling unit can provide a friendly response and confirm the purpose of the visit. Furthermore, if it is the visitor's first visit, the visitor handling unit can provide a response that includes a detailed explanation and carefully confirm the purpose of the visit. Furthermore, if the visitor has caused problems in the past, the visitor handling unit can provide a cautious response and carefully confirm the purpose of the visit. This allows the visitor handling unit to provide an optimal response based on the visit history. The visit history includes, for example, the date and time of the visit, the purpose of the visit, and the content of past responses, but is not limited to these examples.

[0035] The visitor response unit can adjust the response content based on the visitor's current situation. For example, if the visitor visits on rainy days, the visitor response unit checks whether the visitor has an umbrella and, if necessary, guides the visitor to the location of an umbrella stand. Furthermore, if the visitor visits at night, the visitor response unit can respond in a cheerful voice and confirm the purpose of the visit. Furthermore, if the visitor visits on a hot day, the visitor response unit can guide the visitor to wait in a cooler place and confirm the purpose of the visit. This enables the visitor response unit to respond appropriately according to the visitor's situation. The current situation includes, for example, weather information, time of day, surrounding environment, etc., but is not limited to such examples.

[0036] The visitor response unit can prioritize relevant responses by taking into account the visitor's geographical location information. For example, if the visitor is a local resident, the visitor response unit can provide a friendly response and confirm the purpose of the visit. Furthermore, if the visitor is from a distant location, the visitor response unit can provide a response including a detailed explanation and carefully confirm the purpose of the visit. Furthermore, if the visitor is from a specific area, the visitor response unit can provide a response including information related to that area and confirm the purpose of the visit. This enables the visitor response unit to provide an appropriate response based on the visitor's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0037] The visitor correspondence unit can analyze the visitor's social media activity and reflect related information in the response. For example, if the visitor is participating in a specific event on social media, the visitor correspondence unit can send a response including information related to the event and confirm the purpose of the visit. Furthermore, if the visitor has expressed a specific interest on social media, the visitor correspondence unit can send a response including information related to that interest and confirm the purpose of the visit. Furthermore, if the visitor has a specific problem on social media, the visitor correspondence unit can send a response including information related to that problem and confirm the purpose of the visit. This enables the visitor correspondence unit to respond appropriately based on the visitor's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, the number of likes, and the like.

[0038] The face recognition unit can improve the recognition accuracy by referring to the visitor's past face data during face recognition. For example, the face recognition unit can improve the recognition accuracy by referring to the face data of the visitor when they visited in the past. Furthermore, if the visitor has caused problems in the past, the face recognition unit can refer to that face data and recognize them with caution. Furthermore, if the visitor has visited frequently in the past, the face recognition unit can also refer to that face data and improve the recognition accuracy. In this way, the face recognition unit improves the accuracy of face recognition based on the past face data. Past face data includes, for example, face images, recognition results, visit dates and times, etc., but is not limited to these examples.

[0039] The face recognition unit can perform face recognition by taking into account the visitor's attribute information. For example, the face recognition unit can take into account the visitor's age and capture facial features according to the age. The face recognition unit can also take into account the visitor's gender and capture facial features according to the gender. Furthermore, the face recognition unit can improve the accuracy of face recognition based on the visitor's attribute information. This allows the face recognition unit to improve the accuracy of face recognition based on the visitor's attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples.

[0040] The face recognition unit can improve the recognition accuracy by taking into account the geographical location information of the visitor during face recognition. For example, if the visitor is a local resident, the face recognition unit can improve the recognition accuracy by taking into account facial features unique to the area. In addition, if the visitor comes from a distant location, the face recognition unit can improve the recognition accuracy by taking into account facial features unique to the area. Furthermore, if the visitor comes from a specific area, the face recognition unit can improve the recognition accuracy by taking into account facial features unique to the area. In this way, the face recognition unit improves the face recognition accuracy based on the geographical location information of the visitor. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0041] During face recognition, the face recognition unit can improve the recognition accuracy by referring to related literature and databases about the visitor. For example, if the visitor's facial data is described in related literature, the face recognition unit can improve the recognition accuracy by referring to the data. Also, if the visitor's facial data is registered in a database, the face recognition unit can improve the recognition accuracy by referring to the data. Furthermore, if the visitor's facial data remains in past records, the face recognition unit can improve the recognition accuracy by referring to the data. In this way, the face recognition unit improves the accuracy of face recognition based on related literature and databases. Related literature and databases include, for example, academic papers and patent databases, but are not limited to such examples.

[0042] During voice recognition, the voice recognition unit can improve recognition accuracy by referring to the visitor's past voice data. For example, the voice recognition unit improves recognition accuracy by referring to voice data from when the visitor visited in the past. Furthermore, if the visitor has caused problems in the past, the voice recognition unit can refer to that voice data and recognize the visitor with caution. Furthermore, if the visitor has visited frequently in the past, the voice recognition unit can also refer to that voice data and improve recognition accuracy. In this way, the voice recognition unit improves voice recognition accuracy based on past voice data. Past voice data includes, for example, voice files, recognition results, visit dates and times, etc., but is not limited to these examples.

[0043] The voice recognition unit can perform voice recognition taking into consideration the language and dialect spoken by the visitor. For example, if the visitor speaks a specific dialect, the voice recognition unit can perform voice recognition corresponding to that dialect. Also, if the visitor speaks a foreign language, the voice recognition unit can perform voice recognition corresponding to that language. Furthermore, the voice recognition unit can improve the accuracy of voice recognition based on the language and dialect spoken by the visitor. In this way, the voice recognition unit improves the accuracy of voice recognition based on the language and dialect spoken by the visitor. Languages ​​and dialects include, but are not limited to, standard Japanese, regional dialects, and foreign languages, for example.

[0044] The voice recognition unit can improve the recognition accuracy by taking into account the geographical location information of the visitor during voice recognition. For example, if the visitor is a local resident, the voice recognition unit can improve the recognition accuracy by taking into account the dialect and expressions specific to the area. Furthermore, if the visitor is from a distant location, the voice recognition unit can improve the recognition accuracy by taking into account the dialect and expressions specific to the area. Furthermore, if the visitor is from a specific area, the voice recognition unit can improve the recognition accuracy by taking into account the dialect and expressions specific to the area. In this way, the voice recognition unit improves the recognition accuracy based on the geographical location information of the visitor. Examples of geographical location information include, but are not limited to, GPS data, address information, etc.

[0045] During voice recognition, the voice recognition unit can improve the recognition accuracy by referring to related literature and databases about the visitor. For example, if the visitor's voice data is described in related literature, the voice recognition unit can improve the recognition accuracy by referring to that data. Also, if the visitor's voice data is registered in a database, the voice recognition unit can improve the recognition accuracy by referring to that data. Furthermore, if the visitor's voice data remains in past records, the voice recognition unit can improve the recognition accuracy by referring to that data. In this way, the voice recognition unit improves the voice recognition accuracy based on related literature and databases. Related literature and databases include, for example, academic papers and patent databases, but are not limited to such examples.

[0046] When making a notification, the notification unit can determine the priority of the notification by referring to the visitor's past visit history. For example, if the visitor has visited frequently in the past, the notification unit can prioritize the notification. Furthermore, if it is the visitor's first visit, the notification unit can also provide a notification including a detailed explanation. Furthermore, if the visitor has caused problems in the past, the notification unit can also provide a cautious notification. This allows the notification unit to determine the priority of the notification based on the past visit history. The visit history includes, for example, the date and time of the visit, the purpose of the visit, and the content of past responses, but is not limited to these examples.

[0047] When sending a notification, the notification unit can customize the notification content by taking into account the visitor's attribute information. For example, the notification unit can take into account the visitor's age and provide notification content appropriate to the visitor's age. The notification unit can also take into account the visitor's gender and provide notification content appropriate to the visitor's gender. Furthermore, the notification unit can customize the notification content based on the visitor's attribute information. This allows the notification unit to customize the notification content based on the visitor's attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples.

[0048] The notification unit can adjust the notification content taking into account the geographical location information of the visitor when sending a notification. For example, if the visitor is a local resident, the notification unit can send a friendly notification. If the visitor is from a distant location, the notification unit can also send a notification including a detailed explanation. Furthermore, if the visitor is from a specific area, the notification unit can also send a notification including information related to that area. This allows the notification unit to adjust the notification content based on the geographical location information of the visitor. Geographical location information includes, but is not limited to, GPS data, address information, and the like.

[0049] The notification unit can analyze the visitor's social media activity at the time of notification and reflect related information in the notification. For example, if the visitor is participating in a specific event on social media, the notification unit can provide a notification including information related to the event. Furthermore, if the visitor has expressed a specific interest on social media, the notification unit can provide a notification including information related to the interest. Furthermore, if the visitor has a specific problem on social media, the notification unit can provide a notification including information related to the problem. This allows the notification unit to tailor the content of the notification based on the visitor's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, the number of likes, and the like.

[0050] When receiving a package, the package receiving device can select the optimal package receiving method by referring to past delivery history. For example, if the delivery company has been used frequently in the past, the package receiving device will guide the user to a method that will allow for smooth delivery. Furthermore, if the delivery company is being used for the first time, the package receiving device can also guide the user to a delivery method that includes detailed explanations. Furthermore, if the delivery company has had problems in the past, the package receiving device can also guide the user to a cautious delivery method. This allows the package receiving device to select the optimal package receiving method based on past delivery history. Delivery history includes, for example, delivery date and time, delivery company, package contents, etc., but is not limited to these examples.

[0051] When receiving a parcel, the parcel receiving device can adjust the parcel receiving method taking into account the geographical location information of the courier. For example, if the courier is from a nearby area, the parcel receiving device can guide the user on a method that will ensure smooth delivery. Furthermore, if the courier is from a distant location, the parcel receiving device can also guide the user on a method of delivery that includes detailed instructions. Furthermore, if the courier is from a specific area, the parcel receiving device can also guide the user on a method of delivery that includes information related to that area. This allows the parcel receiving device to adjust the parcel receiving method based on the geographical location information of the courier. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0052] When issuing an alarm, the alarm device can improve the accuracy of the alarm by referring to past suspicious person data. For example, the alarm device can improve the accuracy of the alarm by referring to data on suspicious persons who have caused problems in the past. The alarm device can also improve the accuracy of the alarm by referring to data on suspicious persons who have appeared frequently in the past. Furthermore, the alarm device can improve the accuracy of the alarm based on past suspicious person data. In this way, the alarm device improves the accuracy of the alarm based on past suspicious person data. Suspicious person data includes, for example, the characteristics of the suspicious person, behavior patterns, past alarm history, etc., but is not limited to these examples.

[0053] When issuing an alarm, the alarm device can adjust the sounding of the alarm taking into account the geographical location information of the suspicious individual. For example, if the suspicious individual is coming from a nearby area, the alarm device can increase the sound of the alarm to heighten vigilance. Also, if the suspicious individual is coming from a distant area, the alarm device can increase the sound of the alarm to heighten vigilance. Furthermore, if the suspicious individual is coming from a specific area, the alarm device can also sound an alarm that includes information related to that area. This allows the alarm device to adjust the sounding of the alarm based on the geographical location information of the suspicious individual. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0054] When taking a photograph, the camera device can improve the accuracy of the photograph by referring to past photographing data. For example, the camera device can improve the accuracy of the photograph by referring to data on visitors photographed in the past. The camera device can also improve the accuracy of the photograph by referring to data on visitors who have caused problems in the past. Furthermore, the camera device can improve the accuracy of the photograph by referring to data on visitors who have frequently visited in the past. In this way, the camera device improves the accuracy of the photograph based on the past photographing data. Past photographing data includes, for example, the date and time of the photograph, the location of the photograph, and the subject of the photograph, but is not limited to these examples.

[0055] When capturing an image, the camera device can adjust the capturing method taking into account the geographical location information of the visitor. For example, if the visitor is a local resident, the camera device can adjust the capturing method taking into account facial features unique to the area. In addition, if the visitor comes from a distant location, the camera device can also adjust the capturing method taking into account facial features unique to the area. Furthermore, if the visitor comes from a specific area, the camera device can also adjust the capturing method taking into account facial features unique to the area. This allows the camera device to adjust the capturing method based on the geographical location information of the visitor. Geographical location information includes, but is not limited to, GPS data, address information, and the like.

[0056] When receiving a circular, the neighborhood association correspondence department can select the optimal receiving method by referring to the past circular history. For example, if the neighborhood association member has used the service frequently in the past, the neighborhood association correspondence department will guide them to a method that will allow them to receive their circular smoothly. In addition, if the neighborhood association member is using the service for the first time, the neighborhood association correspondence department can also guide them to a receiving method that includes detailed explanations. Furthermore, in the case of a neighborhood association member who has had problems in the past, the neighborhood association correspondence department can also guide them to a receiving method that requires caution. In this way, the neighborhood association correspondence department can select the optimal receiving method based on the past circular history. The circular history includes, for example, the circulation date and time, the person who circulated it, the circulation content, etc., but is not limited to these examples.

[0057] When receiving a circular, the neighborhood association correspondence department can adjust the receiving method taking into account the neighborhood member's geographical location information. For example, if a neighborhood member comes from a nearby area, the neighborhood association correspondence department can guide the neighborhood member to a method that will allow for smooth receiving. Furthermore, if a neighborhood member comes from a distant area, the neighborhood association correspondence department can also guide the neighborhood member to a receiving method that includes detailed instructions. Furthermore, if a neighborhood member comes from a specific area, the neighborhood association correspondence department can also guide the neighborhood member to a receiving method that includes information related to that area. This allows the neighborhood association correspondence department to adjust the receiving method based on the neighborhood member's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The visitor response department can also estimate the visitor's health condition and adjust the response content based on the estimated health condition. For example, if the visitor is tired, the department can provide a short and concise response and quickly confirm the purpose of the visit. If the visitor is in good health, the department can provide a response with a detailed explanation and carefully confirm the purpose of the visit. Furthermore, if the visitor is feeling unwell, the department can respond with priority and provide the necessary support. This allows the visitor response department to respond appropriately according to the visitor's health condition.

[0060] The parcel receiving device can also adjust the receiving method based on the contents of the parcel. For example, if the parcel contains valuables, it can recommend a receiving method with strict security measures. If the parcel contains food, it can recommend a method for quicker receiving. Furthermore, if the parcel contains large items, it can instruct the user to place them in an appropriate location. In this way, the parcel receiving device can provide the optimal receiving method according to the contents of the parcel.

[0061] The alarm device can also adjust the type and volume of the alarm sound when sounding an alarm, taking into account the surrounding environmental sounds. For example, the alarm sound can be reduced in volume during quiet nighttime hours and increased in volume during noisy daytime hours. The alarm sound can also be softened if there are children nearby. Furthermore, the type of alarm sound can be changed depending on the surrounding environmental sounds. This allows the alarm device to provide an appropriate alarm according to the surrounding environment.

[0062] The camera device can also analyze the visitor's clothing and belongings to infer the purpose of the visit. For example, if the visitor is wearing a suit, it can be assumed that the visit is for business purposes and an appropriate response can be made. If the visitor is wearing a delivery person's uniform, it can be assumed that the visitor is there to receive a package and a prompt response can be made. Furthermore, if the visitor is carrying tools, it can be assumed that the visitor's purpose is repair or maintenance and appropriate guidance can be provided. In this way, the camera device can infer the purpose of the visit based on the visitor's clothing and belongings and take appropriate action.

[0063] The neighborhood association response department can also automatically select the next distribution destination based on the contents of the circular. For example, if the circular contains information related to a specific area, it will distribute it to neighborhood association members in that area first. Also, if the circular contains highly urgent information, it can quickly select the next distribution destination and move the circular. Furthermore, if the circular contains information related to a specific event, it can also distribute it to neighborhood association members who are planning to attend the event. This allows the neighborhood association response department to select the optimal distribution destination based on the contents of the circular and move the circular efficiently.

[0064] The visitor handling department can also analyze the visitor's past visit history and select a response method. For example, if the visitor has visited frequently in the past, a friendly response can be provided and the purpose of the visit can be confirmed. If it is the visitor's first visit, a response including a detailed explanation can be provided and the purpose of the visit can be carefully confirmed. Furthermore, if the visitor has caused problems in the past, a cautious response can be provided and the purpose of the visit can be carefully confirmed. This allows the visitor handling department to select the optimal response based on the visitor's past visit history.

[0065] The visitor handling department can also adjust its response based on the visitor's current situation. For example, if a visitor arrives on a rainy day, it can check whether they have an umbrella and, if necessary, guide them to the location of an umbrella stand. If a visitor arrives at night, it can respond in a cheerful voice and confirm the purpose of their visit. Furthermore, if a visitor arrives on a hot day, it can guide them to a cooler place and confirm the purpose of their visit. This allows the visitor handling department to respond appropriately according to the visitor's situation.

[0066] The visitor response unit can also prioritize relevant responses by taking into account the visitor's geographic location information. For example, if the visitor is a local resident, a friendly response can be provided to confirm the purpose of their visit. If the visitor is from a distant location, a response including a detailed explanation can be provided to carefully confirm the purpose of their visit. Furthermore, if the visitor is from a specific area, a response including information related to that area can be provided to confirm the purpose of their visit. This allows the visitor response unit to provide an appropriate response based on the visitor's geographic location information.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The visitor reception department collects visitor information, including names, facial photographs, and audio data. The visitor reception department can collect visitor information through the intercom, as well as using cameras and microphones. Step 2: The facial recognition unit analyzes the information collected by the visitor reception unit. The facial recognition unit analyzes and recognizes the visitor's face using image analysis algorithms and deep learning technology. Step 3: The voice recognition unit analyzes the voice collected by the visitor reception unit. The voice recognition unit analyzes and recognizes the visitor's voice using voice analysis technology and natural language processing technology. Step 4: The notification unit issues a notification based on the information obtained by the face recognition unit and the voice recognition unit. The notification unit issues a notification using a push notification to the smartphone, an email notification, an alert sound, or the like.

[0069] (Example 2) A home safety system according to an embodiment of the present invention is a system for ensuring the safety of a home while the resident is away from home. This system utilizes AI to recognize the face and voice of visitors and automatically respond appropriately. Specifically, the system comprises the following steps: First, when a visitor arrives, the visitor response unit automatically responds via the intercom and confirms the purpose of the visit. Next, the face recognition unit captures the visitor's face with a camera, and the voice recognition unit analyzes the visitor's voice. If necessary, the notification unit sends a notification to a smartphone. In addition, a package receiving device receives packages from couriers, and an alarm unit sounds an alarm if it detects a suspicious person. Furthermore, a neighborhood association response unit automatically receives circular notices, confirms the next distribution destination, and moves the notices. By linking with a smartphone, this system allows users to check the status of their home and take necessary action even when they are away from home. For example, the visitor response unit automatically responds via the intercom and confirms the purpose of the visit. The face recognition unit captures the visitor's face with a camera, and the voice recognition unit analyzes the visitor's voice. If necessary, the notification unit sends a notification to a smartphone. The package receiving device receives packages from delivery companies, and the alarm device sounds an alarm if it detects a suspicious person. The neighborhood association response department automatically receives circulars, checks the next distribution destination, and moves the circular. This allows the home safety system to ensure the safety of your home while you are away, and efficiently collect, analyze, recognize, and notify visitors' information.

[0070] A home safety system according to an embodiment includes a visitor response unit, a face recognition unit, a voice recognition unit, and a notification unit. The visitor response unit collects visitor information. The visitor information includes, but is not limited to, a name, a facial photo, and audio data. The visitor response unit collects visitor information, for example, through an intercom. The visitor response unit can also use a camera or a microphone to collect visitor information. The face recognition unit analyzes the information collected by the visitor response unit. The face recognition unit can analyze the visitor's face, for example, using an image analysis algorithm. The face recognition unit can also use deep learning technology to recognize the visitor's face. The voice recognition unit analyzes the audio collected by the visitor response unit. The voice recognition unit can analyze the visitor's voice, for example, using voice analysis technology. The voice recognition unit can also use natural language processing technology to recognize the visitor's voice. The notification unit provides a notification based on the information obtained by the face recognition unit and the voice recognition unit. The notification unit can provide a push notification to a smartphone, for example. The notification unit can also notify the user by email or by sound alert, thereby enabling the home safety system according to the embodiment to efficiently collect, analyze, recognize, and notify the user of visitor information.

[0071] The home security system includes a parcel receiving device. The parcel receiving device can automatically receive parcels from a delivery company. The parcel receiving device may include, but is not limited to, a function to accommodate the size and weight of the parcel. The parcel receiving device receives parcels, for example, using a parcel locker. The parcel receiving device can also instruct the delivery company to place the parcel in a specified location. For example, the parcel receiving device guides the delivery company to place the parcel in the specified location. This allows the parcel receiving device to automatically receive the parcel from the delivery company.

[0072] The home safety system includes an alarm device. The alarm device can sound an alarm when a suspicious person is detected. The alarm device includes, for example, but is not limited to, a type of alarm sound and a trigger condition for the alarm. For example, the alarm device can sound an alarm when a suspicious person is detected. The alarm device can also change the type of alarm sound. For example, the alarm device can adjust the volume of the alarm sound. This allows the alarm device to sound an alarm when a suspicious person is detected.

[0073] The home security system includes a camera device. The camera device can capture a visitor's face. The camera device has, for example, but is not limited to, a resolution, a capture range, and a nighttime capture function. For example, the camera device captures a visitor's face with high resolution. The camera device also has a function for capturing images at night. For example, the camera device can capture images at night using an infrared camera. This allows the camera device to capture a visitor's face.

[0074] The home safety assurance system includes a neighborhood association response unit. The neighborhood association response unit can automatically receive neighborhood association circulars, confirm the next distribution destination, and move the circulars. The neighborhood association response unit includes, for example, a method for receiving a circular and a method for confirming the next distribution destination, but is not limited to these examples. For example, the neighborhood association response unit automatically receives a circular. Furthermore, the neighborhood association response unit can confirm the next distribution destination and move the circular. For example, the neighborhood association response unit specifies a location to receive the circular and confirm the next distribution destination. This allows the neighborhood association response unit to automatically receive neighborhood association circulars, confirm the next distribution destination, and move the circular.

[0075] The visitor response unit can estimate the visitor's emotions and adjust the response content based on the estimated visitor's emotions. For example, if the visitor is nervous, the visitor response unit responds in a calm voice and adjusts the response content to provide a sense of security. If the visitor is in a hurry, the visitor response unit can provide a quick and concise response to quickly confirm the purpose of the visit. If the visitor is relaxed, the visitor response unit can provide a response including a detailed explanation to carefully confirm the purpose of the visit. This enables the visitor response unit to provide an appropriate response according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The visitor handling unit can analyze the visitor's past visit history and select a response method. For example, if the visitor has visited frequently in the past, the visitor handling unit can provide a friendly response and confirm the purpose of the visit. Furthermore, if it is the visitor's first visit, the visitor handling unit can provide a response that includes a detailed explanation and carefully confirm the purpose of the visit. Furthermore, if the visitor has caused problems in the past, the visitor handling unit can provide a cautious response and carefully confirm the purpose of the visit. This allows the visitor handling unit to provide an optimal response based on the visit history. The visit history includes, for example, the date and time of the visit, the purpose of the visit, and the content of past responses, but is not limited to these examples.

[0077] The visitor response unit can adjust the response content based on the visitor's current situation. For example, if the visitor visits on rainy days, the visitor response unit checks whether the visitor has an umbrella and, if necessary, guides the visitor to the location of an umbrella stand. Furthermore, if the visitor visits at night, the visitor response unit can respond in a cheerful voice and confirm the purpose of the visit. Furthermore, if the visitor visits on a hot day, the visitor response unit can guide the visitor to wait in a cooler place and confirm the purpose of the visit. This enables the visitor response unit to respond appropriately according to the visitor's situation. The current situation includes, for example, weather information, time of day, surrounding environment, etc., but is not limited to such examples.

[0078] The visitor response unit can estimate the visitor's emotions and prioritize responses based on the estimated visitor's emotions. For example, if the visitor is nervous, the visitor response unit prioritizes responses and adjusts the content to provide a sense of security. If the visitor is in a hurry, the visitor response unit can provide a quick and concise response to quickly confirm the purpose of the visit. If the visitor is relaxed, the visitor response unit can provide a response including a detailed explanation to carefully confirm the purpose of the visit. This allows the visitor response unit to prioritize responses according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The visitor response unit can prioritize relevant responses by taking into account the visitor's geographical location information. For example, if the visitor is a local resident, the visitor response unit can provide a friendly response and confirm the purpose of the visit. Furthermore, if the visitor is from a distant location, the visitor response unit can provide a response including a detailed explanation and carefully confirm the purpose of the visit. Furthermore, if the visitor is from a specific area, the visitor response unit can provide a response including information related to that area and confirm the purpose of the visit. This enables the visitor response unit to provide an appropriate response based on the visitor's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0080] The visitor correspondence unit can analyze the visitor's social media activity and reflect related information in the response. For example, if the visitor is participating in a specific event on social media, the visitor correspondence unit can send a response including information related to the event and confirm the purpose of the visit. Furthermore, if the visitor has expressed a specific interest on social media, the visitor correspondence unit can send a response including information related to that interest and confirm the purpose of the visit. Furthermore, if the visitor has a specific problem on social media, the visitor correspondence unit can send a response including information related to that problem and confirm the purpose of the visit. This enables the visitor correspondence unit to respond appropriately based on the visitor's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, the number of likes, and the like.

[0081] The face recognition unit can estimate the visitor's emotions and adjust the accuracy of face recognition based on the estimated visitor's emotions. For example, if the visitor is nervous, the facial expression will be stiff, so the face recognition unit can increase the accuracy of face recognition. The face recognition unit can also adjust the accuracy of face recognition if the visitor is relaxed, so that the facial expression will be natural. Furthermore, if the visitor is in a hurry, the facial movements will be quick, so the face recognition unit can increase the accuracy of face recognition. This allows the face recognition unit to adjust the accuracy of face recognition according to the visitor's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] The face recognition unit can improve the recognition accuracy by referring to the visitor's past face data during face recognition. For example, the face recognition unit can improve the recognition accuracy by referring to the face data of the visitor when they visited in the past. Furthermore, if the visitor has caused problems in the past, the face recognition unit can refer to that face data and recognize them with caution. Furthermore, if the visitor has visited frequently in the past, the face recognition unit can also refer to that face data and improve the recognition accuracy. In this way, the face recognition unit improves the accuracy of face recognition based on the past face data. Past face data includes, for example, face images, recognition results, visit dates and times, etc., but is not limited to these examples.

[0083] The face recognition unit can perform face recognition by taking into account the visitor's attribute information. For example, the face recognition unit can take into account the visitor's age and capture facial features according to the age. The face recognition unit can also take into account the visitor's gender and capture facial features according to the gender. Furthermore, the face recognition unit can improve the accuracy of face recognition based on the visitor's attribute information. This allows the face recognition unit to improve the accuracy of face recognition based on the visitor's attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples.

[0084] The face recognition unit can estimate the visitor's emotions and determine the priority of face recognition based on the estimated visitor's emotions. For example, if the visitor is nervous, the face recognition unit can prioritize face recognition to provide a sense of security. Furthermore, if the visitor is in a hurry, the face recognition unit can quickly perform face recognition to quickly confirm the purpose of the visit. Furthermore, if the visitor is relaxed, the face recognition unit can perform detailed face recognition to carefully confirm the purpose of the visit. This allows the face recognition unit to determine the priority of face recognition according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The face recognition unit can improve the recognition accuracy by taking into account the geographical location information of the visitor during face recognition. For example, if the visitor is a local resident, the face recognition unit can improve the recognition accuracy by taking into account facial features unique to the area. In addition, if the visitor comes from a distant location, the face recognition unit can improve the recognition accuracy by taking into account facial features unique to the area. Furthermore, if the visitor comes from a specific area, the face recognition unit can improve the recognition accuracy by taking into account facial features unique to the area. In this way, the face recognition unit improves the face recognition accuracy based on the geographical location information of the visitor. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0086] During face recognition, the face recognition unit can improve the recognition accuracy by referring to related literature and databases about the visitor. For example, if the visitor's facial data is described in related literature, the face recognition unit can improve the recognition accuracy by referring to the data. Also, if the visitor's facial data is registered in a database, the face recognition unit can improve the recognition accuracy by referring to the data. Furthermore, if the visitor's facial data remains in past records, the face recognition unit can improve the recognition accuracy by referring to the data. In this way, the face recognition unit improves the accuracy of face recognition based on related literature and databases. Related literature and databases include, for example, academic papers and patent databases, but are not limited to such examples.

[0087] The voice recognition unit can estimate the visitor's emotion and adjust the accuracy of voice recognition based on the estimated emotion of the visitor. For example, if the visitor is nervous, the voice will tremble, so the voice recognition unit increases the accuracy of voice recognition. The voice recognition unit can also adjust the accuracy of voice recognition to capture a natural voice if the visitor is relaxed. Furthermore, if the visitor is in a hurry, the voice will speed up, so the voice recognition unit can increase the accuracy of voice recognition. This allows the voice recognition unit to adjust the accuracy of voice recognition according to the visitor's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] During voice recognition, the voice recognition unit can improve recognition accuracy by referring to the visitor's past voice data. For example, the voice recognition unit improves recognition accuracy by referring to voice data from when the visitor visited in the past. Furthermore, if the visitor has caused problems in the past, the voice recognition unit can refer to that voice data and recognize the visitor with caution. Furthermore, if the visitor has visited frequently in the past, the voice recognition unit can also refer to that voice data and improve recognition accuracy. In this way, the voice recognition unit improves voice recognition accuracy based on past voice data. Past voice data includes, for example, voice files, recognition results, visit dates and times, etc., but is not limited to these examples.

[0089] The voice recognition unit can perform voice recognition taking into consideration the language and dialect spoken by the visitor. For example, if the visitor speaks a specific dialect, the voice recognition unit can perform voice recognition corresponding to that dialect. Also, if the visitor speaks a foreign language, the voice recognition unit can perform voice recognition corresponding to that language. Furthermore, the voice recognition unit can improve the accuracy of voice recognition based on the language and dialect spoken by the visitor. In this way, the voice recognition unit improves the accuracy of voice recognition based on the language and dialect spoken by the visitor. Languages ​​and dialects include, but are not limited to, standard Japanese, regional dialects, and foreign languages, for example.

[0090] The voice recognition unit can estimate the visitor's emotions and determine the priority of voice recognition based on the estimated visitor's emotions. For example, if the visitor is nervous, the voice recognition unit can prioritize voice recognition to provide a sense of security. Furthermore, if the visitor is in a hurry, the voice recognition unit can quickly perform voice recognition to quickly confirm the purpose of the visit. Furthermore, if the visitor is relaxed, the voice recognition unit can perform detailed voice recognition to carefully confirm the purpose of the visit. This allows the voice recognition unit to determine the priority of voice recognition according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The voice recognition unit can improve the recognition accuracy by taking into account the geographical location information of the visitor during voice recognition. For example, if the visitor is a local resident, the voice recognition unit can improve the recognition accuracy by taking into account the dialect and expressions specific to the area. Furthermore, if the visitor is from a distant location, the voice recognition unit can improve the recognition accuracy by taking into account the dialect and expressions specific to the area. Furthermore, if the visitor is from a specific area, the voice recognition unit can improve the recognition accuracy by taking into account the dialect and expressions specific to the area. In this way, the voice recognition unit improves the recognition accuracy based on the geographical location information of the visitor. Examples of geographical location information include, but are not limited to, GPS data, address information, etc.

[0092] During voice recognition, the voice recognition unit can improve the recognition accuracy by referring to related literature and databases about the visitor. For example, if the visitor's voice data is described in related literature, the voice recognition unit can improve the recognition accuracy by referring to that data. Also, if the visitor's voice data is registered in a database, the voice recognition unit can improve the recognition accuracy by referring to that data. Furthermore, if the visitor's voice data remains in past records, the voice recognition unit can improve the recognition accuracy by referring to that data. In this way, the voice recognition unit improves the voice recognition accuracy based on related literature and databases. Related literature and databases include, for example, academic papers and patent databases, but are not limited to such examples.

[0093] The notification unit can estimate the visitor's emotions and adjust the notification content based on the estimated visitor's emotions. For example, if the visitor is nervous, the notification unit can provide a notification with content that provides a sense of security. If the visitor is in a hurry, the notification unit can also provide a quick and concise notification. Furthermore, if the visitor is relaxed, the notification unit can also provide a notification that includes a detailed explanation. This enables the notification unit to adjust the notification content according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] When making a notification, the notification unit can determine the priority of the notification by referring to the visitor's past visit history. For example, if the visitor has visited frequently in the past, the notification unit can prioritize the notification. Furthermore, if it is the visitor's first visit, the notification unit can also provide a notification including a detailed explanation. Furthermore, if the visitor has caused problems in the past, the notification unit can also provide a cautious notification. This allows the notification unit to determine the priority of the notification based on the past visit history. The visit history includes, for example, the date and time of the visit, the purpose of the visit, and the content of past responses, but is not limited to these examples.

[0095] When sending a notification, the notification unit can customize the notification content by taking into account the visitor's attribute information. For example, the notification unit can take into account the visitor's age and provide notification content appropriate to the visitor's age. The notification unit can also take into account the visitor's gender and provide notification content appropriate to the visitor's gender. Furthermore, the notification unit can customize the notification content based on the visitor's attribute information. This allows the notification unit to customize the notification content based on the visitor's attribute information. Attribute information includes, for example, age, gender, occupation, etc., but is not limited to these examples.

[0096] The notification unit can estimate the visitor's emotions and adjust the timing of the notification based on the estimated visitor's emotions. For example, if the visitor is nervous, the notification unit can notify the visitor early to provide a sense of security. Furthermore, if the visitor is in a hurry, the notification unit can notify the visitor quickly and quickly confirm the purpose of the visit. Furthermore, if the visitor is relaxed, the notification unit can notify the visitor with a detailed explanation and carefully confirm the purpose of the visit. This allows the notification unit to adjust the timing of the notification according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0097] The notification unit can adjust the notification content taking into account the geographical location information of the visitor when sending a notification. For example, if the visitor is a local resident, the notification unit can send a friendly notification. If the visitor is from a distant location, the notification unit can also send a notification including a detailed explanation. Furthermore, if the visitor is from a specific area, the notification unit can also send a notification including information related to that area. This allows the notification unit to adjust the notification content based on the geographical location information of the visitor. Geographical location information includes, but is not limited to, GPS data, address information, and the like.

[0098] The notification unit can analyze the visitor's social media activity at the time of notification and reflect related information in the notification. For example, if the visitor is participating in a specific event on social media, the notification unit can provide a notification including information related to the event. Furthermore, if the visitor has expressed a specific interest on social media, the notification unit can provide a notification including information related to the interest. Furthermore, if the visitor has a specific problem on social media, the notification unit can provide a notification including information related to the problem. This allows the notification unit to tailor the content of the notification based on the visitor's social media activity. Social media activity includes, but is not limited to, the content of posts, the number of followers, the number of likes, and the like.

[0099] The parcel receiving device can estimate the emotion of the courier and adjust the parcel receiving method based on the estimated emotion of the courier. For example, if the courier is in a hurry, the parcel receiving device can guide the courier on how to receive the parcel quickly. Furthermore, if the courier is relaxed, the parcel receiving device can guide the courier on how to receive the parcel including detailed instructions. Furthermore, if the courier is nervous, the parcel receiving device can guide the courier on how to receive the parcel with content that gives a sense of security. This enables the parcel receiving device to adjust the parcel receiving method according to the emotion of the courier. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] When receiving a package, the package receiving device can select the optimal package receiving method by referring to past delivery history. For example, if the delivery company has been used frequently in the past, the package receiving device will guide the user to a method that will allow for smooth delivery. Furthermore, if the delivery company is being used for the first time, the package receiving device can also guide the user to a delivery method that includes detailed explanations. Furthermore, if the delivery company has had problems in the past, the package receiving device can also guide the user to a cautious delivery method. This allows the package receiving device to select the optimal package receiving method based on past delivery history. Delivery history includes, for example, delivery date and time, delivery company, package contents, etc., but is not limited to these examples.

[0101] The parcel receiving device can estimate the emotion of a courier and determine the priority of parcel receiving based on the estimated emotion of the courier. For example, if the courier is in a hurry, the parcel receiving device can provide instructions on how to receive the parcel with priority. Furthermore, if the courier is relaxed, the parcel receiving device can provide instructions on how to receive the parcel with detailed instructions. Furthermore, if the courier is nervous, the parcel receiving device can provide instructions on how to receive the parcel with content that gives a sense of security. This allows the parcel receiving device to determine the priority of parcel receiving based on the emotion of the courier. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When receiving a parcel, the parcel receiving device can adjust the parcel receiving method taking into account the geographical location information of the courier. For example, if the courier is from a nearby area, the parcel receiving device can guide the user on a method that will ensure smooth delivery. Furthermore, if the courier is from a distant location, the parcel receiving device can also guide the user on a method of delivery that includes detailed instructions. Furthermore, if the courier is from a specific area, the parcel receiving device can also guide the user on a method of delivery that includes information related to that area. This allows the parcel receiving device to adjust the parcel receiving method based on the geographical location information of the courier. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0103] The alarm device can estimate the emotions of a suspicious person and adjust the sounding of the alarm based on the estimated emotions of the suspicious person. For example, if the suspicious person is nervous, the alarm device can increase the volume of the alarm to heighten their vigilance. Furthermore, if the suspicious person is relaxed, the alarm device can also increase the volume of the alarm to heighten their vigilance. Furthermore, if the suspicious person is in a hurry, the alarm device can also increase the volume of the alarm to heighten their vigilance. This enables the alarm device to adjust the sounding of the alarm according to the emotions of the suspicious person. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] When issuing an alarm, the alarm device can improve the accuracy of the alarm by referring to past suspicious person data. For example, the alarm device can improve the accuracy of the alarm by referring to data on suspicious persons who have caused problems in the past. The alarm device can also improve the accuracy of the alarm by referring to data on suspicious persons who have appeared frequently in the past. Furthermore, the alarm device can improve the accuracy of the alarm based on past suspicious person data. In this way, the alarm device improves the accuracy of the alarm based on past suspicious person data. Suspicious person data includes, for example, the characteristics of the suspicious person, behavior patterns, past alarm history, etc., but is not limited to these examples.

[0105] The alarm device can estimate the emotions of a suspicious person and determine the priority of an alarm based on the estimated emotions of the suspicious person. For example, if the suspicious person is nervous, the alarm device can prioritize sounding the alarm and increasing their vigilance. The alarm device can also prioritize sounding the alarm and increasing their vigilance if the suspicious person is relaxed. Furthermore, the alarm device can prioritize sounding the alarm and increasing their vigilance if the suspicious person is in a hurry. This allows the alarm device to determine the priority of an alarm according to the emotions of the suspicious person. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] When issuing an alarm, the alarm device can adjust the sounding of the alarm taking into account the geographical location information of the suspicious individual. For example, if the suspicious individual is coming from a nearby area, the alarm device can increase the sound of the alarm to heighten vigilance. Also, if the suspicious individual is coming from a distant area, the alarm device can increase the sound of the alarm to heighten vigilance. Furthermore, if the suspicious individual is coming from a specific area, the alarm device can also sound an alarm that includes information related to that area. This allows the alarm device to adjust the sounding of the alarm based on the geographical location information of the suspicious individual. Geographical location information includes, but is not limited to, GPS data, address information, etc.

[0107] The camera device can estimate the visitor's emotions and adjust the camera's shooting method based on the estimated visitor's emotions. For example, if the visitor is nervous, the camera device adjusts the camera's shooting angle because the visitor's facial expression becomes stiff. The camera device can also adjust the camera's shooting angle to capture a natural expression if the visitor is relaxed. Furthermore, if the visitor is in a hurry, the camera device can also adjust the camera's shooting angle because the visitor's facial movements become faster. This enables the camera device to adjust the camera's shooting method according to the visitor's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0108] When taking a photograph, the camera device can improve the accuracy of the photograph by referring to past photographing data. For example, the camera device can improve the accuracy of the photograph by referring to data on visitors photographed in the past. The camera device can also improve the accuracy of the photograph by referring to data on visitors who have caused problems in the past. Furthermore, the camera device can improve the accuracy of the photograph by referring to data on visitors who have frequently visited in the past. In this way, the camera device improves the accuracy of the photograph based on the past photographing data. Past photographing data includes, for example, the date and time of the photograph, the location of the photograph, and the subject of the photograph, but is not limited to these examples.

[0109] The camera device can estimate the visitor's emotions and determine the priority of photographing based on the estimated visitor's emotions. For example, if the visitor is nervous, the camera device can prioritize photographing, giving the visitor a sense of security. Also, if the visitor is in a hurry, the camera device can quickly photograph the visitor and quickly confirm the purpose of their visit. Furthermore, if the visitor is relaxed, the camera device can take detailed photographs and carefully confirm the purpose of their visit. This allows the camera device to determine the priority of photographing according to the visitor's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] When capturing an image, the camera device can adjust the capturing method taking into account the geographical location information of the visitor. For example, if the visitor is a local resident, the camera device can adjust the capturing method taking into account facial features unique to the area. In addition, if the visitor comes from a distant location, the camera device can also adjust the capturing method taking into account facial features unique to the area. Furthermore, if the visitor comes from a specific area, the camera device can also adjust the capturing method taking into account facial features unique to the area. This allows the camera device to adjust the capturing method based on the geographical location information of the visitor. Geographical location information includes, but is not limited to, GPS data, address information, and the like.

[0111] The neighborhood association correspondence unit can estimate the emotions of neighborhood association members and adjust how they receive the circulars based on the estimated emotions of the neighborhood association members. For example, if a neighborhood association member is in a hurry, the neighborhood association correspondence unit can guide them on how to receive the circulars quickly. Furthermore, if a neighborhood association member is relaxed, the neighborhood association correspondence unit can guide them on how to receive the circulars with detailed instructions. Furthermore, if a neighborhood association member is nervous, the neighborhood association correspondence unit can guide them on how to receive the circulars with content that will give them a sense of security. This enables the neighborhood association correspondence unit to adjust how they receive the circulars based on the emotions of the neighborhood association members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0112] When receiving a circular, the neighborhood association correspondence department can select the optimal receiving method by referring to the past circular history. For example, if the neighborhood association member has used the service frequently in the past, the neighborhood association correspondence department will guide them to a method that will allow them to receive their circular smoothly. In addition, if the neighborhood association member is using the service for the first time, the neighborhood association correspondence department can also guide them to a receiving method that includes detailed explanations. Furthermore, in the case of a neighborhood association member who has had problems in the past, the neighborhood association correspondence department can also guide them to a receiving method that requires caution. In this way, the neighborhood association correspondence department can select the optimal receiving method based on the past circular history. The circular history includes, for example, the circulation date and time, the person who circulated it, the circulation content, etc., but is not limited to these examples.

[0113] The neighborhood association correspondence unit can estimate the emotions of neighborhood association members and determine the priority of circular notices based on the estimated emotions of neighborhood association members. For example, if a neighborhood association member is in a hurry, the neighborhood association correspondence unit can guide the member on how to receive the circular notice with priority. Furthermore, if a neighborhood association member is relaxed, the neighborhood association correspondence unit can guide the member on how to receive a circular notice with detailed instructions. Furthermore, if a neighborhood association member is nervous, the neighborhood association correspondence unit can guide the member on how to receive a circular notice with content that will give them a sense of security. In this way, the neighborhood association correspondence unit can determine the priority of circular notices based on the emotions of neighborhood association members. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0114] When receiving a circular, the neighborhood association correspondence department can adjust the receiving method taking into account the neighborhood member's geographical location information. For example, if a neighborhood member comes from a nearby area, the neighborhood association correspondence department can guide the neighborhood member to a method that will allow for smooth receiving. Furthermore, if a neighborhood member comes from a distant area, the neighborhood association correspondence department can also guide the neighborhood member to a receiving method that includes detailed instructions. Furthermore, if a neighborhood member comes from a specific area, the neighborhood association correspondence department can also guide the neighborhood member to a receiving method that includes information related to that area. This allows the neighborhood association correspondence department to adjust the receiving method based on the neighborhood member's geographical location information. Geographical location information includes, but is not limited to, GPS data, address information, etc. === Hard Collateral 1-1 === Each of the multiple elements, including the visitor response unit, face recognition unit, voice recognition unit, notification unit, parcel receiving device, alarm device, camera device, and neighborhood association response unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the visitor response unit is implemented by the control unit 46A of the smart device 14 and collects visitor information through an intercom. The face recognition unit photographs the visitor's face using the camera 42 of the smart device 14 and analyzes the image by the specific processing unit 290 of the data processing device 12. The voice recognition unit collects the visitor's voice using the microphone 38B of the smart device 14 and analyzes the voice by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the specific processing unit 290 of the data processing device 12 and sends a push notification to the smartphone. The parcel receiving device is implemented by the control unit 46A of the smart device 14 and receives parcels using a delivery box. The alarm unit is implemented by the control unit 46A of the smart device 14 and sounds an alarm when a suspicious person is detected. The camera device is realized by the camera 42 of the smart device 14, and captures a high-resolution image of the face of the visitor. The neighborhood association support unit is realized by the control unit 46A of the smart device 14, and automatically receives the circular, checks the next distribution destination, and moves the circular. === Hard Collateral 1-2 === Each of the multiple elements, including the visitor response unit, face recognition unit, voice recognition unit, notification unit, parcel receiving device, alarm device, camera device, and neighborhood association response unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the visitor response unit is realized by the control unit 46A of the smart glasses 214 and collects visitor information through an intercom. The face recognition unit photographs the visitor's face using the camera 42 of the smart glasses 214 and analyzes the image by the specific processing unit 290 of the data processing device 12. The voice recognition unit collects the visitor's voice using the microphone 238 of the smart glasses 214 and analyzes the voice by the specific processing unit 290 of the data processing device 12. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and sends a push notification to the smartphone. The parcel receiving device is realized by the control unit 46A of the smart glasses 214 and receives parcels using a delivery box. The alarm device is realized by the control unit 46A of the smart glasses 214 and sounds an alarm when a suspicious person is detected. The camera device is realized by the camera 42 of the smart glasses 214, and takes high-resolution pictures of the faces of visitors. The neighborhood association support unit is realized by the control unit 46A of the smart glasses 214, and automatically receives the circular, checks the next distribution destination, and moves the circular. === Hard Collateral 1-3 === Each of the multiple elements, including the visitor response unit, face recognition unit, voice recognition unit, notification unit, parcel receiving device, alarm device, camera device, and neighborhood association response unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the visitor response unit is implemented by the control unit 46A of the headset terminal 314 and collects visitor information via an intercom. The face recognition unit photographs the visitor's face using the camera 42 of the headset terminal 314 and analyzes the image by the specific processing unit 290 of the data processing device 12. The voice recognition unit collects the visitor's voice using the microphone 238 of the headset terminal 314 and analyzes the voice by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the specific processing unit 290 of the data processing device 12 and sends a push notification to the smartphone. The parcel receiving device is implemented by the control unit 46A of the headset terminal 314 and receives parcels using a delivery box. The alarm unit is implemented by the control unit 46A of the headset terminal 314 and sounds an alarm when a suspicious person is detected. The camera device is realized by the camera 42 of the headset terminal 314, and takes high-resolution pictures of the faces of visitors. The neighborhood association support unit is realized by the control unit 46A of the headset terminal 314, and automatically receives the circular, checks the next distribution destination, and moves the circular. === Hard Collateral 1-4 === Each of the multiple elements, including the visitor response unit, face recognition unit, voice recognition unit, notification unit, parcel receiving device, alarm device, camera device, and neighborhood association response unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the visitor response unit is realized by the control unit 46A of the robot 414 and collects visitor information through an intercom. The face recognition unit photographs the visitor's face using the camera 42 of the robot 414 and analyzes the photograph by the specific processing unit 290 of the data processing device 12. The voice recognition unit collects the visitor's voice using the microphone 238 of the robot 414 and analyzes the voice by the specific processing unit 290 of the data processing device 12. The notification unit is realized by the specific processing unit 290 of the data processing device 12 and sends a push notification to the smartphone. The parcel receiving device is realized by the control unit 46A of the robot 414 and receives parcels using a delivery box. The alarm device is realized by the control unit 46A of the robot 414 and sounds an alarm when a suspicious person is detected. The camera device is realized by the camera 42 of the robot 414, and takes high-resolution pictures of the faces of visitors. The neighborhood association support unit is realized by the control unit 46A of the robot 414, and automatically receives the circular, checks the next distribution destination, and moves the circular.

[0115] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0116] The visitor response department can also estimate the visitor's health condition and adjust the response content based on the estimated health condition. For example, if the visitor is tired, the department can provide a short and concise response and quickly confirm the purpose of the visit. If the visitor is in good health, the department can provide a response with a detailed explanation and carefully confirm the purpose of the visit. Furthermore, if the visitor is feeling unwell, the department can respond with priority and provide the necessary support. This allows the visitor response department to respond appropriately according to the visitor's health condition.

[0117] The parcel receiving device can also adjust the receiving method based on the contents of the parcel. For example, if the parcel contains valuables, it can recommend a receiving method with strict security measures. If the parcel contains food, it can recommend a method for quicker receiving. Furthermore, if the parcel contains large items, it can instruct the user to place them in an appropriate location. In this way, the parcel receiving device can provide the optimal receiving method according to the contents of the parcel.

[0118] The alarm device can also adjust the type and volume of the alarm sound when sounding an alarm, taking into account the surrounding environmental sounds. For example, the alarm sound can be reduced in volume during quiet nighttime hours and increased in volume during noisy daytime hours. The alarm sound can also be softened if there are children nearby. Furthermore, the type of alarm sound can be changed depending on the surrounding environmental sounds. This allows the alarm device to provide an appropriate alarm according to the surrounding environment.

[0119] The camera device can also analyze the visitor's clothing and belongings to infer the purpose of the visit. For example, if the visitor is wearing a suit, it can be assumed that the visit is for business purposes and an appropriate response can be made. If the visitor is wearing a delivery person's uniform, it can be assumed that the visitor is there to receive a package and a prompt response can be made. Furthermore, if the visitor is carrying tools, it can be assumed that the visitor's purpose is repair or maintenance and appropriate guidance can be provided. In this way, the camera device can infer the purpose of the visit based on the visitor's clothing and belongings and take appropriate action.

[0120] The neighborhood association response department can also automatically select the next distribution destination based on the contents of the circular. For example, if the circular contains information related to a specific area, it will distribute it to neighborhood association members in that area first. Also, if the circular contains highly urgent information, it can quickly select the next distribution destination and move the circular. Furthermore, if the circular contains information related to a specific event, it can also distribute it to neighborhood association members who are planning to attend the event. This allows the neighborhood association response department to select the optimal distribution destination based on the contents of the circular and move the circular efficiently.

[0121] The visitor handling unit can also estimate the visitor's emotions and adjust the content of the response based on the estimated visitor's emotions. For example, if the visitor is nervous, the response can be adjusted to a calm voice and reassuring content. If the visitor is in a hurry, the response can be quick and concise, and the purpose of the visit can be quickly confirmed. Furthermore, if the visitor is relaxed, the response can include a detailed explanation and carefully confirm the purpose of the visit. This enables the visitor handling unit to respond appropriately according to the visitor's emotions.

[0122] The visitor handling department can also analyze the visitor's past visit history and select a response method. For example, if the visitor has visited frequently in the past, a friendly response can be provided and the purpose of the visit can be confirmed. If it is the visitor's first visit, a response including a detailed explanation can be provided and the purpose of the visit can be carefully confirmed. Furthermore, if the visitor has caused problems in the past, a cautious response can be provided and the purpose of the visit can be carefully confirmed. This allows the visitor handling department to select the optimal response based on the visitor's past visit history.

[0123] The visitor handling department can also adjust its response based on the visitor's current situation. For example, if a visitor arrives on a rainy day, it can check whether they have an umbrella and, if necessary, guide them to the location of an umbrella stand. If a visitor arrives at night, it can respond in a cheerful voice and confirm the purpose of their visit. Furthermore, if a visitor arrives on a hot day, it can guide them to a cooler place and confirm the purpose of their visit. This allows the visitor handling department to respond appropriately according to the visitor's situation.

[0124] The visitor handling unit can also estimate the visitor's emotions and prioritize responses based on the estimated visitor's emotions. For example, if the visitor is nervous, the response can be prioritized and tailored to provide a sense of security. If the visitor is in a hurry, the response can be quick and concise, and the purpose of the visit can be quickly confirmed. Furthermore, if the visitor is relaxed, the response can include a detailed explanation, and the purpose of the visit can be carefully confirmed. This allows the visitor handling unit to prioritize responses according to the visitor's emotions.

[0125] The visitor response unit can also prioritize relevant responses by taking into account the visitor's geographic location information. For example, if the visitor is a local resident, a friendly response can be provided to confirm the purpose of their visit. If the visitor is from a distant location, a response including a detailed explanation can be provided to carefully confirm the purpose of their visit. Furthermore, if the visitor is from a specific area, a response including information related to that area can be provided to confirm the purpose of their visit. This allows the visitor response unit to provide an appropriate response based on the visitor's geographic location information.

[0126] The processing flow of the second embodiment will be briefly explained below.

[0127] Step 1: The visitor reception department collects visitor information, including names, facial photographs, and audio data. The visitor reception department can collect visitor information through the intercom, as well as using cameras and microphones. Step 2: The facial recognition unit analyzes the information collected by the visitor reception unit. The facial recognition unit analyzes and recognizes the visitor's face using image analysis algorithms and deep learning technology. Step 3: The voice recognition unit analyzes the voice collected by the visitor reception unit. The voice recognition unit analyzes and recognizes the visitor's voice using voice analysis technology and natural language processing technology. Step 4: The notification unit issues a notification based on the information obtained by the face recognition unit and the voice recognition unit. The notification unit issues a notification using a push notification to the smartphone, an email notification, an alert sound, or the like.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0132] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0164] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a visitor relations department that collects visitor information; a face recognition unit that analyzes the information collected by the visitor interaction unit; a unit for recognizing the face of a visitor based on the information analyzed by the face recognition unit; a voice recognition unit that analyzes the voice collected by the visitor response unit; a notification unit that issues a notification based on information obtained by the face recognition unit and the voice recognition unit; Equipped with A system characterized by:

2. Equipped with a baggage receiving device The system of claim 1 .

3. Equipped with an alarm system The system of claim 1 .

4. Equipped with a camera device The system of claim 1 .

5. The visitor reception unit Inferring visitor sentiment and tailoring responses based on the estimated sentiment The system of claim 1 .

6. The visitor reception unit Analyze the visitor's past visit history and decide how to respond The system of claim 1 .

7. The visitor reception unit Tailor your responses based on the visitor's current situation The system of claim 1 .

8. The visitor reception unit Estimate visitor sentiment and prioritize responses based on the estimated visitor sentiment The system of claim 1 .

9. The visitor reception unit Considers the visitor's geographic location to prioritize relevant responses The system of claim 1 .

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