system

Abstract

The system according to this embodiment aims to efficiently locate lost or abandoned items and contact their owners. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a discovery unit, an identification unit, and a communication unit. The collection unit collects data from surveillance cameras and sensors. The analysis unit analyzes the data collected by the collection unit. The discovery unit finds abandoned or lost items based on the data analyzed by the analysis unit. The identification unit identifies the owners of the abandoned or lost items found by the discovery unit. The communication unit contacts the owners identified by the identification unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the discovery of lost items and unattended objects and the identification of their owners in public places, offices, and commercial facilities are not efficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently discover lost items and unattended objects and contact their owners.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a discovery unit, an identification unit, and a communication unit. The collection unit collects data from surveillance cameras and sensors. The analysis unit analyzes the data collected by the collection unit. The discovery unit finds abandoned or lost items based on the data analyzed by the analysis unit. The identification unit identifies the owners of the abandoned or lost items found by the discovery unit. The communication unit contacts the owners identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently locate lost or abandoned items and contact their owners. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The lost and found tracking and contact agent according to an embodiment of the present invention is an agent that solves the problem of users forgetting or losing their personal belongings in public places, offices, and commercial facilities. The lost and found tracking and contact agent is an AI agent that autonomously analyzes data from surveillance cameras and sensors to find abandoned or lost items. Furthermore, the lost and found tracking and contact agent analyzes the circumstances under which the item was abandoned and identifies the owner. If the owner is registered in the facility system, the lost and found tracking and contact agent has the function to contact the owner directly. This allows the owner to realize they have forgotten something before leaving the facility. For example, the lost and found tracking and contact agent is an AI agent that autonomously analyzes data from surveillance cameras and sensors. In this case, surveillance cameras and sensors are installed in various locations within the facility and collect data in real time. For example, cameras and sensors installed in the entrance of a shopping mall or a conference room in an office monitor user movements and detect abandoned or lost items. Next, the lost and found tracking and contact agent analyzes the circumstances of the detected abandoned or lost items. For example, the system analyzes camera footage and sensor data to determine how an item was left behind. This allows the system to understand the circumstances under which the owner misplaced or lost the item. Furthermore, the lost and found tracking and contact agent utilizes information registered in facility systems to identify the owner. For instance, it refers to information registered in facility access control systems and membership registration systems to identify the owner. Once the owner is identified, the lost and found tracking and contact agent has the function to contact the owner directly. For example, it can send a notification to the owner's smartphone or display a message on a sign within the facility to inform the owner that their lost item has been found. This system allows owners to realize they have lost something before leaving the facility. For example, if a user leaves their wallet behind while shopping in a shopping mall, the lost and found tracking and contact agent can detect the wallet and send a notification to the owner, allowing them to retrieve their wallet immediately.Furthermore, if an employee leaves their laptop behind during a meeting in the office, the lost and found tracking and contact agent can locate the laptop and send a notification to the owner, allowing them to retrieve it immediately. In this way, the lost and found tracking and contact agent solves the problem of users forgetting or losing their personal belongings in public places, offices, and commercial facilities. The AI ​​agent autonomously analyzes data from surveillance cameras and sensors to find abandoned or lost items, identify the owners, and contact them directly, allowing owners to realize they've left something behind before leaving the facility. As a result, the lost and found tracking and contact agent can quickly resolve the problem of users forgetting or losing their personal belongings.

[0029] The lost item tracking and communication agent according to this embodiment comprises a collection unit, an analysis unit, a discovery unit, an identification unit, and a communication unit. The collection unit collects data from surveillance cameras and sensors. The collection unit collects data from various locations within a facility, for example, using surveillance cameras and sensors. The collection unit can collect data from cameras and sensors installed, for example, in the entrance of a shopping mall or a conference room in an office. The collection unit can collect, for example, video data, audio data, temperature data, etc. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data, for example, using AI. The analysis unit can, for example, analyze video data to detect abandoned or lost items. The analysis unit can, for example, analyze audio data to detect abandoned or lost items. The analysis unit can, for example, analyze temperature data to detect abandoned or lost items. The discovery unit finds abandoned or lost items based on the data analyzed by the analysis unit. The discovery unit uses, for example, AI to locate abandoned or lost items based on analyzed data. The discovery unit can locate abandoned or lost items based on, for example, video data. The discovery unit can locate abandoned or lost items based on, for example, audio data. The discovery unit can locate abandoned or lost items based on, for example, temperature data. The identification unit identifies the owner of the abandoned or lost item discovered by the discovery unit. The identification unit uses, for example, AI to identify the owner of the discovered abandoned or lost item. The identification unit can use, for example, facial recognition technology to identify the owner of the discovered abandoned or lost item. The identification unit can identify the owner of the discovered abandoned or lost item by referring to information registered in, for example, a facility's access control system or membership registration system. The contact unit contacts the owner identified by the identification unit. The contact unit uses, for example, AI to contact the identified owner. The contact unit can, for example, send a notification to the owner's smartphone. The contact unit can, for example, display a message on a signboard within the facility. The liaison department can, for example, send an email.As a result, the lost item tracking and contact agent according to the embodiment can quickly contact the owner of the lost item by collecting, analyzing, locating, identifying, and contacting data from surveillance cameras and sensors.

[0030] The data collection unit collects data from surveillance cameras and sensors. For example, it can collect data from various locations within a facility using surveillance cameras and sensors. Specifically, it can collect data from cameras and sensors installed in any location within the facility, such as shopping mall entrances, office conference rooms, parking lots, and corridors. The data collection unit can collect diverse types of data, including video data, audio data, and temperature data. For instance, surveillance cameras capture high-resolution video in real time, and sensors detect changes in sound and temperature. This data is centrally managed by the data collection unit and transmitted to a central database. The data collection unit can flexibly adapt to specific situations and conditions by adjusting the data collection frequency and resolution. For example, by increasing the data collection frequency during specific times or events, more detailed information can be obtained. Furthermore, the data collection unit prioritizes privacy protection during data collection and can anonymize or encrypt data as needed. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data. Specifically, it can analyze video data to detect abandoned or lost items. Using image recognition technology, the AI ​​identifies objects in the video and detects objects or movements that are different from normal situations. For example, it can identify a bag left at the entrance of a shopping mall or a laptop forgotten in an office conference room. It can also analyze audio data to detect abandoned or lost items. Using speech recognition technology, the AI ​​analyzes the surrounding audio environment and detects unusual sounds or specific audio patterns. For example, it can detect the sound of someone dropping something or a cry for help. Furthermore, it can analyze temperature data to detect abandoned or lost items. The AI ​​analyzes data from temperature sensors and detects patterns that differ from normal temperature changes. For example, it can identify cases where an object left for a long time has a temperature different from the surrounding temperature. This allows the analysis unit to quickly and accurately analyze the collected data and understand the surrounding situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and anomaly detection. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The detection unit finds abandoned or lost items based on data analyzed by the analysis unit. For example, the detection unit uses AI to find abandoned or lost items based on the analyzed data. Specifically, it can find abandoned or lost items based on video data. The AI ​​uses image recognition technology to identify objects in the video and detect objects or movements that are different from normal situations. For example, it can identify a bag left in the entrance of a shopping mall or a laptop left behind in an office conference room. It can also find abandoned or lost items based on audio data. The AI ​​uses speech recognition technology to analyze the surrounding audio environment and detect unusual sounds or specific audio patterns. For example, it can detect the sound of someone dropping something or a cry for help. Furthermore, it can also find abandoned or lost items based on temperature data. The AI ​​analyzes data from temperature sensors and detects patterns that are different from normal temperature changes. For example, it can identify cases where an object that has been left for a long time has a temperature different from the ambient temperature. As a result, the detection unit can quickly and accurately find abandoned or lost items based on the analyzed data. Furthermore, the detection unit can record the location information and characteristics of the discovered object, which can then be used for subsequent processing. This allows the detection unit to efficiently and effectively locate abandoned or lost items, thereby improving the overall performance of the system.

[0033] The identification unit identifies the owners of abandoned or lost items discovered by the discovery unit. The identification unit uses AI, for example, to identify the owners of discovered abandoned or lost items. Specifically, it can use facial recognition technology to identify the owners of discovered abandoned or lost items. The AI ​​analyzes video data from surveillance cameras and recognizes the faces of people who were near the discovered object. For example, it can identify the owner of a bag left at the entrance of a shopping mall or the owner of a laptop left in an office conference room. It can also identify the owners of discovered abandoned or lost items by referring to information registered in facility access control systems or membership registration systems. For example, it can identify a person who entered a conference room by referring to office access records. This allows the identification unit to quickly and accurately identify the owners of discovered abandoned or lost items. Furthermore, the identification unit can obtain the owner's contact information and use it for subsequent contact processing. This allows the identification unit to efficiently and effectively identify the owners of abandoned or lost items and improve the overall performance of the system.

[0034] The liaison department contacts the owner identified by the identification department. The liaison department uses AI, for example, to contact the identified owner. Specifically, it can send notifications to the owner's smartphone. Based on the owner's contact information, the AI ​​sends a push notification to the smartphone informing them that the lost item has been found. For example, it can send a notification to the smartphone of the owner of a bag left at the entrance of a shopping mall. It can also display messages on information boards within the facility. The AI ​​displays messages on digital signage and information boards within the facility informing the owner that the lost item has been found. For example, it can display a message on the information board in a conference room for the owner of a laptop left in a conference room at an office. Furthermore, it can also send emails. Based on the owner's email address, the AI ​​sends an email informing them that the lost item has been found. This allows the liaison department to contact the identified owner quickly and reliably. In addition, the liaison department can collect feedback from the owner and continuously improve the accuracy and effectiveness of the communication. For example, it can review the communication method and message content based on the owner's response. Furthermore, the liaison department can reliably transmit information using multiple communication methods. For example, in addition to smartphone notifications, they can use voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the liaison department to contact owners quickly and reliably, and to return lost items smoothly.

[0035] The identification unit includes a facial recognition unit that uses facial recognition technology to identify the owner. For example, the identification unit can use facial recognition technology to identify the owner of abandoned or lost items that have been found. For example, the identification unit can use AI to perform facial recognition technology. For example, the identification unit can use facial recognition technology to refer to information registered in facility access control systems or membership registration systems to identify the owner of abandoned or lost items that have been found. This improves the accuracy of owner identification by utilizing facial recognition technology.

[0036] The communication unit includes a notification unit that sends notifications to the owner's smartphone. The communication unit can, for example, send notifications to the owner's smartphone. The communication unit can, for example, use AI to send notifications to the owner's smartphone. The communication unit can, for example, quickly contact the owner by sending notifications to the owner's smartphone. This allows for quick contact with the owner by sending notifications to the owner's smartphone.

[0037] The data collection unit can dynamically change the frequency of data collection, taking into account the congestion level within the facility. For example, if the facility is crowded, the data collection unit increases the frequency of data collection to quickly grasp the situation. For example, if the facility is not crowded, the data collection unit decreases the frequency of data collection to conserve resources. For example, if a particular area is crowded, the data collection unit increases the frequency of data collection for that area to collect data efficiently. This allows for efficient data collection by adjusting the frequency of data collection according to the congestion level within the facility. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input facility congestion data into a generating AI and have the generating AI dynamically change the frequency of data collection.

[0038] The data collection unit can expand the scope of data collection depending on specific events or time periods. For example, the data collection unit can expand the scope of data collection during an event to cover the entire event area. For example, the data collection unit can expand the scope of data collection during peak hours to understand congestion levels. For example, the data collection unit can expand the scope of data collection during specific time periods to efficiently collect data. This enables efficient data collection by expanding the scope of data collection depending on specific events or time periods. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for specific events or time periods into a generating AI and have the generating AI execute the expansion of the data collection scope.

[0039] The data collection unit can simultaneously collect environmental data such as temperature and humidity within the facility and use it for analysis. For example, the data collection unit can collect temperature data within the facility and use it for analysis. For example, the data collection unit can collect humidity data within the facility and use it for analysis. For example, the data collection unit can collect environmental data within the facility and use it for analysis. By collecting and using environmental data within the facility, more detailed analysis becomes possible. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data from within the facility into a generating AI and use it for analysis.

[0040] The collection unit can collect audio data within the facility and use it for analysis. For example, the collection unit can collect audio data within the facility and use it for analysis. For example, the collection unit can collect conversation data within the facility and use it for analysis. For example, the collection unit can collect ambient sound data within the facility and use it for analysis. By collecting and using audio data within the facility, more detailed analysis becomes possible. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input audio data from within the facility into a generating AI and use it for analysis.

[0041] The analysis unit can detect abnormal patterns from the collected data and perform detailed analysis. For example, the analysis unit can detect abnormal patterns from the collected data and perform detailed analysis. For example, the analysis unit can detect abnormal patterns and identify their causes. For example, the analysis unit can detect abnormal patterns and propose countermeasures. In this way, by detecting abnormal patterns and performing detailed analysis, it is possible to identify the causes of anomalies and propose countermeasures. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform abnormal pattern detection and detailed analysis.

[0042] The analysis unit can identify new trends by comparing them with past data. For example, the analysis unit identifies new trends by comparing them with past data. For example, the analysis unit identifies new trends and evaluates their impact. For example, the analysis unit identifies new trends and proposes countermeasures. This enables efficient analysis by identifying new trends by comparing them with past data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the identification of new trends.

[0043] The analysis unit can analyze the collected data in real time and provide results immediately. For example, the analysis unit can analyze the collected data in real time and provide results immediately. For example, the analysis unit can perform analysis in real time and immediately notify if an anomaly is detected. For example, the analysis unit can perform analysis in real time and immediately propose necessary countermeasures. This enables a rapid response by analyzing the collected data in real time and providing results immediately. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform real-time analysis and provide results.

[0044] The analysis unit can integrate and analyze data from different sensors. For example, the analysis unit can integrate and analyze data from different sensors. For example, the analysis unit can integrate data from different sensors and provide a comprehensive analysis result. For example, the analysis unit can integrate data from different sensors and detect anomalies. In this way, by integrating and analyzing data from different sensors, a comprehensive analysis result can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from different sensors into a generating AI and have the generating AI perform data integration and analysis.

[0045] The detection unit can increase the frequency of finding abandoned or lost items in a specific area. For example, the detection unit can increase the frequency of finding abandoned or lost items in a specific area. For example, the detection unit can increase the frequency of finding abandoned or lost items in a specific area and improve the safety of that area. For example, the detection unit can increase the frequency of finding abandoned or lost items in a specific area and respond quickly. This makes it possible to respond quickly by increasing the frequency of finding items in a specific area. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from a specific area into a generating AI and have the generating AI perform the task of increasing the frequency of finding items.

[0046] The discovery unit can improve the accuracy of discoveries by referring to past discovery data. For example, the discovery unit improves the accuracy of discoveries by referring to past discovery data. For example, the discovery unit adjusts algorithms to improve the accuracy of discoveries by referring to past discovery data. For example, the discovery unit proposes measures to improve the accuracy of discoveries by referring to past discovery data. As a result, the accuracy of discoveries is improved by referring to past discovery data. Some or all of the above processes in the discovery unit may be performed using AI, for example, or without using AI. For example, the discovery unit can input past discovery data into a generating AI and have the generating AI perform the task of improving the accuracy of discoveries.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] The data collection unit can adjust the timing of data collection considering the lighting conditions within the facility. For example, if the facility is dark, the data collection unit increases the frequency of data collection to quickly grasp the situation. If the facility is bright, the data collection unit decreases the frequency of data collection to conserve resources. If a specific area is dark, the data collection unit increases the frequency of data collection for that area to collect data efficiently. In this way, by adjusting the timing of data collection according to the lighting conditions within the facility, efficient data collection becomes possible.

[0049] The detection unit can improve its detection accuracy by considering environmental data such as temperature and humidity within the facility. For example, the detection unit can improve its detection accuracy by referring to temperature data within the facility. The detection unit can improve its detection accuracy by referring to humidity data within the facility. The detection unit can improve its detection accuracy by referring to environmental data within the facility. In this way, the detection accuracy is improved by considering the environmental data within the facility.

[0050] The collection unit can collect audio data from within the facility and use it for analysis. For example, the collection unit can collect audio data from within the facility and use it for analysis. The collection unit can collect conversation data from within the facility and use it for analysis. The collection unit can collect ambient sound data from within the facility and use it for analysis. By collecting and using audio data from within the facility, more detailed analysis becomes possible.

[0051] The data collection unit can dynamically change the frequency of data collection, taking into account the congestion level within the facility. For example, if the facility is crowded, the data collection frequency is increased to quickly grasp the situation. If the facility is not crowded, the data collection frequency is decreased to conserve resources. If a specific area is crowded, the data collection frequency for that area is increased to collect data efficiently. In this way, by adjusting the data collection frequency according to the congestion level within the facility, efficient data collection becomes possible.

[0052] The data collection unit can expand its data collection scope depending on specific events or time periods. For example, it can expand the data collection scope during events to cover the entire event area. It can also expand the data collection scope during peak hours to understand congestion levels. By expanding the data collection scope during specific time periods, data can be collected efficiently. This allows for efficient data collection by expanding the data collection scope according to specific events or time periods.

[0053] The analysis unit can identify new trends by comparing them with past data. For example, it can identify new trends by comparing them with past data. It can identify new trends and evaluate their impact. It can identify new trends and propose countermeasures. This enables efficient analysis by identifying new trends by comparing them with past data.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit collects data from surveillance cameras and sensors. The collection unit can collect video data, audio data, temperature data, etc., from cameras and sensors installed, for example, in the entrance of a shopping mall or a conference room in an office. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze video data, audio data, and temperature data to detect abandoned or lost items. Step 3: The discovery unit finds abandoned or lost items based on the data analyzed by the analysis unit. For example, the discovery unit can find abandoned or lost items based on video data, audio data, and temperature data analyzed using AI. Step 4: The Identification Unit identifies the owner of the abandoned or lost item discovered by the Discovery Unit. The Identification Unit can, for example, use AI to identify the owner by referring to information registered in facial recognition technology, facility access control systems, membership registration systems, etc. Step 5: The liaison department contacts the owner identified by the identification department. The liaison department can, for example, use AI to send a notification to the owner's smartphone, display a message on a sign within the facility, or send an email.

[0056] (Example of form 2) The lost and found tracking and contact agent according to an embodiment of the present invention is an agent that solves the problem of users forgetting or losing their personal belongings in public places, offices, and commercial facilities. The lost and found tracking and contact agent is an AI agent that autonomously analyzes data from surveillance cameras and sensors to find abandoned or lost items. Furthermore, the lost and found tracking and contact agent analyzes the circumstances under which the item was abandoned and identifies the owner. If the owner is registered in the facility system, the lost and found tracking and contact agent has the function to contact the owner directly. This allows the owner to realize they have forgotten something before leaving the facility. For example, the lost and found tracking and contact agent is an AI agent that autonomously analyzes data from surveillance cameras and sensors. In this case, surveillance cameras and sensors are installed in various locations within the facility and collect data in real time. For example, cameras and sensors installed in the entrance of a shopping mall or a conference room in an office monitor user movements and detect abandoned or lost items. Next, the lost and found tracking and contact agent analyzes the circumstances of the detected abandoned or lost items. For example, the system analyzes camera footage and sensor data to determine how an item was left behind. This allows the system to understand the circumstances under which the owner misplaced or lost the item. Furthermore, the lost and found tracking and contact agent utilizes information registered in facility systems to identify the owner. For instance, it refers to information registered in facility access control systems and membership registration systems to identify the owner. Once the owner is identified, the lost and found tracking and contact agent has the function to contact the owner directly. For example, it can send a notification to the owner's smartphone or display a message on a sign within the facility to inform the owner that their lost item has been found. This system allows owners to realize they have lost something before leaving the facility. For example, if a user leaves their wallet behind while shopping in a shopping mall, the lost and found tracking and contact agent can detect the wallet and send a notification to the owner, allowing them to retrieve their wallet immediately.Furthermore, if an employee leaves their laptop behind during a meeting in the office, the lost and found tracking and contact agent can locate the laptop and send a notification to the owner, allowing them to retrieve it immediately. In this way, the lost and found tracking and contact agent solves the problem of users forgetting or losing their personal belongings in public places, offices, and commercial facilities. The AI ​​agent autonomously analyzes data from surveillance cameras and sensors to find abandoned or lost items, identify the owners, and contact them directly, allowing owners to realize they've left something behind before leaving the facility. As a result, the lost and found tracking and contact agent can quickly resolve the problem of users forgetting or losing their personal belongings.

[0057] The lost item tracking and communication agent according to this embodiment comprises a collection unit, an analysis unit, a discovery unit, an identification unit, and a communication unit. The collection unit collects data from surveillance cameras and sensors. The collection unit collects data from various locations within a facility, for example, using surveillance cameras and sensors. The collection unit can collect data from cameras and sensors installed, for example, in the entrance of a shopping mall or a conference room in an office. The collection unit can collect, for example, video data, audio data, temperature data, etc. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data, for example, using AI. The analysis unit can, for example, analyze video data to detect abandoned or lost items. The analysis unit can, for example, analyze audio data to detect abandoned or lost items. The analysis unit can, for example, analyze temperature data to detect abandoned or lost items. The discovery unit finds abandoned or lost items based on the data analyzed by the analysis unit. The discovery unit uses, for example, AI to locate abandoned or lost items based on analyzed data. The discovery unit can locate abandoned or lost items based on, for example, video data. The discovery unit can locate abandoned or lost items based on, for example, audio data. The discovery unit can locate abandoned or lost items based on, for example, temperature data. The identification unit identifies the owner of the abandoned or lost item discovered by the discovery unit. The identification unit uses, for example, AI to identify the owner of the discovered abandoned or lost item. The identification unit can use, for example, facial recognition technology to identify the owner of the discovered abandoned or lost item. The identification unit can identify the owner of the discovered abandoned or lost item by referring to information registered in, for example, a facility's access control system or membership registration system. The contact unit contacts the owner identified by the identification unit. The contact unit uses, for example, AI to contact the identified owner. The contact unit can, for example, send a notification to the owner's smartphone. The contact unit can, for example, display a message on a signboard within the facility. The liaison department can, for example, send an email.As a result, the lost item tracking and contact agent according to the embodiment can quickly contact the owner of the lost item by collecting, analyzing, locating, identifying, and contacting data from surveillance cameras and sensors.

[0058] The data collection unit collects data from surveillance cameras and sensors. For example, it can collect data from various locations within a facility using surveillance cameras and sensors. Specifically, it can collect data from cameras and sensors installed in any location within the facility, such as shopping mall entrances, office conference rooms, parking lots, and corridors. The data collection unit can collect diverse types of data, including video data, audio data, and temperature data. For instance, surveillance cameras capture high-resolution video in real time, and sensors detect changes in sound and temperature. This data is centrally managed by the data collection unit and transmitted to a central database. The data collection unit can flexibly adapt to specific situations and conditions by adjusting the data collection frequency and resolution. For example, by increasing the data collection frequency during specific times or events, more detailed information can be obtained. Furthermore, the data collection unit prioritizes privacy protection during data collection and can anonymize or encrypt data as needed. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0059] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the collected data. Specifically, it can analyze video data to detect abandoned or lost items. Using image recognition technology, the AI ​​identifies objects in the video and detects objects or movements that are different from normal situations. For example, it can identify a bag left at the entrance of a shopping mall or a laptop forgotten in an office conference room. It can also analyze audio data to detect abandoned or lost items. Using speech recognition technology, the AI ​​analyzes the surrounding audio environment and detects unusual sounds or specific audio patterns. For example, it can detect the sound of someone dropping something or a cry for help. Furthermore, it can analyze temperature data to detect abandoned or lost items. The AI ​​analyzes data from temperature sensors and detects patterns that differ from normal temperature changes. For example, it can identify cases where an object left for a long time has a temperature different from the surrounding temperature. This allows the analysis unit to quickly and accurately analyze the collected data and understand the surrounding situation in real time. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend analysis and anomaly detection. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0060] The detection unit finds abandoned or lost items based on data analyzed by the analysis unit. For example, the detection unit uses AI to find abandoned or lost items based on the analyzed data. Specifically, it can find abandoned or lost items based on video data. The AI ​​uses image recognition technology to identify objects in the video and detect objects or movements that are different from normal situations. For example, it can identify a bag left in the entrance of a shopping mall or a laptop left behind in an office conference room. It can also find abandoned or lost items based on audio data. The AI ​​uses speech recognition technology to analyze the surrounding audio environment and detect unusual sounds or specific audio patterns. For example, it can detect the sound of someone dropping something or a cry for help. Furthermore, it can also find abandoned or lost items based on temperature data. The AI ​​analyzes data from temperature sensors and detects patterns that are different from normal temperature changes. For example, it can identify cases where an object that has been left for a long time has a temperature different from the ambient temperature. As a result, the detection unit can quickly and accurately find abandoned or lost items based on the analyzed data. Furthermore, the detection unit can record the location information and characteristics of the discovered object, which can then be used for subsequent processing. This allows the detection unit to efficiently and effectively locate abandoned or lost items, thereby improving the overall performance of the system.

[0061] The identification unit identifies the owners of abandoned or lost items discovered by the discovery unit. The identification unit uses AI, for example, to identify the owners of discovered abandoned or lost items. Specifically, it can use facial recognition technology to identify the owners of discovered abandoned or lost items. The AI ​​analyzes video data from surveillance cameras and recognizes the faces of people who were near the discovered object. For example, it can identify the owner of a bag left at the entrance of a shopping mall or the owner of a laptop left in an office conference room. It can also identify the owners of discovered abandoned or lost items by referring to information registered in facility access control systems or membership registration systems. For example, it can identify a person who entered a conference room by referring to office access records. This allows the identification unit to quickly and accurately identify the owners of discovered abandoned or lost items. Furthermore, the identification unit can obtain the owner's contact information and use it for subsequent contact processing. This allows the identification unit to efficiently and effectively identify the owners of abandoned or lost items and improve the overall performance of the system.

[0062] The liaison department contacts the owner identified by the identification department. The liaison department uses AI, for example, to contact the identified owner. Specifically, it can send notifications to the owner's smartphone. Based on the owner's contact information, the AI ​​sends a push notification to the smartphone informing them that the lost item has been found. For example, it can send a notification to the smartphone of the owner of a bag left at the entrance of a shopping mall. It can also display messages on information boards within the facility. The AI ​​displays messages on digital signage and information boards within the facility informing the owner that the lost item has been found. For example, it can display a message on the information board in a conference room for the owner of a laptop left in a conference room at an office. Furthermore, it can also send emails. Based on the owner's email address, the AI ​​sends an email informing them that the lost item has been found. This allows the liaison department to contact the identified owner quickly and reliably. In addition, the liaison department can collect feedback from the owner and continuously improve the accuracy and effectiveness of the communication. For example, it can review the communication method and message content based on the owner's response. Furthermore, the liaison department can reliably transmit information using multiple communication methods. For example, in addition to smartphone notifications, they can use voice calls, SMS, and email in combination to ensure that important information is delivered reliably. This allows the liaison department to contact owners quickly and reliably, and to return lost items smoothly.

[0063] The identification unit includes a facial recognition unit that uses facial recognition technology to identify the owner. For example, the identification unit can use facial recognition technology to identify the owner of abandoned or lost items that have been found. For example, the identification unit can use AI to perform facial recognition technology. For example, the identification unit can use facial recognition technology to refer to information registered in facility access control systems or membership registration systems to identify the owner of abandoned or lost items that have been found. This improves the accuracy of owner identification by utilizing facial recognition technology.

[0064] The communication unit includes a notification unit that sends notifications to the owner's smartphone. The communication unit can, for example, send notifications to the owner's smartphone. The communication unit can, for example, use AI to send notifications to the owner's smartphone. The communication unit can, for example, quickly contact the owner by sending notifications to the owner's smartphone. This allows for quick contact with the owner by sending notifications to the owner's smartphone.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is anxious, the data collection unit increases the frequency of data collection to quickly grasp the situation. For example, if the user is relaxed, the data collection unit decreases the frequency of data collection to conserve resources. For example, if the user is in a crowded place, the data collection unit adjusts the timing of data collection to collect data efficiently. This enables efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0066] The data collection unit can dynamically change the frequency of data collection, taking into account the congestion level within the facility. For example, if the facility is crowded, the data collection unit increases the frequency of data collection to quickly grasp the situation. For example, if the facility is not crowded, the data collection unit decreases the frequency of data collection to conserve resources. For example, if a particular area is crowded, the data collection unit increases the frequency of data collection for that area to collect data efficiently. This allows for efficient data collection by adjusting the frequency of data collection according to the congestion level within the facility. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input facility congestion data into a generating AI and have the generating AI dynamically change the frequency of data collection.

[0067] The data collection unit can expand the scope of data collection depending on specific events or time periods. For example, the data collection unit can expand the scope of data collection during an event to cover the entire event area. For example, the data collection unit can expand the scope of data collection during peak hours to understand congestion levels. For example, the data collection unit can expand the scope of data collection during specific time periods to efficiently collect data. This enables efficient data collection by expanding the scope of data collection depending on specific events or time periods. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data for specific events or time periods into a generating AI and have the generating AI execute the expansion of the data collection scope.

[0068] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is anxious, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a crowded place, the data collection unit will prioritize collecting data related to the crowding situation. This enables efficient data collection by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0069] The data collection unit can simultaneously collect environmental data such as temperature and humidity within the facility and use it for analysis. For example, the data collection unit can collect temperature data within the facility and use it for analysis. For example, the data collection unit can collect humidity data within the facility and use it for analysis. For example, the data collection unit can collect environmental data within the facility and use it for analysis. By collecting and using environmental data within the facility, more detailed analysis becomes possible. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input environmental data from within the facility into a generating AI and use it for analysis.

[0070] The collection unit can collect audio data within the facility and use it for analysis. For example, the collection unit can collect audio data within the facility and use it for analysis. For example, the collection unit can collect conversation data within the facility and use it for analysis. For example, the collection unit can collect ambient sound data within the facility and use it for analysis. By collecting and using audio data within the facility, more detailed analysis becomes possible. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input audio data from within the facility into a generating AI and use it for analysis.

[0071] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is anxious, the analysis unit uses an algorithm that performs a rapid analysis. For example, if the user is relaxed, the analysis unit uses an algorithm that performs a detailed analysis. For example, if the user is in a crowded place, the analysis unit uses an algorithm that performs an analysis appropriate to the crowding situation. This allows for efficient analysis by adjusting the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the analysis algorithm.

[0072] The analysis unit can detect abnormal patterns from the collected data and perform detailed analysis. For example, the analysis unit can detect abnormal patterns from the collected data and perform detailed analysis. For example, the analysis unit can detect abnormal patterns and identify their causes. For example, the analysis unit can detect abnormal patterns and propose countermeasures. In this way, by detecting abnormal patterns and performing detailed analysis, it is possible to identify the causes of anomalies and propose countermeasures. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform abnormal pattern detection and detailed analysis.

[0073] The analysis unit can identify new trends by comparing them with past data. For example, the analysis unit identifies new trends by comparing them with past data. For example, the analysis unit identifies new trends and evaluates their impact. For example, the analysis unit identifies new trends and proposes countermeasures. This enables efficient analysis by identifying new trends by comparing them with past data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past data into a generating AI and have the generating AI perform the identification of new trends.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. This allows for the efficient provision of analysis results by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0075] The analysis unit can analyze the collected data in real time and provide results immediately. For example, the analysis unit can analyze the collected data in real time and provide results immediately. For example, the analysis unit can perform analysis in real time and immediately notify if an anomaly is detected. For example, the analysis unit can perform analysis in real time and immediately propose necessary countermeasures. This enables a rapid response by analyzing the collected data in real time and providing results immediately. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform real-time analysis and provide results.

[0076] The analysis unit can integrate and analyze data from different sensors. For example, the analysis unit can integrate and analyze data from different sensors. For example, the analysis unit can integrate data from different sensors and provide a comprehensive analysis result. For example, the analysis unit can integrate data from different sensors and detect anomalies. In this way, by integrating and analyzing data from different sensors, a comprehensive analysis result can be provided. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from different sensors into a generating AI and have the generating AI perform data integration and analysis.

[0077] The discovery unit can estimate the user's emotions and adjust the discovery criteria based on the estimated emotions. For example, if the user is anxious, the discovery unit will use criteria for rapid discovery. For example, if the user is relaxed, the discovery unit will use criteria for detailed discovery. For example, if the user is in a crowded place, the discovery unit will use criteria for discovery appropriate to the crowd situation. This allows for efficient discovery by adjusting the discovery criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input user emotion data into the generative AI and have the generative AI adjust the discovery criteria.

[0078] The detection unit can increase the frequency of finding abandoned or lost items in a specific area. For example, the detection unit can increase the frequency of finding abandoned or lost items in a specific area. For example, the detection unit can increase the frequency of finding abandoned or lost items in a specific area and improve the safety of that area. For example, the detection unit can increase the frequency of finding abandoned or lost items in a specific area and respond quickly. This makes it possible to respond quickly by increasing the frequency of finding items in a specific area. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input data from a specific area into a generating AI and have the generating AI perform the task of increasing the frequency of finding items.

[0079] The discovery unit can improve the accuracy of discoveries by referring to past discovery data. For example, the discovery unit improves the accuracy of discoveries by referring to past discovery data. For example, the discovery unit adjusts algorithms to improve the accuracy of discoveries by referring to past discovery data. For example, the discovery unit proposes measures to improve the accuracy of discoveries by referring to past discovery data. As a result, the accuracy of discoveries is improved by referring to past discovery data. Some or all of the above processes in the discovery unit may be performed using AI, for example, or without using AI. For example, the discovery unit can input past discovery data into a generating AI and have the generating AI perform the task of improving the accuracy of discoveries.

[0080] The discovery unit can estimate the user's emotions and adjust the display method of the discovery results based on the estimated user emotions. For example, if the user is nervous, the discovery unit provides a simple and highly visible display method. For example, if the user is relaxed, the discovery unit provides a display method that includes detailed information. For example, if the user is in a hurry, the discovery unit provides a display method that gets straight to the point. This allows for the efficient provision of discovery results by adjusting the display method of the discovery results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the discovery unit may be performed using AI, for example, or without AI. For example, the discovery unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the discovery results.

[0081] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0082] The data collection unit can adjust the timing of data collection considering the lighting conditions within the facility. For example, if the facility is dark, the data collection unit increases the frequency of data collection to quickly grasp the situation. If the facility is bright, the data collection unit decreases the frequency of data collection to conserve resources. If a specific area is dark, the data collection unit increases the frequency of data collection for that area to collect data efficiently. In this way, by adjusting the timing of data collection according to the lighting conditions within the facility, efficient data collection becomes possible.

[0083] The analysis unit can estimate the user's emotions and prioritize the analysis results based on those emotions. For example, if the user is anxious, important analysis results will be displayed first. If the user is relaxed, detailed analysis results will be displayed. If the user is in a crowded place, analysis results related to the crowding situation will be displayed first. This allows for the efficient provision of analysis results by prioritizing them according to the user's emotions.

[0084] The detection unit can improve its detection accuracy by considering environmental data such as temperature and humidity within the facility. For example, the detection unit can improve its detection accuracy by referring to temperature data within the facility. The detection unit can improve its detection accuracy by referring to humidity data within the facility. The detection unit can improve its detection accuracy by referring to environmental data within the facility. In this way, the detection accuracy is improved by considering the environmental data within the facility.

[0085] The communication unit can estimate the user's emotions and adjust the communication method based on that estimation. For example, if the user is anxious, it will use a method that allows for quick communication. If the user is relaxed, it will use a more detailed communication method. If the user is in a crowded place, it will use a communication method appropriate to the crowd situation. This allows for more efficient communication by adjusting the communication method according to the user's emotions.

[0086] The collection unit can collect audio data from within the facility and use it for analysis. For example, the collection unit can collect audio data from within the facility and use it for analysis. The collection unit can collect conversation data from within the facility and use it for analysis. The collection unit can collect ambient sound data from within the facility and use it for analysis. By collecting and using audio data from within the facility, more detailed analysis becomes possible.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on those emotions. For example, if the user is anxious, it uses an algorithm that performs a rapid analysis. If the user is relaxed, it uses an algorithm that performs a detailed analysis. If the user is in a crowded place, it uses an algorithm that performs an analysis appropriate to the crowding situation. This allows for efficient analysis by adjusting the analysis algorithm according to the user's emotions.

[0088] The data collection unit can dynamically change the frequency of data collection, taking into account the congestion level within the facility. For example, if the facility is crowded, the data collection frequency is increased to quickly grasp the situation. If the facility is not crowded, the data collection frequency is decreased to conserve resources. If a specific area is crowded, the data collection frequency for that area is increased to collect data efficiently. In this way, by adjusting the data collection frequency according to the congestion level within the facility, efficient data collection becomes possible.

[0089] The discovery unit can estimate the user's emotions and adjust the discovery criteria based on those emotions. For example, if the user is anxious, it will use criteria for quick discovery. If the user is relaxed, it will use criteria for detailed discovery. If the user is in a crowded place, it will use criteria for discovery appropriate to the crowd situation. By adjusting the discovery criteria according to the user's emotions, efficient discovery becomes possible.

[0090] The data collection unit can expand its data collection scope depending on specific events or time periods. For example, it can expand the data collection scope during events to cover the entire event area. It can also expand the data collection scope during peak hours to understand congestion levels. By expanding the data collection scope during specific time periods, data can be collected efficiently. This allows for efficient data collection by expanding the data collection scope according to specific events or time periods.

[0091] The analysis unit can identify new trends by comparing them with past data. For example, it can identify new trends by comparing them with past data. It can identify new trends and evaluate their impact. It can identify new trends and propose countermeasures. This enables efficient analysis by identifying new trends by comparing them with past data.

[0092] The following briefly describes the processing flow for example form 2.

[0093] Step 1: The collection unit collects data from surveillance cameras and sensors. The collection unit can collect video data, audio data, temperature data, etc., from cameras and sensors installed, for example, in the entrance of a shopping mall or a conference room in an office. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, use AI to analyze video data, audio data, and temperature data to detect abandoned or lost items. Step 3: The discovery unit finds abandoned or lost items based on the data analyzed by the analysis unit. For example, the discovery unit can find abandoned or lost items based on video data, audio data, and temperature data analyzed using AI. Step 4: The Identification Unit identifies the owner of the abandoned or lost item discovered by the Discovery Unit. The Identification Unit can, for example, use AI to identify the owner by referring to information registered in facial recognition technology, facility access control systems, membership registration systems, etc. Step 5: The liaison department contacts the owner identified by the identification department. The liaison department can, for example, use AI to send a notification to the owner's smartphone, display a message on a sign within the facility, or send an email.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0096] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, identification unit, and communication unit, is implemented in, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12. The discovery unit finds abandoned or lost items based on the data analyzed by the identification processing unit 290 of the data processing unit 12. The identification unit identifies the owner of the abandoned or lost items found by the identification processing unit 290 of the data processing unit 12. The communication unit contacts the owner identified by, for example, the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0098] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0099] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0101] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0102] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0104] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0105] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0106] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0107] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0108] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0111] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0112] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, identification unit, and communication unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the collected data, for example, by the identification processing unit 290 of the data processing unit 12. The discovery unit finds abandoned or lost items based on the data analyzed by the identification processing unit 290 of the data processing unit 12. The identification unit identifies the owner of the abandoned or lost items found by the identification processing unit 290 of the data processing unit 12. The communication unit contacts the owner identified by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0115] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, identification unit, and communication unit, is implemented in, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the headset terminal 314. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12. The discovery unit finds abandoned or lost items based on the data analyzed by the identification processing unit 290 of the data processing unit 12. The identification unit identifies the owner of the abandoned or lost items found by the identification processing unit 290 of the data processing unit 12. The communication unit contacts the owner identified by, for example, the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0131] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0138] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, discovery unit, identification unit, and communication unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and sensors of the robot 414. The analysis unit analyzes the collected data by, for example, the identification unit 290 of the data processing unit 12. The discovery unit finds abandoned or lost items based on the data analyzed by the identification unit 290 of the data processing unit 12. The identification unit identifies the owner of the abandoned or lost items found by the identification unit 290 of the data processing unit 12. The communication unit contacts the owner identified by, for example, the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0148] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0149] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0150] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0151] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0152] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0153] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0154] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0155] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0156] 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.

[0157] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0158] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0159] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0160] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0161] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0162] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0163] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0164] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0165] (Note 1) A data collection unit that collects data from surveillance cameras and sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, A discovery unit that finds abandoned or lost items based on the data analyzed by the aforementioned analysis unit, The identification unit identifies the owner of the abandoned or lost item discovered by the discovery unit, The system includes a communication unit that contacts the owner identified by the specified unit. A system characterized by the following features. (Note 2) The specified part is, It is equipped with a facial recognition unit that uses facial recognition technology to identify the owner. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned liaison department, It has a notification unit that sends notifications to the owner's smartphone. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The frequency of data collection is dynamically changed to take into account the congestion level within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is Expand the scope of data collection depending on specific events or time periods. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Environmental data such as temperature and humidity within the facility will also be collected and used for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We collect and analyze audio data from within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We detect abnormal patterns in the collected data and perform detailed analysis. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Identify new trends by comparing with past data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The collected data is analyzed in real time, and the results are provided immediately. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Integrate and analyze data from different sensors. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned detection unit is We estimate the user's emotions and adjust the discovery criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned detection unit is Increase the frequency of finding abandoned or lost items in specific areas. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned detection unit is By referring to past discovery data, we can improve the accuracy of our discoveries. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned detection unit is It estimates the user's emotions and adjusts how the discovery results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data from surveillance cameras and sensors, An analysis unit analyzes the data collected by the aforementioned collection unit, A discovery unit that finds abandoned or lost items based on the data analyzed by the aforementioned analysis unit, The identification unit identifies the owner of the abandoned or lost item discovered by the discovery unit, The system includes a communication unit that contacts the owner identified by the specified unit. A system characterized by the following features.

2. The specified part is, It is equipped with a facial recognition unit that uses facial recognition technology to identify the owner. The system according to feature 1.

3. The aforementioned liaison department, It has a notification unit that sends notifications to the owner's smartphone. The system according to feature 1.

4. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is The frequency of data collection is dynamically changed to take into account the congestion level within the facility. The system according to feature 1.

6. The aforementioned collection unit is Expand the scope of data collection depending on specific events or time periods. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Environmental data such as temperature and humidity within the facility will also be collected and used for analysis. The system according to feature 1.

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