System

Abstract

An object of the system according to the embodiment is to quickly and effectively find a pet when the pet is lost.SOLUTION: A system according to an embodiment includes a generation AI, a shelter cooperation unit, a sound analysis unit, and a know-how share unit. The generated AI is used to analyze the shelter AI when the pet is missing to assist in finding the pet. The shelter cooperation part cooperates with a shelter to support finding of the pet. The bark analysis part analyzes the bark of the pet and specifies the pet having a specific bark pattern. The know-how sharing unit shares know-how and knowledge about finding and protecting pets.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to quickly and effectively locate missing pets.

[0005] The system according to the embodiment aims to quickly and effectively find a pet when it goes missing. [Means for solving the problem]

[0006] The system according to the embodiment comprises a generation AI, a shelter collaboration unit, a cry analysis unit, and a know-how sharing unit. The generation AI uses the generation AI to analyze data from shelters when pets go missing and assists in locating the pet. The shelter collaboration unit collaborates with shelters to assist in locating the pet. The cry analysis unit analyzes pet cries and identifies pets with specific cry patterns. The know-how sharing unit shares know-how and knowledge regarding the locating and protecting of pets. [Effects of the Invention]

[0007] The system according to the embodiment allows for quick and effective locating of lost pets. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A pet finding system according to an embodiment of the present invention utilizes generative AI to quickly find and protect missing pets. This system uses generative AI to expand the matching area and utilizes data on shelters and animal sounds. Furthermore, by sharing know-how and knowledge, efficient discovery and protection are achieved. This allows the pet finding system to quickly find and protect missing pets.

[0029] A pet finding system according to an embodiment includes a generation AI, a shelter collaboration unit, a cry analysis unit, and a know-how sharing unit. The generation AI analyzes data from a shelter when a pet goes missing and assists in the discovery of the pet. For example, the generation AI collects information on pets housed at the shelter and compares it with missing pets. The generation AI also analyzes pet cries and identifies pets with specific cry patterns. The shelter collaboration unit collaborates with the shelter to assist in the discovery of pets. For example, it registers photos and characteristics of pets housed at the shelter in a database and compares them with missing pets. It also collects information from the shelter in real time to help find the pet. The cry analysis unit analyzes pet cries and identifies pets with specific cry patterns. For example, it records the cries of a missing pet and inputs the data into the generation AI. The generation AI analyzes the recorded cries and compares them with cries recorded at the shelter and other locations. The know-how sharing unit shares know-how and knowledge regarding the discovery and protection of pets. For example, information such as what to do when a pet goes missing and how to work with shelters can be shared. Also, a database of past successes and failures can be compiled and shared with other users. This allows the pet discovery system to quickly find and protect missing pets.

[0030] The generation AI can analyze a pet's behavioral patterns and identify locations where the pet is likely to be found based on its behavioral history before going missing. For example, the generation AI can analyze a pet's GPS data and past behavioral history to identify locations the pet frequently visited before going missing. For example, it can prioritize searches of parks and walking routes where the pet often played. The generation AI can also analyze a pet's behavioral patterns to predict the likelihood that the pet will be in a specific location at a specific time of day. For example, if a pet has a habit of going to a specific location every evening, the generation AI can focus its search on that location during that time. The generation AI can also identify locations where the pet exhibited abnormal behavior before going missing based on the pet's behavioral history. For example, if a pet suddenly starts going to a place it doesn't usually go, the generation AI can focus its search on that location. This allows the generation AI to analyze a pet's behavioral patterns and identify locations where the pet is likely to be found.

[0031] The generation AI can predict the range of movement and hiding places based on the pet's health and age. The generation AI predicts the range of movement based on the pet's health and age. For example, elderly pets and pets in poor health have smaller ranges of movement, so the AI ​​will focus its search within those ranges. The generation AI also predicts hiding places taking the pet's health and age into account. For example, young pets are more likely to be active and move over a wide area, while older pets tend to hide in quiet places. The generation AI also predicts behavioral patterns under specific environmental conditions based on the pet's health and age. For example, pets in poor health tend to prefer cool or shady places, so the AI ​​will focus its search within those areas. This makes it possible to predict the range of movement and hiding places taking the pet's health and age into account.

[0032] When a pet goes missing, the Generator AI can collect sighting information from neighbors and match it in real time. For example, the Generator AI can build a system that collects sighting information from neighbors and matches it with missing pets in real time. For example, when a resident enters sighting information into an app, the Generator AI immediately analyzes it and compares it with the pet's characteristics. The Generator AI can also develop a dedicated app for collecting sighting information, making it easy for residents to provide information. For example, by simply entering a photo and characteristics of a pet into the app, the Generator AI can automatically match it. The Generator AI can also analyze sighting information and identify locations where the missing pet is likely to be. For example, if multiple residents report sightings in the same location, it will focus its search on that location. This makes it possible to collect sighting information from neighbors and match it in real time.

[0033] When a pet goes missing, generative AI can use drones to conduct a wide-area search and analyze the video data to identify the pet. Generative AI, for example, can build a system that uses drones to conduct a wide-area search and analyze the collected video data to identify the missing pet. For example, it can analyze video footage captured by drones in real time and compare it with the pet's characteristics. Generative AI can also develop an algorithm that automatically detects pet characteristics using a camera mounted on the drone. For example, it can identify a pet from video data based on its coat color and physique. Generative AI can also build a system that uses multiple drones to efficiently search a wide area. For example, drones can work together to share the search area, and the generative AI can analyze the collected video data. This makes it possible to conduct a wide-area search using drones and analyze the video data to identify the pet.

[0034] By integrating shelter databases, the generation AI can automatically collect and update the latest information on housed pets. A system will be built in which the generation AI will integrate shelter databases and automatically collect and update the latest information on housed pets. For example, it will link with the databases of each shelter and update information in real time. The generation AI will also periodically scan the shelter databases and automatically collect information on newly housed pets. For example, it will update the database at a set time each day. The generation AI will also integrate the shelter databases and develop a system to centrally manage information on housed pets. For example, it will consolidate data from multiple shelters onto a single platform. This will allow the shelter databases to be integrated, allowing the generation AI to automatically collect and update the latest information on housed pets.

[0035] By analyzing the health conditions and behavioral characteristics of pets at shelters, it is possible to improve the accuracy of matching with missing pets. A system is constructed that analyzes the health conditions and behavioral characteristics of pets at shelters and improves the accuracy of matching with missing pets. For example, the pets' health records and behavioral patterns are registered in a database. The generation AI also analyzes the health conditions and behavioral characteristics of pets at shelters and evaluates their similarity to missing pets. For example, pets with specific illnesses or behavioral characteristics are prioritized for matching. The generation AI also develops an algorithm that improves the accuracy of matching with missing pets based on the health conditions and behavioral characteristics of pets at shelters. For example, health conditions and behavioral characteristics are weighted for matching. This allows the health conditions and behavioral characteristics of pets at shelters to be analyzed and improves the accuracy of matching with missing pets.

[0036] By working with shelters, information can be shared with local veterinary clinics and pet shops when a pet goes missing, increasing the chances of finding the pet. By working with shelters, a system can be built to share information with local veterinary clinics and pet shops when a pet goes missing. For example, information about missing pets can be notified all at once. By working with veterinary clinics and pet shops, information about missing pets can be shared in real time. For example, the characteristics of the pet and the location where it went missing can be shared. An information-sharing platform can also be developed between shelters, veterinary clinics, and pet shops to increase the chances of finding missing pets. For example, when each facility enters information, other facilities can be immediately notified. This will allow cooperation with shelters to share information with local veterinary clinics and pet shops when a pet goes missing, increasing the chances of finding the pet.

[0037] A generative AI can analyze photos and videos of pets at shelters to assess their similarity to missing pets. A system can be built in which a generative AI analyzes photos and videos of pets at shelters and assesses their similarity to missing pets. For example, similarity can be assessed using pet facial recognition technology. The generative AI can also analyze photos and videos of pets at shelters and compare them with features of missing pets. For example, similarity can be assessed based on coat color, physique, and specific patterns. The generative AI can also analyze photos and videos of pets at shelters and develop a system that scores their similarity to missing pets. For example, pets with high similarity scores can be matched preferentially. This allows a generative AI to analyze photos and videos of pets at shelters and assess their similarity to missing pets.

[0038] The generation AI can perform frequency analysis of pet cries and identify pets with specific cry patterns with high accuracy. For example, the generation AI can perform frequency analysis of pet cries and build a system that can identify pets with specific cry patterns with high accuracy. For example, it can analyze the frequency spectrum of the cries and detect specific patterns. The generation AI can also develop an algorithm that identifies pet characteristics based on the frequency analysis of the cries. For example, it can identify pets with characteristic cries in a specific frequency band. The generation AI can also perform frequency analysis of the cries and build a system that matches pets with specific cry patterns with a database. For example, it can identify the type and individual pet based on the characteristics of the cries. This allows the generation AI to perform frequency analysis of pet cries and identify pets with specific cry patterns with high accuracy.

[0039] Based on the results of the analysis of pet cries, a pet's emotional state can be estimated, which can be used to predict behavior. A system will be built that estimates a pet's emotional state based on the results of the analysis of pet cries. For example, the tone and rhythm of the cries can be analyzed to determine whether the pet is excited or relaxed. The generation AI will also estimate a pet's emotional state based on the results of the analysis of pet cries, and use this information to predict behavior. For example, if a pet is feeling anxious, it will predict a hiding place. The generation AI will also develop a system that monitors a pet's emotional state in real time based on the results of the analysis of pet cries, which can be used to predict behavior. For example, when a pet cries, its emotional state can be analyzed instantly. This makes it possible to estimate a pet's emotional state based on the results of the analysis of pet cries, which can be used to predict behavior.

[0040] The generation AI can analyze pet cries and build a system that monitors the cries of missing pets in real time. For example, the generation AI can analyze pet cries and build a system that monitors the cries of missing pets in real time. For example, it can identify pets with characteristic cries in a specific frequency band. The generation AI can also develop a real-time monitoring system for cries and detect the cries of missing pets. For example, it can constantly monitor pet cries and issue an alert when a specific cries are detected. The generation AI can also analyze pet cries in real time and build a system that identifies the cries of missing pets. For example, it can identify the type and individual pet based on the characteristics of the cries. This makes it possible for the generation AI to analyze pet cries and build a system that monitors the cries of missing pets in real time.

[0041] Based on the results of the analysis of pet cries, it is possible to predict pet behavior patterns and narrow down the search area. A system will be built that predicts pet behavior patterns based on the results of the analysis of pet cries. For example, it will predict what behavior a pet with a specific cries pattern will exhibit. The generation AI will also predict pet behavior patterns based on the results of the analysis of pet cries and narrow down the search area. For example, it will identify the preferred locations of pets that make specific cries. The generation AI will also develop a system that predicts pet behavior patterns in real time based on the results of the analysis of pet cries and narrow down the search area. For example, when a pet cries, it will instantly analyze its behavior pattern. This will make it possible to predict pet behavior patterns and narrow down the search area based on the results of the analysis of pet cries.

[0042] An online platform for sharing know-how and knowledge can be built, which promotes the exchange of information between users. An online platform for sharing know-how and knowledge can be built, and a system developed to promote the exchange of information between users. For example, a forum can be set up for sharing search methods and success stories. A function can also be provided on the online platform that allows users to post questions and advice about searches. For example, users can receive feedback from other users. An online platform for sharing know-how and knowledge can also be built, which allows users to share information that is useful for search activities in real time. For example, users can share progress during the search and information about discoveries. In this way, an online platform for sharing know-how and knowledge can be built, which promotes the exchange of information between users.

[0043] Workshops and seminars can be held regularly to share know-how and knowledge, providing opportunities to receive advice from experts. A system can be constructed to hold workshops and seminars regularly to share know-how and knowledge, providing opportunities to receive advice from experts. For example, a seminar can be held to introduce search methods and success stories. Discussions at the workshops and seminars can be recorded and saved as audio data. The audio can then be analyzed to extract important ideas and opinions, which are then saved in a database. Materials and presentation data used in the workshops and seminars can be collected and saved in a database as background information for the ideas. This makes the origin and rationale of the ideas clear. This makes it possible to hold workshops and seminars regularly to share know-how and knowledge, providing opportunities to receive advice from experts.

[0044] It is possible to build a system in which generative AI automatically organizes know-how and knowledge to provide users with the most appropriate information. We will build a system in which generative AI automatically organizes know-how and knowledge to provide users with the most appropriate information. For example, the generative AI could suggest the most appropriate search method for users based on past success stories and failure stories. The generative AI could also automatically classify know-how and knowledge to make it easier for users to access. For example, it could organize information by category so that users can quickly find the information they need. We will also develop a system in which the generative AI updates know-how and knowledge in real time to provide users with the latest information. For example, if a new success story is added, the user would be immediately notified of this information. This makes it possible to build a system in which generative AI automatically organizes know-how and knowledge to provide users with the most appropriate information.

[0045] The generative AI will analyze the pet feature database to improve the accuracy of matching with missing pets. We will build a system in which the generative AI analyzes the pet feature database to improve the accuracy of matching with missing pets. For example, matching will be performed based on the pet's coat color, physique, and specific patterns. We will also develop an algorithm to evaluate the similarity to missing pets based on the pet feature database. For example, the degree of similarity of features will be scored, and pets with high scores will be matched preferentially. We will also develop a system in which the generative AI analyzes the pet feature database in real time to improve the accuracy of matching with missing pets. For example, if new feature data is added, that information will be analyzed immediately and reflected in the matching. This will enable the generative AI to analyze the pet feature database and improve the accuracy of matching with missing pets.

[0046] By adding a pet's health condition and behavioral characteristics to the pet feature database, it is possible to provide more detailed information. A system will be built that adds a pet's health condition and behavioral characteristics to the pet feature database and provides more detailed information. For example, a pet's health records and behavioral patterns will be registered in the database. A generative AI will also analyze the pet's health condition and behavioral characteristics and add them to the feature database. For example, detailed information on pets with specific illnesses or behavioral characteristics will be recorded. A system will also be developed that performs matching based on the pet feature database, taking into account the pet's health condition and behavioral characteristics. For example, matching will be performed by weighting the health condition and behavioral characteristics. This will allow a pet's health condition and behavioral characteristics to be added to the pet feature database and provide more detailed information.

[0047] The pet characteristic database can be linked with other pet-related services to provide comprehensive services. The pet characteristic database can be linked with other pet-related services such as pet insurance and pet food to build a system that provides comprehensive services. For example, insurance plans based on the pet's health condition can be proposed. In addition, the generative AI analyzes the pet characteristic database and links with other pet-related services. For example, pet food based on the pet's dietary restrictions can be proposed. In addition, based on the pet characteristic database, a system can be developed that links with other pet-related services to provide comprehensive services. For example, services based on the pet's health condition and behavioral characteristics can be proposed. In this way, the pet characteristic database can be linked with other pet-related services to provide comprehensive services.

[0048] The generative AI will analyze the pet feature database and identify new features that will be useful in finding missing pets. We will build a system in which the generative AI analyzes the pet feature database and identifies new features that will be useful in finding missing pets. For example, new features will be identified based on specific behavioral patterns or health conditions. We will also develop an algorithm based on the pet feature database to identify new features that will be useful in finding missing pets. For example, we will score the degree of match between features and prioritize matching with features that have high scores. We will also develop a system in which the generative AI analyzes the pet feature database in real time and identifies new features that will be useful in finding missing pets. For example, if new feature data is added, the information will be analyzed immediately and reflected in the matching. This will enable the generative AI to analyze the pet feature database and identify new features that will be useful in finding missing pets.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The pet discovery system can further include a sighting information collection unit that collects sighting information from neighboring residents. For example, when a resident enters sighting information into an app, the sighting information collection unit immediately analyzes it and compares it with the pet's characteristics. The sighting information collection unit can also develop a dedicated app for collecting sighting information, making it easy for residents to provide information. For example, by simply entering a photo and characteristics of the pet into the app, the sighting information collection unit automatically performs matching. The sighting information collection unit also analyzes the sighting information and identifies locations where the missing pet is likely to be. For example, if multiple residents report sightings at the same location, it will focus its search on that location. This allows sighting information from neighboring residents to be collected and matched in real time.

[0051] The pet discovery system can further include a drone search unit that uses drones to conduct wide-area searches. For example, it analyzes video footage captured by the drone in real time and compares it with the pet's characteristics. The drone search unit also develops an algorithm that automatically detects the pet's characteristics using a camera mounted on the drone. For example, it identifies the pet from video data based on the pet's coat color and physique. The drone search unit also uses multiple drones to build a system that efficiently searches wide areas. For example, the drones work together to share search areas and analyze the collected video data. This makes it possible to conduct wide-area searches using drones and identify the pet by analyzing the video data.

[0052] The pet discovery system may further include a database integration unit that integrates shelter databases and enables the generation AI to automatically collect and update the latest housed pet information. For example, it may link with the databases of each shelter and update information in real time. The database integration unit may also periodically scan the shelter databases and automatically collect information on newly housed pets. For example, it may update the database at a set time each day. The database integration unit may also integrate the shelter databases and develop a system for centrally managing housed pet information. For example, it may consolidate data from multiple shelters onto a single platform. This allows the shelter databases to be integrated and the generation AI to automatically collect and update the latest housed pet information.

[0053] The pet finding system may further include a health analysis unit that analyzes the health conditions and behavioral characteristics of pets at the shelter to improve the accuracy of matching with missing pets. For example, the health records and behavioral patterns of pets may be registered in a database. The health analysis unit may also analyze the health conditions and behavioral characteristics of pets at the shelter to evaluate their similarity to the missing pet. For example, pets with specific illnesses or behavioral characteristics may be preferentially matched. The health analysis unit may also develop an algorithm that improves the accuracy of matching with missing pets based on the health conditions and behavioral characteristics of pets at the shelter. For example, the health analysis unit may weight the health conditions and behavioral characteristics when performing matching. This allows the health conditions and behavioral characteristics of pets at the shelter to be analyzed and the accuracy of matching with missing pets to be improved.

[0054] The pet discovery system can further include an information sharing unit that works in cooperation with shelters to share information with local veterinary clinics and pet shops when a pet goes missing. For example, it can simultaneously notify all parties of information about missing pets. The information sharing unit also works with veterinary clinics and pet shops to share information about missing pets in real time. For example, it can share the pet's characteristics and the location where it went missing. The information sharing unit can also develop an information sharing platform between shelters, veterinary clinics, and pet shops to increase the likelihood of finding a missing pet. For example, when each facility enters information, it immediately notifies the other facilities. This allows the system to work in cooperation with shelters to share information with local veterinary clinics and pet shops when a pet goes missing, increasing the likelihood of finding the pet.

[0055] The pet discovery system can further build an online platform for sharing know-how and knowledge, and promote information exchange among users. For example, a forum can be set up for sharing search methods and success stories. The online platform can also provide a function that allows users to post questions and advice about searches. For example, users can receive feedback from other users. The online platform can also allow users to share information that is useful for search activities in real time. For example, users can share progress during the search and information about the discovery. In this way, an online platform for sharing know-how and knowledge can be built, and information exchange among users can be promoted.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: When a pet goes missing, the Generative AI analyzes data from the shelter to help find it. For example, the Generative AI collects information on pets housed at the shelter and matches it with the missing pet. The Generative AI also analyzes the sounds pets make and identifies pets with specific sound patterns. Step 2: The Shelter Liaison Department works with shelters to help find pets. For example, it registers photos and descriptions of pets housed at shelters in a database and matches them with missing pets. It also collects information from shelters in real time to help find pets. Step 3: The sound analyzer analyzes the pet's sounds and identifies pets with specific sound patterns. For example, a missing pet's sound can be recorded and input into the generator AI. The generator AI analyzes the recorded sound and compares it with sounds recorded at shelters and other locations. Step 4: The Know-how Sharing Department shares know-how and knowledge about finding and protecting pets. For example, they share information such as what to do when a pet goes missing and how to work with pet shelters. They also create a database of success stories and failures based on past cases and share it with other users.

[0058] (Example 2) A pet finding system according to an embodiment of the present invention utilizes generative AI to quickly find and protect missing pets. This system uses generative AI to expand the matching area and utilizes data on shelters and animal sounds. Furthermore, by sharing know-how and knowledge, efficient discovery and protection are achieved. This allows the pet finding system to quickly find and protect missing pets.

[0059] A pet finding system according to an embodiment includes a generation AI, a shelter collaboration unit, a cry analysis unit, and a know-how sharing unit. The generation AI analyzes data from a shelter when a pet goes missing and assists in the discovery of the pet. For example, the generation AI collects information on pets housed at the shelter and compares it with missing pets. The generation AI also analyzes pet cries and identifies pets with specific cry patterns. The shelter collaboration unit collaborates with the shelter to assist in the discovery of pets. For example, it registers photos and characteristics of pets housed at the shelter in a database and compares them with missing pets. It also collects information from the shelter in real time to help find the pet. The cry analysis unit analyzes pet cries and identifies pets with specific cry patterns. For example, it records the cries of a missing pet and inputs the data into the generation AI. The generation AI analyzes the recorded cries and compares them with cries recorded at the shelter and other locations. The know-how sharing unit shares know-how and knowledge regarding the discovery and protection of pets. For example, information such as what to do when a pet goes missing and how to work with shelters can be shared. Also, a database of past successes and failures can be compiled and shared with other users. This allows the pet discovery system to quickly find and protect missing pets.

[0060] The generation AI can analyze a pet's behavioral patterns and identify locations where the pet is likely to be found based on its behavioral history before going missing. For example, the generation AI can analyze a pet's GPS data and past behavioral history to identify locations the pet frequently visited before going missing. For example, it can prioritize searches of parks and walking routes where the pet often played. The generation AI can also analyze a pet's behavioral patterns to predict the likelihood that the pet will be in a specific location at a specific time of day. For example, if a pet has a habit of going to a specific location every evening, the generation AI can focus its search on that location during that time. The generation AI can also identify locations where the pet exhibited abnormal behavior before going missing based on the pet's behavioral history. For example, if a pet suddenly starts going to a place it doesn't usually go, the generation AI can focus its search on that location. This allows the generation AI to analyze a pet's behavioral patterns and identify locations where the pet is likely to be found.

[0061] The generation AI can predict the range of movement and hiding places based on the pet's health and age. The generation AI predicts the range of movement based on the pet's health and age. For example, elderly pets and pets in poor health have smaller ranges of movement, so the AI ​​will focus its search within those ranges. The generation AI also predicts hiding places taking the pet's health and age into account. For example, young pets are more likely to be active and move over a wide area, while older pets tend to hide in quiet places. The generation AI also predicts behavioral patterns under specific environmental conditions based on the pet's health and age. For example, pets in poor health tend to prefer cool or shady places, so the AI ​​will focus its search within those areas. This makes it possible to predict the range of movement and hiding places taking the pet's health and age into account.

[0062] The generation AI can use the emotion estimation function to analyze the owner's emotions when a pet goes missing and suggest the optimal search method based on those emotions. For example, the generation AI can use the emotion estimation function to analyze the owner's emotions in real time when a pet goes missing and suggest the optimal search method based on those emotions. For example, if the owner is feeling anxious or impatient, it will suggest a quick search method. The generation AI also provides search support according to the owner's emotions based on the owner's emotional data. For example, if the owner is calm, it will suggest a planned search method, and if the owner is anxious, it will encourage immediate action. The generation AI also uses the emotion estimation function to analyze the owner's emotional state and provide search support messages based on their emotions. For example, if the owner is feeling anxious, it will present encouraging messages or success stories. This makes it possible to suggest the optimal search method based on the owner's emotions.

[0063] When a pet goes missing, the Generator AI can collect sighting information from neighbors and match it in real time. For example, the Generator AI can build a system that collects sighting information from neighbors and matches it with missing pets in real time. For example, when a resident enters sighting information into an app, the Generator AI immediately analyzes it and compares it with the pet's characteristics. The Generator AI can also develop a dedicated app for collecting sighting information, making it easy for residents to provide information. For example, by simply entering a photo and characteristics of a pet into the app, the Generator AI can automatically match it. The Generator AI can also analyze sighting information and identify locations where the missing pet is likely to be. For example, if multiple residents report sightings in the same location, it will focus its search on that location. This makes it possible to collect sighting information from neighbors and match it in real time.

[0064] When a pet goes missing, generative AI can use drones to conduct a wide-area search and analyze the video data to identify the pet. Generative AI, for example, can build a system that uses drones to conduct a wide-area search and analyze the collected video data to identify the missing pet. For example, it can analyze video footage captured by drones in real time and compare it with the pet's characteristics. Generative AI can also develop an algorithm that automatically detects pet characteristics using a camera mounted on the drone. For example, it can identify a pet from video data based on its coat color and physique. Generative AI can also build a system that uses multiple drones to efficiently search a wide area. For example, drones can work together to share the search area, and the generative AI can analyze the collected video data. This makes it possible to conduct a wide-area search using drones and analyze the video data to identify the pet.

[0065] The generation AI can use its emotion estimation function to monitor the owner's emotions in real time when a pet goes missing and provide appropriate support according to the progress of the search operation. For example, the generation AI can use its emotion estimation function to monitor the owner's emotions in real time when a pet goes missing and build a system that provides appropriate support according to the progress of the search operation. For example, if the owner is feeling anxious, it can send an encouraging message. The generation AI can also provide support messages based on the owner's emotional data according to the progress of the search operation. For example, if the search is progressing, it can send a positive message, and if there is no progress, it can suggest the next step. The generation AI can also use its emotion estimation function to analyze the owner's emotional state and provide real-time support according to the progress of the search operation. For example, if the owner is feeling anxious, it can provide advice to stay calm. This makes it possible to monitor the owner's emotions in real time and provide appropriate support according to the progress of the search operation.

[0066] By integrating shelter databases, the generation AI can automatically collect and update the latest information on housed pets. A system will be built in which the generation AI will integrate shelter databases and automatically collect and update the latest information on housed pets. For example, it will link with the databases of each shelter and update information in real time. The generation AI will also periodically scan the shelter databases and automatically collect information on newly housed pets. For example, it will update the database at a set time each day. The generation AI will also integrate the shelter databases and develop a system to centrally manage information on housed pets. For example, it will consolidate data from multiple shelters onto a single platform. This will allow the shelter databases to be integrated, allowing the generation AI to automatically collect and update the latest information on housed pets.

[0067] By analyzing the health conditions and behavioral characteristics of pets at shelters, it is possible to improve the accuracy of matching with missing pets. A system is constructed that analyzes the health conditions and behavioral characteristics of pets at shelters and improves the accuracy of matching with missing pets. For example, the pets' health records and behavioral patterns are registered in a database. The generation AI also analyzes the health conditions and behavioral characteristics of pets at shelters and evaluates their similarity to missing pets. For example, pets with specific illnesses or behavioral characteristics are prioritized for matching. The generation AI also develops an algorithm that improves the accuracy of matching with missing pets based on the health conditions and behavioral characteristics of pets at shelters. For example, health conditions and behavioral characteristics are weighted for matching. This allows the health conditions and behavioral characteristics of pets at shelters to be analyzed and improves the accuracy of matching with missing pets.

[0068] The emotion estimation function can be used to analyze the emotions of shelter staff and suggest the best way to care for pets. A system can be built using the emotion estimation function to analyze the emotions of shelter staff and suggest the best way to care for pets. For example, if a staff member is feeling stressed, the system can suggest ways to refresh them. The system can also suggest the best way to care for pets based on the staff's emotional data. For example, if a staff member is tired, the system can send a message encouraging them to take a break. The emotion estimation function can also be used to analyze the emotional state of shelter staff and provide real-time support for pet care. For example, if a staff member is feeling anxious, the system can send an encouraging message. This makes it possible to analyze the emotions of shelter staff and suggest the best way to care for pets.

[0069] By working with shelters, information can be shared with local veterinary clinics and pet shops when a pet goes missing, increasing the chances of finding the pet. By working with shelters, a system can be built to share information with local veterinary clinics and pet shops when a pet goes missing. For example, information about missing pets can be notified all at once. By working with veterinary clinics and pet shops, information about missing pets can be shared in real time. For example, the characteristics of the pet and the location where it went missing can be shared. An information-sharing platform can also be developed between shelters, veterinary clinics, and pet shops to increase the chances of finding missing pets. For example, when each facility enters information, other facilities can be immediately notified. This will allow cooperation with shelters to share information with local veterinary clinics and pet shops when a pet goes missing, increasing the chances of finding the pet.

[0070] A generative AI can analyze photos and videos of pets at shelters to assess their similarity to missing pets. A system can be built in which a generative AI analyzes photos and videos of pets at shelters and assesses their similarity to missing pets. For example, similarity can be assessed using pet facial recognition technology. The generative AI can also analyze photos and videos of pets at shelters and compare them with features of missing pets. For example, similarity can be assessed based on coat color, physique, and specific patterns. The generative AI can also analyze photos and videos of pets at shelters and develop a system that scores their similarity to missing pets. For example, pets with high similarity scores can be matched preferentially. This allows a generative AI to analyze photos and videos of pets at shelters and assess their similarity to missing pets.

[0071] The emotion estimation function can be used to analyze the emotions of visitors to a shelter and support optimal matching in finding foster homes for pets. Using the emotion estimation function, a system can be built that analyzes the emotions of visitors to a shelter and supports optimal matching in finding foster homes for pets. For example, if a visitor expresses positive emotions toward a particular pet, that pet can be introduced preferentially. Furthermore, optimal matching in finding foster homes is suggested based on the visitor's emotional data. For example, if a visitor expresses positive reactions toward a pet with a particular personality or characteristics, that pet can be recommended. The emotion estimation function can also be used to analyze the emotional state of visitors to a shelter and provide real-time support in finding foster homes. For example, if a visitor is feeling anxious, a reassuring message can be sent. This allows the emotions of visitors to a shelter to be analyzed and optimal matching in finding foster homes for pets to be supported.

[0072] The generation AI can perform frequency analysis of pet cries and identify pets with specific cry patterns with high accuracy. For example, the generation AI can perform frequency analysis of pet cries and build a system that can identify pets with specific cry patterns with high accuracy. For example, it can analyze the frequency spectrum of the cries and detect specific patterns. The generation AI can also develop an algorithm that identifies pet characteristics based on the frequency analysis of the cries. For example, it can identify pets with characteristic cries in a specific frequency band. The generation AI can also perform frequency analysis of the cries and build a system that matches pets with specific cry patterns with a database. For example, it can identify the type and individual pet based on the characteristics of the cries. This allows the generation AI to perform frequency analysis of pet cries and identify pets with specific cry patterns with high accuracy.

[0073] Based on the results of the analysis of pet cries, a pet's emotional state can be estimated, which can be used to predict behavior. A system will be built that estimates a pet's emotional state based on the results of the analysis of pet cries. For example, the tone and rhythm of the cries can be analyzed to determine whether the pet is excited or relaxed. The generation AI will also estimate a pet's emotional state based on the results of the analysis of pet cries, and use this information to predict behavior. For example, if a pet is feeling anxious, it will predict a hiding place. The generation AI will also develop a system that monitors a pet's emotional state in real time based on the results of the analysis of pet cries, which can be used to predict behavior. For example, when a pet cries, its emotional state can be analyzed instantly. This makes it possible to estimate a pet's emotional state based on the results of the analysis of pet cries, which can be used to predict behavior.

[0074] The emotion estimation function can be used to analyze the owner's emotional response to their pet's cries and suggest the optimal search method. A system can be built that uses the emotion estimation function to analyze the owner's emotional response to their pet's cries and suggest the optimal search method. For example, if the owner has a strong emotional reaction to a particular cries, a search can be conducted based on that cry. The system can also analyze the owner's emotional response to their pet's cries based on the owner's emotional data and suggest a search method. For example, if the owner is feeling anxious, a quick search method can be suggested. The emotion estimation function can also be used to analyze the owner's emotional state in response to their pet's cries and provide support according to the progress of the search activity. For example, if the owner is feeling anxious, advice on how to stay calm is provided. This makes it possible to analyze the owner's emotional response to their pet's cries and suggest the optimal search method.

[0075] The generation AI can analyze pet cries and build a system that monitors the cries of missing pets in real time. For example, the generation AI can analyze pet cries and build a system that monitors the cries of missing pets in real time. For example, it can identify pets with characteristic cries in a specific frequency band. The generation AI can also develop a real-time monitoring system for cries and detect the cries of missing pets. For example, it can constantly monitor pet cries and issue an alert when a specific cries are detected. The generation AI can also analyze pet cries in real time and build a system that identifies the cries of missing pets. For example, it can identify the type and individual pet based on the characteristics of the cries. This makes it possible for the generation AI to analyze pet cries and build a system that monitors the cries of missing pets in real time.

[0076] Based on the results of the analysis of pet cries, it is possible to predict pet behavior patterns and narrow down the search area. A system will be built that predicts pet behavior patterns based on the results of the analysis of pet cries. For example, it will predict what behavior a pet with a specific cries pattern will exhibit. The generation AI will also predict pet behavior patterns based on the results of the analysis of pet cries and narrow down the search area. For example, it will identify the preferred locations of pets that make specific cries. The generation AI will also develop a system that predicts pet behavior patterns in real time based on the results of the analysis of pet cries and narrow down the search area. For example, when a pet cries, it will instantly analyze its behavior pattern. This will make it possible to predict pet behavior patterns and narrow down the search area based on the results of the analysis of pet cries.

[0077] The emotion estimation function can be used to analyze the emotional reactions of people in the vicinity to a pet's cry, and the results can be used to aid in search operations. Using the emotion estimation function, a system can be constructed that analyzes the emotional reactions of people in the vicinity to a pet's cry and aids in search operations. For example, if a person shows a strong emotional reaction to a particular sound, a search can be conducted based on that sound. Furthermore, based on the emotional data of people in the vicinity, the system can analyze their emotional reactions to a pet's cry and suggest a search method. For example, if a person shows a positive reaction to a particular sound, a search can be conducted based on that sound. Furthermore, the emotion estimation function can be used to analyze the emotional state of people in the vicinity to a pet's cry, and provide support according to the progress of the search operations. For example, if people in the vicinity are feeling anxious, a message that provides reassurance can be sent. This allows the system to analyze the emotional reactions of people in the vicinity to a pet's cry and aid in search operations.

[0078] An online platform for sharing know-how and knowledge can be built, which promotes the exchange of information between users. An online platform for sharing know-how and knowledge can be built, and a system developed to promote the exchange of information between users. For example, a forum can be set up for sharing search methods and success stories. A function can also be provided on the online platform that allows users to post questions and advice about searches. For example, users can receive feedback from other users. An online platform for sharing know-how and knowledge can also be built, which allows users to share information that is useful for search activities in real time. For example, users can share progress during the search and information about discoveries. In this way, an online platform for sharing know-how and knowledge can be built, which promotes the exchange of information between users.

[0079] Using the emotion estimation function, it is possible to analyze the emotions of pet owners during search activities and provide appropriate support information. Using the emotion estimation function, we will build a system that analyzes the emotions of pet owners during search activities and provides appropriate support information. For example, if the owner is feeling anxious, an encouraging message will be sent. In addition, appropriate support information during search activities will be provided based on the owner's emotional data. For example, if the owner is feeling anxious, advice on how to stay calm will be provided. In addition, using the emotion estimation function, we will develop a system that analyzes the emotional state of pet owners during search activities and provides support information in real time. For example, if the owner is calm, a planned search method will be suggested. This will make it possible to analyze the emotions of pet owners during search activities and provide appropriate support information.

[0080] Workshops and seminars can be held regularly to share know-how and knowledge, providing opportunities to receive advice from experts. A system can be constructed to hold workshops and seminars regularly to share know-how and knowledge, providing opportunities to receive advice from experts. For example, a seminar can be held to introduce search methods and success stories. Discussions at the workshops and seminars can be recorded and saved as audio data. The audio can then be analyzed to extract important ideas and opinions, which are then saved in a database. Materials and presentation data used in the workshops and seminars can be collected and saved in a database as background information for the ideas. This makes the origin and rationale of the ideas clear. This makes it possible to hold workshops and seminars regularly to share know-how and knowledge, providing opportunities to receive advice from experts.

[0081] It is possible to build a system in which generative AI automatically organizes know-how and knowledge to provide users with the most appropriate information. We will build a system in which generative AI automatically organizes know-how and knowledge to provide users with the most appropriate information. For example, the generative AI could suggest the most appropriate search method for users based on past success stories and failure stories. The generative AI could also automatically classify know-how and knowledge to make it easier for users to access. For example, it could organize information by category so that users can quickly find the information they need. We will also develop a system in which the generative AI updates know-how and knowledge in real time to provide users with the latest information. For example, if a new success story is added, the user would be immediately notified of this information. This makes it possible to build a system in which generative AI automatically organizes know-how and knowledge to provide users with the most appropriate information.

[0082] The emotion estimation function can be used to analyze users' emotional reactions to sharing know-how and knowledge, and improve sharing methods. A system can be constructed that uses the emotion estimation function to analyze users' emotional reactions to sharing know-how and knowledge, and improve sharing methods. For example, sharing methods in which users express positive emotions can be prioritized. The know-how and knowledge sharing method can also be improved based on the user's emotional data. For example, if a user expresses negative emotions, the sharing method can be revised. The emotion estimation function can also be used to analyze users' emotional state regarding sharing know-how and knowledge, and develop a system that improves sharing methods in real time. For example, the sharing method can be dynamically adjusted according to the user's emotional reaction. This makes it possible to analyze users' emotional reactions to sharing know-how and knowledge, and improve sharing methods.

[0083] The generative AI will analyze the pet feature database to improve the accuracy of matching with missing pets. We will build a system in which the generative AI analyzes the pet feature database to improve the accuracy of matching with missing pets. For example, matching will be performed based on the pet's coat color, physique, and specific patterns. We will also develop an algorithm to evaluate the similarity to missing pets based on the pet feature database. For example, the degree of similarity of features will be scored, and pets with high scores will be matched preferentially. We will also develop a system in which the generative AI analyzes the pet feature database in real time to improve the accuracy of matching with missing pets. For example, if new feature data is added, that information will be analyzed immediately and reflected in the matching. This will enable the generative AI to analyze the pet feature database and improve the accuracy of matching with missing pets.

[0084] By adding a pet's health condition and behavioral characteristics to the pet feature database, it is possible to provide more detailed information. A system will be built that adds a pet's health condition and behavioral characteristics to the pet feature database and provides more detailed information. For example, a pet's health records and behavioral patterns will be registered in the database. A generative AI will also analyze the pet's health condition and behavioral characteristics and add them to the feature database. For example, detailed information on pets with specific illnesses or behavioral characteristics will be recorded. A system will also be developed that performs matching based on the pet feature database, taking into account the pet's health condition and behavioral characteristics. For example, matching will be performed by weighting the health condition and behavioral characteristics. This will allow a pet's health condition and behavioral characteristics to be added to the pet feature database and provide more detailed information.

[0085] The emotion estimation function can be used to analyze the owner's emotional response to pet characteristics and improve the accuracy of the database. A system is constructed that uses the emotion estimation function to analyze the owner's emotional response to pet characteristics and improve the accuracy of the database. For example, if the owner shows positive emotions toward a particular characteristic, that characteristic is emphasized. The accuracy of the pet characteristic database is also improved based on the owner's emotional data. For example, characteristics for which the owner showed negative emotions are reviewed. A system is also developed that uses the emotion estimation function to analyze the owner's emotional state toward pet characteristics and improve the accuracy of the database in real time. For example, the database is dynamically adjusted according to the owner's emotional response. This allows the owner's emotional response to pet characteristics to be analyzed and the accuracy of the database to be improved.

[0086] The pet characteristic database can be linked with other pet-related services to provide comprehensive services. The pet characteristic database can be linked with other pet-related services such as pet insurance and pet food to build a system that provides comprehensive services. For example, insurance plans based on the pet's health condition can be proposed. In addition, the generative AI analyzes the pet characteristic database and links with other pet-related services. For example, pet food based on the pet's dietary restrictions can be proposed. In addition, based on the pet characteristic database, a system can be developed that links with other pet-related services to provide comprehensive services. For example, services based on the pet's health condition and behavioral characteristics can be proposed. In this way, the pet characteristic database can be linked with other pet-related services to provide comprehensive services.

[0087] The generative AI will analyze the pet feature database and identify new features that will be useful in finding missing pets. We will build a system in which the generative AI analyzes the pet feature database and identifies new features that will be useful in finding missing pets. For example, new features will be identified based on specific behavioral patterns or health conditions. We will also develop an algorithm based on the pet feature database to identify new features that will be useful in finding missing pets. For example, we will score the degree of match between features and prioritize matching with features that have high scores. We will also develop a system in which the generative AI analyzes the pet feature database in real time and identifies new features that will be useful in finding missing pets. For example, if new feature data is added, the information will be analyzed immediately and reflected in the matching. This will enable the generative AI to analyze the pet feature database and identify new features that will be useful in finding missing pets.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The pet discovery system can further include a sighting information collection unit that collects sighting information from neighboring residents. For example, when a resident enters sighting information into an app, the sighting information collection unit immediately analyzes it and compares it with the pet's characteristics. The sighting information collection unit can also develop a dedicated app for collecting sighting information, making it easy for residents to provide information. For example, by simply entering a photo and characteristics of the pet into the app, the sighting information collection unit automatically performs matching. The sighting information collection unit also analyzes the sighting information and identifies locations where the missing pet is likely to be. For example, if multiple residents report sightings at the same location, it will focus its search on that location. This allows sighting information from neighboring residents to be collected and matched in real time.

[0090] The pet discovery system can further include a drone search unit that uses drones to conduct wide-area searches. For example, it analyzes video footage captured by the drone in real time and compares it with the pet's characteristics. The drone search unit also develops an algorithm that automatically detects the pet's characteristics using a camera mounted on the drone. For example, it identifies the pet from video data based on the pet's coat color and physique. The drone search unit also uses multiple drones to build a system that efficiently searches wide areas. For example, the drones work together to share search areas and analyze the collected video data. This makes it possible to conduct wide-area searches using drones and identify the pet by analyzing the video data.

[0091] The pet discovery system may further include a database integration unit that integrates shelter databases and enables the generation AI to automatically collect and update the latest housed pet information. For example, it may link with the databases of each shelter and update information in real time. The database integration unit may also periodically scan the shelter databases and automatically collect information on newly housed pets. For example, it may update the database at a set time each day. The database integration unit may also integrate the shelter databases and develop a system for centrally managing housed pet information. For example, it may consolidate data from multiple shelters onto a single platform. This allows the shelter databases to be integrated and the generation AI to automatically collect and update the latest housed pet information.

[0092] The pet discovery system can further use an emotion estimation function to analyze the emotions of the pet owner when the pet goes missing and suggest the optimal search method based on those emotions. For example, the emotion estimation function can be used to analyze the emotions of the pet owner in real time when the pet goes missing and suggest the optimal search method based on those emotions. For example, if the owner is feeling anxious or impatient, a quick search method can be suggested. The emotion estimation function also provides search support according to the owner's emotions based on the owner's emotional data. For example, if the owner is calm, a planned search method can be suggested, and if the owner is anxious, immediate action can be encouraged. The emotion estimation function also analyzes the owner's emotional state and provides search support messages based on emotions. For example, if the owner is feeling anxious, an encouraging message or success story can be presented. This makes it possible to suggest the optimal search method based on the owner's emotions.

[0093] The pet finding system may further include a health analysis unit that analyzes the health conditions and behavioral characteristics of pets at the shelter to improve the accuracy of matching with missing pets. For example, the health records and behavioral patterns of pets may be registered in a database. The health analysis unit may also analyze the health conditions and behavioral characteristics of pets at the shelter to evaluate their similarity to the missing pet. For example, pets with specific illnesses or behavioral characteristics may be preferentially matched. The health analysis unit may also develop an algorithm that improves the accuracy of matching with missing pets based on the health conditions and behavioral characteristics of pets at the shelter. For example, the health analysis unit may weight the health conditions and behavioral characteristics when performing matching. This allows the health conditions and behavioral characteristics of pets at the shelter to be analyzed and the accuracy of matching with missing pets to be improved.

[0094] The pet discovery system can further use the emotion estimation function to analyze the emotions of shelter staff and suggest optimal ways to care for pets. For example, if a staff member is feeling stressed, the system can suggest ways to refresh them. The system can also suggest optimal ways to care for pets based on the staff's emotional data. For example, if a staff member is tired, the system can send a message encouraging them to take a break. The emotion estimation function can also analyze the emotional state of shelter staff and provide real-time support in caring for pets. For example, if a staff member is feeling anxious, the system can send an encouraging message. This allows the system to analyze the emotions of shelter staff and suggest optimal ways to care for pets.

[0095] The pet discovery system can further include an information sharing unit that works in cooperation with shelters to share information with local veterinary clinics and pet shops when a pet goes missing. For example, it can simultaneously notify all parties of information about missing pets. The information sharing unit also works with veterinary clinics and pet shops to share information about missing pets in real time. For example, it can share the pet's characteristics and the location where it went missing. The information sharing unit can also develop an information sharing platform between shelters, veterinary clinics, and pet shops to increase the likelihood of finding a missing pet. For example, when each facility enters information, it immediately notifies the other facilities. This allows the system to work in cooperation with shelters to share information with local veterinary clinics and pet shops when a pet goes missing, increasing the likelihood of finding the pet.

[0096] The pet discovery system can further use the emotion estimation function to analyze the emotions of visitors to the shelter and support optimal matching in finding pet foster homes. For example, if a visitor expresses positive emotions toward a particular pet, that pet will be introduced preferentially. The system also suggests optimal matches in finding foster homes based on the visitor's emotion data. For example, if a visitor expresses positive emotions toward a pet with a particular personality or characteristics, that pet will be recommended. The emotion estimation function also analyzes the emotional state of visitors to the shelter and provides real-time support in finding foster homes. For example, if a visitor is feeling anxious, a reassuring message will be sent. This allows the system to analyze the emotions of visitors to the shelter and support optimal matching in finding pet foster homes.

[0097] The pet discovery system can further build an online platform for sharing know-how and knowledge, and promote information exchange among users. For example, a forum can be set up for sharing search methods and success stories. The online platform can also provide a function that allows users to post questions and advice about searches. For example, users can receive feedback from other users. The online platform can also allow users to share information that is useful for search activities in real time. For example, users can share progress during the search and information about the discovery. In this way, an online platform for sharing know-how and knowledge can be built, and information exchange among users can be promoted.

[0098] The pet discovery system will also use an emotion estimation function to analyze the owner's emotions during the search operation and provide appropriate support information. For example, if the owner is feeling anxious, an encouraging message will be sent. The system will also provide appropriate support information during the search operation based on the owner's emotional data. For example, if the owner is feeling anxious, advice on how to stay calm will be provided. The emotion estimation function will also analyze the owner's emotional state during the search operation and develop a system that provides support information in real time. For example, if the owner is calm, a planned search method will be suggested. This will allow the system to analyze the owner's emotions during the search operation and provide appropriate support information.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: When a pet goes missing, the Generative AI analyzes data from the shelter to help find it. For example, the Generative AI collects information on pets housed at the shelter and matches it with the missing pet. The Generative AI also analyzes the sounds pets make and identifies pets with specific sound patterns. Step 2: The Shelter Liaison Department works with shelters to help find pets. For example, it registers photos and descriptions of pets housed at shelters in a database and matches them with missing pets. It also collects information from shelters in real time to help find pets. Step 3: The sound analyzer analyzes the pet's sounds and identifies pets with specific sound patterns. For example, a missing pet's sound can be recorded and input into the generator AI. The generator AI analyzes the recorded sound and compares it with sounds recorded at shelters and other locations. Step 4: The Know-how Sharing Department shares know-how and knowledge about finding and protecting pets. For example, they share information such as what to do when a pet goes missing and how to work with pet shelters. They also create a database of success stories and failures based on past cases and share it with other users.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0112] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0126] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0145] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Generative AI that analyzes data from shelters when pets go missing and helps find them. a shelter cooperation department that cooperates with the shelter to assist in finding the pet; a cry analysis unit that analyzes pet cries and identifies pets with specific cry patterns; A know-how sharing department that shares know-how and knowledge regarding the discovery and protection of pets. A system characterized by:

2. The generated AI is When a pet goes missing, drones are used to conduct a wide-area search and identify the pet by analyzing the video data.

2. The system of claim 1.

3. By integrating the database of the shelter, the AI ​​automatically collects and updates the latest information on housed pets.

2. The system of claim 1.

4. The generated AI is The pet's cry is frequency-analyzed to identify with high accuracy the pet having a specific cry pattern.

2. The system of claim 1.

5. The generated AI is Analyze past search cases and propose optimal search methods based on success and failure.

2. The system of claim 1.

6. The generated AI is Using emotion estimation function, the system analyzes the emotions of pet owners when their pet goes missing and suggests optimal search methods based on those emotions.

2. The system of claim 1.

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