system

Abstract

We provide the system. [Solution] A processing device for acquiring image data using an input device and analyzing the characteristics of an animal from that image data, A computing device that acquires data using an input device and analyzes the characteristics of animals from that data, A search means that searches for matching information by comparing it with existing information in an information storage device based on the characteristics of the analyzed animal, A notification means that notifies relevant individuals based on the search results, A means including a method for acquiring the characteristics of an animal using an image acquisition device and transmitting them to a computing device in real time, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] ​​​​​​​​This invention provides a matching means that analyzes the characteristics of animals from image data acquired using an input device and compares those characteristics with existing information in a database, thereby enabling the effective search for lost animals. Furthermore, it has developed a system that supports the smooth transfer of animals by providing a method for determining suitability by comparing the analyzed animal information with information on potential adoptive parents and notifying highly suitable candidates. In addition, it also has a means for efficiently providing appropriate care information by analyzing input questions using a natural language processing device and presenting relevant information to the user. Thus, this invention proposes an efficient and integrated system that aims to improve animal welfare.

[0006] "Image data" refers to visual information acquired from an input device to analyze the characteristics of an animal.

[0007] "Animal characteristics" refer to traits that distinguish an animal from others, such as species, color, pattern, and body shape.

[0008] A "processing device" is a computer device that analyzes input image data and extracts the characteristics of animals.

[0009] "Existing information in the database" refers to an information system that stores information about animals collected in the past.

[0010] A "matching method" is a method for comparing the characteristics of an analyzed animal with information in a database to find matches.

[0011] "Notification method" refers to a method of conveying relevant information to the necessary individuals based on the matching results.

[0012] "Animal information" refers to data about the animal's species, temperament, and health status.

[0013] "Foster parent candidate information" refers to data that includes the conditions and preferences of people who wish to adopt an animal.

[0014] A "natural language processing device" is a device that analyzes input text information, understands its content, and generates responses.

[0015] A "knowledge base" is a database in which various information is accumulated and answers to specific questions can be extracted.

[0016] An "output device" is a device for presenting analysis results and information to the user.

Brief Description of the Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] Shows an emotion map to which multiple emotions are mapped. [Figure 10] Shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12]It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), and the like.

[0021] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0030] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0037] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0038] In the animal search support system according to the present invention, image data acquired from an input device is analyzed in a processing device, and animal characteristics are extracted from the analysis results. The extracted characteristics are compared with existing information in a database on a server to determine possible matches. This process is mainly carried out using image recognition technology and machine learning algorithms. Based on the results identified by the matching means, the server sends a notification to the relevant user, and when the animal is confirmed, the user can provide feedback via a mobile device.

[0039] Furthermore, in the animal adoption matching process, the server analyzes suitability based on animal information entered via the terminal and information on already registered potential adopters. Based on the established criteria, the server selects the most suitable candidate and notifies the corresponding user. This promotes the smooth transfer of animals.

[0040] During operation, users input additional information, such as questions, through their devices. The server then uses a natural language processing unit to analyze the request and retrieve relevant breeding and health information from its knowledge base. This allows users to efficiently obtain the necessary information, supporting the maintenance of healthy lives for their animals.

[0041] As a concrete example, if a user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the system, the server analyzes the image to identify the species and characteristics, and quickly matches it with the database. Based on this result, if the animal is registered, the owner is notified, and the animal can be returned home quickly. In addition, when recruiting foster homes for newly rescued animals, the server can provide necessary information to candidates who match the animal's characteristics and confirm their willingness to adopt. This makes it possible to smoothly and quickly transition animals to new homes.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users take photos of lost animals with their mobile devices and upload the image data to the system.

[0045] Step 2:

[0046] The server receives the uploaded image data and automatically starts the image analysis process. Here, computer vision technology is used to extract animal features in the image (e.g., species, color, body shape, pattern, etc.).

[0047] Step 3:

[0048] The server compares the analyzed feature data with an existing database. The matching algorithm searches the database for animal information with similar features and identifies the most likely matches.

[0049] Step 4:

[0050] Based on the matching results, the server sends a notification message to users who appear to be the animal's owner. Users receive this notification on their mobile devices and can check the information needed to reunite with their animal.

[0051] Step 5:

[0052] Users provide feedback on notifications, and if the found animal is correct, the information is fed back to the server. This feedback information is used to improve the platform's performance in the future.

[0053] Step 6:

[0054] When a foster parent matching is needed, users register animal information using their devices. This includes detailed information such as species, temperament, and health status.

[0055] Step 7:

[0056] The server compares the registered animal information with existing foster parent candidate information and selects the most suitable foster parent candidate from the database for each animal.

[0057] Step 8:

[0058] The server sends a notification containing details about the animal to the selected foster parent candidates and confirms the adoption process. This notification is delivered to the candidates via their devices.

[0059] Step 9:

[0060] Based on the notification received by the user, the system confirms their intention to adopt the animal. If necessary, the user can query the server with additional questions using natural language processing.

[0061] Step 10:

[0062] The server analyzes user inquiries, extracts relevant breeding information from its knowledge base, and provides users with accurate information.

[0063] This will allow the "AnimalGuardian" system to function smoothly and enable the development of harmonious relationships between animals and humans.

[0064] (Example 1)

[0065] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0066] There is a need for a system that can respond quickly and effectively when animals get lost or when new adoptive homes are needed. However, current methods have challenges, such as the time-consuming process of analyzing and matching animal characteristics and finding adoptive homes, and the difficulty in providing appropriate information and collecting feedback.

[0067] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0068] In this invention, the server includes a data processing unit for acquiring image data using an information acquisition device and analyzing the characteristics of animals from the image data; a data matching unit for searching for matching data by comparing the analyzed animal characteristics with existing data in a storage device; an information notification unit for notifying relevant users based on the matching results; a data analysis unit for analyzing requests using natural language processing technology and searching for relevant information from a knowledge database; and a feedback collection unit for enabling relevant users to quickly provide feedback when an animal is found based on the analysis results. This enables a series of processes such as the rapid identification and return of animals, provision of appropriate information, and smooth recommendation to potential adoptive parents.

[0069] "Image data" refers to the digital representation of visual information collected by an information acquisition device.

[0070] An "information acquisition device" is a device operated by a user to collect image data.

[0071] A "data processing unit" is a device that has processing functions for analyzing specific features from collected image data.

[0072] A "storage device" is a data storage device that stores existing information, such as a database, and uses it for verification purposes.

[0073] A "data matching unit" is a device that compares analyzed feature data with information in a storage device and searches for matching information.

[0074] An "information notification unit" is a device with communication capabilities that transmits information to relevant users based on the matching results.

[0075] A "data analysis unit" is a device that analyzes input question data and retrieves necessary information from a knowledge database.

[0076] A "feedback collection unit" is a device that enables the rapid collection of feedback from users.

[0077] "Natural language processing technology" is a technology that analyzes user input and processes information in a text-based manner.

[0078] A "knowledge database" is a collection of data that structures and stores information, making it searchable as needed.

[0079] This animal search support system is implemented by combining a data processing unit that primarily analyzes image data, a data matching unit that compares it with existing data in a database, an information notification unit that notifies users of relevant information, a data analysis unit that uses natural language processing technology, and a feedback collection unit that collects feedback.

[0080] When a user finds a lost animal, they use a mobile device, which is an information acquisition device, to take a picture of the animal. The device provides the image data by uploading the captured image to a server. At this time, the user can also input supplementary information such as the animal's condition and the location where it was found.

[0081] The server analyzes the received image data using a data processing unit. Specifically, it uses image analysis software, a common tool for image recognition technology, to extract animal features from the images. In this process, machine learning algorithms are used to identify visual features such as species and patterns.

[0082] The extracted features are compared with data stored in the storage device. The server uses a data matching unit to compare this feature data with existing data in the internal database and search for matching information.

[0083] Next, the server promptly notifies the relevant users based on the database matching results. This provides a means of contact for the animal's safe return, if an owner exists.

[0084] This system also handles animals seeking foster homes. Users input data on rescued animals into their terminals and send it to the server. The server analyzes the registered foster parent candidate data and the animal's information to perform appropriate matching.

[0085] If the user has further questions, they can send requests to the server in text format through their terminal. By applying natural language processing technology, the server can retrieve relevant information from a knowledge database through a data analysis unit and provide specific advice and information to the user.

[0086] A concrete example is when a user enters a prompt such as, "Describe the characteristics of this animal and retrieve information that matches the database," and the server then processes the request and provides the information. This entire process aims to make the discovery and protection of animals faster and more efficient.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user takes a picture of the lost animal using a mobile device and uploads it to the system. The input is the image data of the animal taken by the user and additional information (e.g., location where it was found). The output of this step is the image data sent to the server. The device functions as an information acquisition device and performs initial data collection.

[0090] Step 2:

[0091] The server analyzes the received image data using a data processing unit. The input is the image data obtained in step 1. The server processes the image data using image recognition technology to extract the animal species and characteristics. The output is characteristic data of the identified animals. Machine learning algorithms are used in this process.

[0092] Step 3:

[0093] The server uses feature data to match it with a database in storage. The input is the animal feature data generated in step 2. The server uses a data matching unit to compare the features with existing data and search for matches. The output is the matching results and related information. Here, if matching information for an animal is found, its details are identified.

[0094] Step 4:

[0095] The server notifies the relevant users through the information notification unit based on the matching results. The input is the matching results from step 3. If the owner is found, the server uses their contact information to send a notification to the owner. The output is the notification to the owner and the provision of a means of contact. This notification enables quick reporting.

[0096] Step 5:

[0097] Users input animal rescue data and information on potential new adoptive families into a terminal and send it to the server. Input consists of detailed information about the animals. The terminal supplies this data to the server in digital format. Output is the transmission of information to the server.

[0098] Step 6:

[0099] The server processes the input data in its data analysis unit and compares it with foster parent candidate information to perform appropriate matching. The input consists of detailed animal information and foster parent candidate information obtained in step 5. The output is a list of the most suitable foster parent candidates. The server selects candidates based on their suitability and prepares to notify them.

[0100] Step 7:

[0101] If the user has a question, they send it from the terminal to the server as a text prompt. The input is the user's question data. The output of this step is the prompt sent to the server.

[0102] Step 8:

[0103] The server uses a data analysis unit and applies natural language processing technology to analyze the question and retrieve relevant information from the knowledge database. The input is the question data from step 7. The output is the retrieved relevant information. The server then prepares the information requested by the user.

[0104] Step 9:

[0105] The server provides knowledge to the user based on the analysis results. The input is the relevant information obtained in step 8. The output is the information provided to the user. Through this, the server helps to strengthen the user's knowledge and solve problems quickly.

[0106] (Application Example 1)

[0107] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0108] In animal search and adoption programs, there are challenges in quickly and accurately obtaining animal information and providing appropriate notifications and matching. In particular, when using robots to assist pets, improving the accuracy of on-site animal recognition and information provision is essential.

[0109] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0110] In this invention, the server includes a computing device for acquiring data from an input device and analyzing the characteristics of animals from that data; a search means for searching for matching information by comparing it with existing information in an information storage device based on the analyzed characteristics of animals; a notification means for notifying relevant individuals based on the search results; and a means for acquiring the characteristics of animals with a video acquisition device and transmitting them to the computing device in real time. This makes it possible to quickly recognize the characteristics of animals and efficiently provide relevant information.

[0111] "Data" refers to a collection of information acquired by an input device for analyzing the characteristics of an animal.

[0112] An "input device" is a device used to acquire data from an external source and plays a role in transmitting necessary information to the computing device.

[0113] A "calculation unit" is a device that has the ability to perform calculations to analyze acquired data and extract the characteristics of animals.

[0114] An "information storage device" is a device that stores existing information as a database and provides a foundation for comparing it with new data.

[0115] A "search method" refers to a method or device for comparing existing information and data within an information storage device to find matching information.

[0116] "Notification means" refers to a method or device for notifying relevant individuals of the results obtained by the search means.

[0117] An "image acquisition device" is a device such as a camera or sensor used to capture the characteristics of animals, and it acquires this data in real time.

[0118] The system implementing this invention mainly includes the following components. Data is acquired using an image acquisition device, such as a high-resolution camera, and functions as an input device. This captures the characteristics of the animal. The acquired data is then transmitted to a computing device, specifically a computing device such as a Raspberry Pi. The computing device performs characteristic analysis of the animal using image processing software such as OpenCV and TENSORFLOW®.

[0119] The server receives the analyzed data and compares it with the information storage device, which is an SQLite database in the cloud. During this process, a Python program runs as a search tool to find information that matches specific criteria. If the comparison is successful, a notification is sent to the user via their mobile device or other notification device.

[0120] As a concrete example, suppose a pet monitoring robot patrols a park and finds a dog that appears to be lost. The robot captures an image of the dog and immediately sends it to a server. The server analyzes the image and checks its database for similar information. If a match is found, the dog's owner is promptly notified. In this way, the robot can be effectively utilized, enabling rapid problem resolution.

[0121] An example of a prompt message might be, "Identify the animal species and characteristics from this image and verify that it matches the information in the database." In this way, the system sequentially analyzes the characteristics of the target animal and quickly links related information, enabling efficient search support.

[0122] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0123] Step 1:

[0124] The camera mounted on the device captures images of the surroundings and recognizes animals. The input is high-resolution image data acquired by the camera. This data is preprocessed to remove noise and normalize the images. This prepares the data for analysis.

[0125] Step 2:

[0126] The terminal's computing power extracts animal features using the pre-processed image data from the previous step. Image processing using OpenCV is performed here, analyzing key features such as the animal's outline, color, and patterns. The output is presented as a characteristics dataset, which is used in the next matching step.

[0127] Step 3:

[0128] The server receives the characteristic dataset transmitted from the computing unit and compares it with a database in an information storage device located in the cloud. Here, a generative AI model is used to analyze the correlation between the received dataset and existing records in the database. The output is the result of the matching determination with the animal information stored in the database.

[0129] Step 4:

[0130] The server uses search tools based on the matching results to extract relevant information. For example, if the found animal is registered in the database, it extracts the registered owner information. At this stage, the program uses the prompt statement "Identify the animal species and characteristics from this image and verify that it matches the information in the database."

[0131] Step 5:

[0132] The server, through a notification system, notifies the relevant owner or associated individual if the data matches based on the matching results. The notification is sent via a terminal application, and the owner is provided with specific characteristic and location information. The output is a notification message to the user.

[0133] Step 6:

[0134] Users check notifications received via their devices and provide feedback or take action as needed. This user feedback is sent back to the server as information useful for updating the database and improving data accuracy. This interaction through feedback improves the overall accuracy and efficiency of the system.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] In the animal search support system according to the present invention, image data is acquired by an input device, and the characteristics of the animal are analyzed from the image data by a processing device. This analysis uses computer vision technology to extract characteristics such as the animal's species, pattern, and body shape, and the server searches for matching information by comparing it with existing information in a database. The results identified based on the matching means are notified to the relevant users. This notification is promptly made to the animal's owner or related parties, and the user confirms the information via a terminal.

[0137] Furthermore, this system incorporates an emotion engine that can recognize emotions from the user's voice data. This emotion recognition device analyzes the user's voice tone, speed, and word choice to understand the user's emotional state. Based on the analysis results, the server adjusts the notification content using emotion-responsive means. For example, if the user is in an anxious situation, the system will provide a more detailed and reassuring notification.

[0138] Furthermore, by monitoring the user's emotional state in real time and optimizing the overall system response through emotion optimization methods, the user experience can be improved. This process is also effective as a means of providing emotional support when reuniting with animals, enabling the rapid and appropriate provision of information to support the complex emotions the user may be experiencing.

[0139] For example, when a user finds a lost animal, they take a picture of the animal's characteristics with their device and upload it to the system. The server then performs image analysis and matches it against a database. Simultaneously, if anxiety is detected from the user's voice, the emotion optimization mechanism activates, and the server sends a notification with particularly detailed explanations and clear instructions for the next steps to support the user. In this way, the present invention enables animal search support and optimization of the user experience.

[0140] The following describes the processing flow.

[0141] Step 1:

[0142] A user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the "Animal Search Support System."

[0143] Step 2:

[0144] The server receives the uploaded image data and uses an image analysis engine to extract animal features. These features include species, markings, and body shape.

[0145] Step 3:

[0146] The server compares the extracted feature data with information in existing databases to identify potential matches.

[0147] Step 4:

[0148] Based on the matching results, the server sends a notification to the user who is believed to be the animal's owner. The content of this notification is optimized according to the user's situation by the emotion engine within the server.

[0149] Step 5:

[0150] Users receive notifications on their devices and confirm necessary actions based on the sentiment analysis results. If anxiety is detected, the notification will include additional information to provide reassurance.

[0151] Step 6:

[0152] When a user is reunited with an animal based on a notification, the server acquires the user's voice data and analyzes it with an emotion recognition device to detect the emotions associated with the reunion.

[0153] Step 7:

[0154] Based on the detected emotional state, the server uses emotional support tools to provide the user with appropriate information and determines whether further support is needed.

[0155] Step 8:

[0156] Users register information about their animals, and if adoption matching is needed, they send detailed information to the server via their device.

[0157] Step 9:

[0158] The server analyzes existing foster parent candidate information based on registered animal information and selects the most suitable candidate.

[0159] Step 10:

[0160] The server sends notifications to selected foster parent candidates, confirming details about the animal and the adoption process. This process also utilizes an emotional engine to appropriately adjust the notification content.

[0161] This process allows the entire system, from animal search assistance to foster care matching and emotional support, to operate effectively and humanely.

[0162] (Example 2)

[0163] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0164] When an animal goes missing, there is a need to quickly and accurately locate its whereabouts and provide information to its owner and other relevant parties. However, conventional methods lack sufficient systems for efficiently analyzing and matching animal characteristics, and furthermore, they do not allow for notifications to be adjusted according to the user's emotions. Therefore, the challenge is to improve the efficiency of animal searches while optimizing the user experience.

[0165] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0166] In this invention, the server includes an analysis means for acquiring image data using a recognition device and analyzing the characteristics of animals from the image data; a search means for searching for matching information by comparing it with existing information in the information resource based on the analyzed characteristics of animals; a notification means for notifying relevant users based on the search results; an emotion response means for recognizing emotions from the user's voice data and adjusting the notification content; and an emotion optimization means for optimizing the overall response based on the results of the emotion recognition. This enables rapid and accurate searching of animals and flexible responses that respond to the user's emotions.

[0167] A "recognition device" is a device that acquires image data using an input device and supplements the information required by the system.

[0168] "Analysis means" refers to technologies and devices that extract animal characteristics from acquired image data to identify species, patterns, body shape, etc.

[0169] A "search method" is a process or device that searches for matching information by comparing the analyzed characteristics of an animal with existing information within an information resource.

[0170] "Notification means" refers to communication methods or means for conveying search results and related information to the person or system that should receive them.

[0171] "Emotional response means" refers to technologies and devices that analyze a user's emotional state based on their voice data and adjust the content of notifications based on the results obtained.

[0172] "Emotional optimization methods" refer to methods and tools for monitoring users' emotional states in real time and optimizing the overall system response.

[0173] This system is designed to effectively support animal searches. The server begins collecting information about animals by first acquiring image data using a recognition device. Common input devices such as smartphones and digital cameras can be used for this purpose. The server processes the acquired image data using analysis tools to extract features such as the animal's species, markings, and body shape. Computer vision technologies such as OpenCV and TensorFlow are used for the analysis.

[0174] The analyzed data is compared with information stored in a database on the server using search tools to find matching information. This process is efficiently carried out using SQL or NoSQL databases. The server then sends the results to relevant individuals or entities using notification methods. Notification methods can include email, SMS, or push notifications.

[0175] In addition, the device collects user voice data and sends it to a server. This data is analyzed using speech recognition technologies such as IBM Watson® and Google® Cloud Speech-to-Text. As a result, the emotion response system analyzes the user's emotions and adjusts the notification content accordingly. The emotion optimization system monitors the user's real-time emotional state and appropriately adjusts the system's overall response to provide a better user experience.

[0176] As a concrete example, a user could take a picture of a lost dog they see in a park with their smartphone and upload it to the system. This image would be analyzed on the server to find information about the dog. At the same time, if the user is expressing anxiety by phone, an emotion optimization mechanism would be activated to provide support by notifying them of specific next steps to help them feel at ease.

[0177] An example of a prompt message to be input to the generating AI model is: "I have uploaded a picture of a dog. Please check the breed and characteristics of this dog. For users who are unsure, please generate a message that explains the next steps in detail."

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The server receives image data from the terminal. The user uses the terminal's camera to take pictures of animals and uploads them to the system. This image data is used as input to identify the animal's species and location.

[0181] Step 2:

[0182] The server processes the acquired image data through analysis tools. Here, image processing technologies such as OpenCV and TensorFlow are used to extract animal features. Specifically, this involves analyzing the shape, pattern, and body type of the animals in the images. The data obtained from this analysis is then used as feature information for the next step.

[0183] Step 3:

[0184] The server uses search tools to compare the animal characteristic information obtained through analysis with a database. The database contains existing animal information, and techniques such as SQL queries are used to search for matching data. The output is either information on a matching animal or a result indicating no match.

[0185] Step 4:

[0186] The server sends the search results to the user via a notification system. This process delivers information to the user's device using email or push notifications. The notification includes detailed information about the discovered animal and the next steps to take.

[0187] Step 5:

[0188] The device collects voice data from the user and sends it to the server. This collected voice data is used as input for emotion recognition.

[0189] Step 6:

[0190] The server analyzes the audio data to understand the user's emotional state. Using IBM Watson or Google Cloud Speech-to-Text, it converts the audio to text and then performs emotional analysis. The output provides information on whether the user is feeling anxiety or joy.

[0191] Step 7:

[0192] The server adjusts notification content based on the emotional information it receives. Using emotional optimization techniques, detailed explanations and reassuring phrases are added. This adjustment allows users to more effectively utilize the information from the system.

[0193] (Application Example 2)

[0194] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0195] When an animal goes missing, there is a need to quickly and effectively locate it and provide appropriate information to those involved. Furthermore, a challenge is to improve the user experience by facilitating smooth communication through notifications that take into account the emotional state of the finder.

[0196] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0197] In this invention, the server includes a processing mechanism for acquiring image information via an input device and analyzing the characteristics of an animal from that image information; an identification means for searching for matching information by comparing it with existing information in an information storage medium based on the analyzed characteristics of the animal; a transmission means for notifying the relevant entity based on the matching result; an emotion analysis mechanism for analyzing voice data and detecting emotional state; and a response adjustment means for adjusting the notification content based on the detected emotional state. This enables the search for animals and the rapid and emotionally sensitive provision of information to the relevant entity.

[0198] "Image information" refers to visual data acquired by an input device to identify the characteristics of an animal.

[0199] An "input device" is a device used to acquire image information, specifically such as a camera or a smartphone.

[0200] A "processing mechanism" is a device or program that has the function of analyzing the characteristics of an animal using acquired image information.

[0201] An "information storage medium" is a database or storage system in which existing animal information is recorded.

[0202] "Identification means" refers to a function that identifies relevant information by comparing the analyzed characteristics of an animal with data on an information storage medium.

[0203] "Transmission means" refers to communication means for notifying the relevant entity of the results obtained by the identification means.

[0204] "Audio data" refers to recordings of voices obtained from relevant parties and is used for sentiment analysis.

[0205] An "emotion analysis mechanism" is a device or program that analyzes audio data and has the function of detecting the emotional state of the relevant subject.

[0206] The "response adjustment mechanism" is a function that optimizes the content of notifications based on the detected emotional state and provides information tailored to the recipient.

[0207] This invention aims to process and quickly identify animal images taken by citizens using their smartphones in an animal search support system. The system first acquires image information from the smartphone and sends it to a server. On the server, it uses computer vision libraries such as OpenCV to analyze the animal's characteristics from the image information. The analyzed characteristics are compared with information storage media in a cloud database such as Firebase to identify the corresponding animal.

[0208] Based on the matching results, notifications are sent to the relevant parties, namely the animal's owner or finder. At this time, voice analysis APIs such as IBM Watson are used to detect emotional states from the voice data, and the notification content is adjusted according to the user's emotional state. For example, a finder feeling anxious will receive a reassuring notification that clearly indicates what to do.

[0209] As a concrete example, imagine a citizen who finds a lost dog in a park, takes a picture of the dog with their smartphone, and uploads it to the system. The server analyzes this image and matches it with a database to send a detailed notification to the dog's owner, such as "The dog is being cared for at a nearby animal hospital." Based on audio data, if the system analyzes sounds indicating anxiety, it will add specific instructions for reuniting the dog to the notification to reassure the owner.

[0210] An example of a prompt message is as follows:

[0211] "Analyze the characteristics of the animal in the photo and find similar registration information. Then, analyze the citizen's sentiment from the following audio data and adjust the notification content. Audio data: 'I found a lost dog, and I'm worried about what to do with it.'"

[0212] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0213] Step 1:

[0214] The user takes a picture of an animal with their smartphone and uploads the image information to the system via the device. The input is image data of the animal, and the output is data to be sent to the server. At this stage, the image data is sent from the input device to the cloud server for preparation for analysis.

[0215] Step 2:

[0216] The server uses a computer vision library (OpenCV) to extract animal features based on the received image information. The input is the image data acquired in step 1, and the output is feature data. This process analyzes the animal's species, pattern, body shape, etc., and generates feature vectors for database searching.

[0217] Step 3:

[0218] The server compares the feature data with the information storage medium in the cloud database (Firebase) to search for matching animal information. The input is the feature data obtained in step 2, and the output is the matching animal information. This matching process compares the image with the registered information in the database to identify the corresponding record.

[0219] Step 4:

[0220] Based on the acquired matching information, the server prepares to send a notification to the relevant entity, namely the animal's owner or discoverer. The input is the matching information acquired in step 3, and the output is the notification data prepared for transmission. At this stage, the content to be sent is generated and the notification method is determined.

[0221] Step 5:

[0222] The server analyzes the audio data received from the terminal and detects the user's emotional state using an audio analysis API (IBM Watson). The input is audio data, and the output is emotional information. In this process, the tone and speed of the voice are analyzed to determine the emotional state.

[0223] Step 6:

[0224] The server adjusts the notification content based on emotional information and sends the notification to the subject in the most optimal form. The input is the emotional information obtained in step 5 and the notification data prepared in step 4, and the output is the final notification data. The notification content is customized according to the user's emotional state, and reassuring information is added.

[0225] Step 7:

[0226] The user receives coordinated notifications through their device to check the animal's location and the next action to take. The input is the final notification data sent from the server, and the output is information that helps the user decide what to do. In this step, the user can review the notification and take the necessary action.

[0227] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0228] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0229] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0230] [Second Embodiment]

[0231] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0232] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0233] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0234] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0235] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0236] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0237] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0238] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0239] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0240] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0241] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0242] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0243] In the animal search support system according to the present invention, image data acquired from an input device is analyzed in a processing device, and animal characteristics are extracted from the analysis results. The extracted characteristics are compared with existing information in a database on a server to determine possible matches. This process is mainly carried out using image recognition technology and machine learning algorithms. Based on the results identified by the matching means, the server sends a notification to the relevant user, and when the animal is confirmed, the user can provide feedback via a mobile device.

[0244] Furthermore, in the animal adoption matching process, the server analyzes suitability based on animal information entered via the terminal and information on already registered potential adopters. Based on the established criteria, the server selects the most suitable candidate and notifies the corresponding user. This promotes the smooth transfer of animals.

[0245] During operation, users input additional information, such as questions, through their devices. The server then uses a natural language processing unit to analyze the request and retrieve relevant breeding and health information from its knowledge base. This allows users to efficiently obtain the necessary information, supporting the maintenance of healthy lives for their animals.

[0246] As a concrete example, if a user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the system, the server analyzes the image to identify the species and characteristics, and quickly matches it with the database. Based on this result, if the animal is registered, the owner is notified, and the animal can be returned home quickly. In addition, when recruiting foster homes for newly rescued animals, the server can provide necessary information to candidates who match the animal's characteristics and confirm their willingness to adopt. This makes it possible to smoothly and quickly transition animals to new homes.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] Users take photos of lost animals with their mobile devices and upload the image data to the system.

[0250] Step 2:

[0251] The server receives the uploaded image data and automatically starts the image analysis process. Here, computer vision technology is used to extract animal features in the image (e.g., species, color, body shape, pattern, etc.).

[0252] Step 3:

[0253] The server compares the analyzed feature data with an existing database. The matching algorithm searches the database for animal information with similar features and identifies the most likely matches.

[0254] Step 4:

[0255] Based on the matching results, the server sends a notification message to users who appear to be the animal's owner. Users receive this notification on their mobile devices and can check the information needed to reunite with their animal.

[0256] Step 5:

[0257] Users provide feedback on notifications, and if the found animal is correct, the information is fed back to the server. This feedback information is used to improve the platform's performance in the future.

[0258] Step 6:

[0259] When a foster parent matching is needed, users register animal information using their devices. This includes detailed information such as species, temperament, and health status.

[0260] Step 7:

[0261] The server compares the registered animal information with existing foster parent candidate information and selects the most suitable foster parent candidate from the database for each animal.

[0262] Step 8:

[0263] The server sends a notification containing details about the animal to the selected foster parent candidates and confirms the adoption process. This notification is delivered to the candidates via their devices.

[0264] Step 9:

[0265] Based on the notification received by the user, the system confirms their intention to adopt the animal. If necessary, the user can query the server with additional questions using natural language processing.

[0266] Step 10:

[0267] The server analyzes user inquiries, extracts relevant breeding information from its knowledge base, and provides users with accurate information.

[0268] This will allow the "AnimalGuardian" system to function smoothly and enable the development of harmonious relationships between animals and humans.

[0269] (Example 1)

[0270] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0271] There is a need for a system that can respond quickly and effectively when animals get lost or when new adoptive homes are needed. However, current methods have challenges, such as the time-consuming process of analyzing and matching animal characteristics and finding adoptive homes, and the difficulty in providing appropriate information and collecting feedback.

[0272] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0273] In this invention, the server includes a data processing unit for acquiring image data using an information acquisition device and analyzing the characteristics of animals from the image data; a data matching unit for searching for matching data by comparing the analyzed animal characteristics with existing data in a storage device; an information notification unit for notifying relevant users based on the matching results; a data analysis unit for analyzing requests using natural language processing technology and searching for relevant information from a knowledge database; and a feedback collection unit for enabling relevant users to quickly provide feedback when an animal is found based on the analysis results. This enables a series of processes such as the rapid identification and return of animals, provision of appropriate information, and smooth recommendation to potential adoptive parents.

[0274] "Image data" refers to the digital representation of visual information collected by an information acquisition device.

[0275] An "information acquisition device" is a device operated by a user to collect image data.

[0276] A "data processing unit" is a device that has processing functions for analyzing specific features from collected image data.

[0277] A "storage device" is a data storage device that stores existing information, such as a database, and uses it for verification purposes.

[0278] A "data matching unit" is a device that compares analyzed feature data with information in a storage device and searches for matching information.

[0279] An "information notification unit" is a device with communication capabilities that transmits information to relevant users based on the matching results.

[0280] A "data analysis unit" is a device that analyzes input question data and retrieves necessary information from a knowledge database.

[0281] The "Feedback Collection Unit" is a device that enables the rapid collection of feedback from users.

[0282] "Natural language processing technology" is a technology that analyzes user input and processes information based on text.

[0283] A "knowledge database" is a collection of data that structures and stores information and enables it to be searchable as needed.

[0284] This animal search support system is mainly realized by combining a data processing unit that analyzes image data, a data matching unit that matches with existing data in a database, an information notification unit that notifies relevant information to users, a data analysis unit that uses natural language processing technology, and a feedback collection unit that collects feedback.

[0285] When a user discovers a lost animal, they use a mobile terminal, which is an information acquisition device, to take a picture of the animal. The terminal provides image data by uploading the taken picture to the server. At this time, the user can additionally input information such as the situation of the found animal and the discovery location.

[0286] The server analyzes the received image data using a data processing unit. Specifically, it uses image analysis software, which is a common tool as an image recognition technology, to extract the characteristics of the animal from the image. In this process, visual characteristics such as species and patterns are identified by using machine learning algorithms.

[0287] The extracted characteristics are matched with the data stored in the storage device. The server uses a data matching unit to compare this characteristic data with the existing data in the internal database and search for matching information.

[0288] Next, the server promptly notifies the relevant users based on the database matching results. This provides a means of contact for the animal's safe return, if an owner exists.

[0289] This system also handles animals seeking foster homes. Users input data on rescued animals into their terminals and send it to the server. The server analyzes the registered foster parent candidate data and the animal's information to perform appropriate matching.

[0290] If the user has further questions, they can send requests to the server in text format through their terminal. By applying natural language processing technology, the server can retrieve relevant information from a knowledge database through a data analysis unit and provide specific advice and information to the user.

[0291] A concrete example is when a user enters a prompt such as, "Describe the characteristics of this animal and retrieve information that matches the database," and the server then processes the request and provides the information. This entire process aims to make the discovery and protection of animals faster and more efficient.

[0292] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0293] Step 1:

[0294] The user takes a picture of the lost animal using a mobile device and uploads it to the system. The input is the image data of the animal taken by the user and additional information (e.g., location where it was found). The output of this step is the image data sent to the server. The device functions as an information acquisition device and performs initial data collection.

[0295] Step 2:

[0296] The server analyzes the received image data using a data processing unit. The input is the image data obtained in step 1. The server processes the image data using image recognition technology to extract the animal species and characteristics. The output is characteristic data of the identified animals. Machine learning algorithms are used in this process.

[0297] Step 3:

[0298] The server uses feature data to match it with a database in storage. The input is the animal feature data generated in step 2. The server uses a data matching unit to compare the features with existing data and search for matches. The output is the matching results and related information. Here, if matching information for an animal is found, its details are identified.

[0299] Step 4:

[0300] The server notifies the relevant users through the information notification unit based on the matching results. The input is the matching results from step 3. If the owner is found, the server uses their contact information to send a notification to the owner. The output is the notification to the owner and the provision of a means of contact. This notification enables quick reporting.

[0301] Step 5:

[0302] Users input animal rescue data and information on potential new adoptive families into a terminal and send it to the server. Input consists of detailed information about the animals. The terminal supplies this data to the server in digital format. Output is the transmission of information to the server.

[0303] Step 6:

[0304] The server processes the input data in its data analysis unit and compares it with foster parent candidate information to perform appropriate matching. The input consists of detailed animal information and foster parent candidate information obtained in step 5. The output is a list of the most suitable foster parent candidates. The server selects candidates based on their suitability and prepares to notify them.

[0305] Step 7:

[0306] If the user has a question, the user sends it from the terminal to the server as a text prompt. The input is the user's question data. The output of this step is the prompt sent to the server.

[0307] Step 8:

[0308] The server uses a data analysis unit to analyze the question by applying natural language processing technology and searches for relevant information from the knowledge database. The input is the question data of Step 7. The output is the retrieved relevant information. The server thereby prepares information corresponding to the user's request.

[0309] Step 9:

[0310] The server provides knowledge to the user based on the analysis result. The input is the relevant information obtained in Step 8. The output is the provision of information to the user. Through this, the server helps to enhance the user's knowledge and solve problems quickly.

[0311] (Application Example 1)

[0312] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0313] In searching for animals and recruiting foster parents, there are problems in quickly and accurately obtaining animal information and performing appropriate notifications and matching. In particular, when using a robot for pet accompaniment support, it is required to improve the accuracy of on-site animal recognition and information provision.

[0314] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0315] In this invention, the server includes a computing device for acquiring data from an input device and analyzing the characteristics of animals from that data; a search means for searching for matching information by comparing it with existing information in an information storage device based on the analyzed characteristics of animals; a notification means for notifying relevant individuals based on the search results; and a means for acquiring the characteristics of animals with a video acquisition device and transmitting them to the computing device in real time. This makes it possible to quickly recognize the characteristics of animals and efficiently provide relevant information.

[0316] "Data" refers to a collection of information acquired by an input device for analyzing the characteristics of an animal.

[0317] An "input device" is a device used to acquire data from an external source and plays a role in transmitting necessary information to the computing device.

[0318] A "calculation unit" is a device that has the ability to perform calculations to analyze acquired data and extract the characteristics of animals.

[0319] An "information storage device" is a device that stores existing information as a database and provides a foundation for comparing it with new data.

[0320] A "search method" refers to a method or device for comparing existing information and data within an information storage device to find matching information.

[0321] "Notification means" refers to a method or device for notifying relevant individuals of the results obtained by the search means.

[0322] An "image acquisition device" is a device such as a camera or sensor used to capture the characteristics of animals, and it acquires this data in real time.

[0323] The system implementing this invention mainly includes the following components. Data is acquired using an image acquisition device, such as a high-resolution camera, and functions as an input device. This captures the characteristics of the animal. The acquired data is then transmitted to a computing device, specifically a computing device such as a Raspberry Pi. The computing device performs characteristic analysis of the animal using image processing software such as OpenCV or TensorFlow.

[0324] The server receives the analyzed data and compares it with the information storage device, which is an SQLite database in the cloud. During this process, a Python program runs as a search tool to find information that matches specific criteria. If the comparison is successful, a notification is sent to the user via their mobile device or other notification device.

[0325] As a concrete example, suppose a pet monitoring robot patrols a park and finds a dog that appears to be lost. The robot captures an image of the dog and immediately sends it to a server. The server analyzes the image and checks its database for similar information. If a match is found, the dog's owner is promptly notified. In this way, the robot can be effectively utilized, enabling rapid problem resolution.

[0326] An example of a prompt message might be, "Identify the animal species and characteristics from this image and verify that it matches the information in the database." In this way, the system sequentially analyzes the characteristics of the target animal and quickly links related information, enabling efficient search support.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The camera mounted on the device captures images of the surroundings and recognizes animals. The input is high-resolution image data acquired by the camera. This data is preprocessed to remove noise and normalize the images. This prepares the data for analysis.

[0330] Step 2:

[0331] The terminal's computing power extracts animal features using the pre-processed image data from the previous step. Image processing using OpenCV is performed here, analyzing key features such as the animal's outline, color, and patterns. The output is presented as a characteristics dataset, which is used in the next matching step.

[0332] Step 3:

[0333] The server receives the characteristic dataset transmitted from the computing unit and compares it with a database in an information storage device located in the cloud. Here, a generative AI model is used to analyze the correlation between the received dataset and existing records in the database. The output is the result of the matching determination with the animal information stored in the database.

[0334] Step 4:

[0335] The server uses search tools based on the matching results to extract relevant information. For example, if the found animal is registered in the database, it extracts the registered owner information. At this stage, the program uses the prompt statement "Identify the animal species and characteristics from this image and verify that it matches the information in the database."

[0336] Step 5:

[0337] The server, through a notification system, notifies the relevant owner or associated individual if the data matches based on the matching results. The notification is sent via a terminal application, and the owner is provided with specific characteristic and location information. The output is a notification message to the user.

[0338] Step 6:

[0339] Users check notifications received via their devices and provide feedback or take action as needed. This user feedback is sent back to the server as information useful for updating the database and improving data accuracy. This interaction through feedback improves the overall accuracy and efficiency of the system.

[0340] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0341] In the animal search support system according to the present invention, image data is acquired by an input device, and the characteristics of the animal are analyzed from the image data by a processing device. This analysis uses computer vision technology to extract characteristics such as the animal's species, pattern, and body shape, and the server searches for matching information by comparing it with existing information in a database. The results identified based on the matching means are notified to the relevant users. This notification is promptly made to the animal's owner or related parties, and the user confirms the information via a terminal.

[0342] Furthermore, this system incorporates an emotion engine that can recognize emotions from the user's voice data. This emotion recognition device analyzes the user's voice tone, speed, and word choice to understand the user's emotional state. Based on the analysis results, the server adjusts the notification content using emotion-responsive means. For example, if the user is in an anxious situation, the system will provide a more detailed and reassuring notification.

[0343] Furthermore, by monitoring the user's emotional state in real time and optimizing the overall system response through emotion optimization methods, the user experience can be improved. This process is also effective as a means of providing emotional support when reuniting with animals, enabling the rapid and appropriate provision of information to support the complex emotions the user may be experiencing.

[0344] For example, when a user finds a lost animal, they take a picture of the animal's characteristics with their device and upload it to the system. The server then performs image analysis and matches it against a database. Simultaneously, if anxiety is detected from the user's voice, the emotion optimization mechanism activates, and the server sends a notification with particularly detailed explanations and clear instructions for the next steps to support the user. In this way, the present invention enables animal search support and optimization of the user experience.

[0345] The following describes the processing flow.

[0346] Step 1:

[0347] A user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the "Animal Search Support System."

[0348] Step 2:

[0349] The server receives the uploaded image data and uses an image analysis engine to extract animal features. These features include species, markings, and body shape.

[0350] Step 3:

[0351] The server compares the extracted feature data with information in existing databases to identify potential matches.

[0352] Step 4:

[0353] Based on the matching results, the server sends a notification to the user who is believed to be the animal's owner. The content of this notification is optimized according to the user's situation by the emotion engine within the server.

[0354] Step 5:

[0355] Users receive notifications on their devices and confirm necessary actions based on the sentiment analysis results. If anxiety is detected, the notification will include additional information to provide reassurance.

[0356] Step 6:

[0357] When a user is reunited with an animal based on a notification, the server acquires the user's voice data and analyzes it with an emotion recognition device to detect the emotions associated with the reunion.

[0358] Step 7:

[0359] Based on the detected emotional state, the server uses emotional support tools to provide the user with appropriate information and determines whether further support is needed.

[0360] Step 8:

[0361] Users register information about their animals, and if adoption matching is needed, they send detailed information to the server via their device.

[0362] Step 9:

[0363] The server analyzes existing foster parent candidate information based on registered animal information and selects the most suitable candidate.

[0364] Step 10:

[0365] The server sends notifications to selected foster parent candidates, confirming details about the animal and the adoption process. This process also utilizes an emotional engine to appropriately adjust the notification content.

[0366] This process allows the entire system, from animal search assistance to foster care matching and emotional support, to operate effectively and humanely.

[0367] (Example 2)

[0368] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0369] When an animal goes missing, there is a need to quickly and accurately locate its whereabouts and provide information to its owner and other relevant parties. However, conventional methods lack sufficient systems for efficiently analyzing and matching animal characteristics, and furthermore, they do not allow for notifications to be adjusted according to the user's emotions. Therefore, the challenge is to improve the efficiency of animal searches while optimizing the user experience.

[0370] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0371] In this invention, the server includes an analysis means for acquiring image data using a recognition device and analyzing the characteristics of animals from the image data; a search means for searching for matching information by comparing it with existing information in the information resource based on the analyzed characteristics of animals; a notification means for notifying relevant users based on the search results; an emotion response means for recognizing emotions from the user's voice data and adjusting the notification content; and an emotion optimization means for optimizing the overall response based on the results of the emotion recognition. This enables rapid and accurate searching of animals and flexible responses that respond to the user's emotions.

[0372] A "recognition device" is a device that acquires image data using an input device and supplements the information required by the system.

[0373] "Analysis means" refers to technologies and devices that extract animal characteristics from acquired image data to identify species, patterns, body shape, etc.

[0374] A "search method" is a process or device that searches for matching information by comparing the analyzed characteristics of an animal with existing information within an information resource.

[0375] "Notification means" refers to communication methods or means for conveying search results and related information to the person or system that should receive them.

[0376] "Emotional response means" refers to technologies and devices that analyze a user's emotional state based on their voice data and adjust the content of notifications based on the results obtained.

[0377] "Emotional optimization methods" refer to methods and tools for monitoring users' emotional states in real time and optimizing the overall system response.

[0378] This system is designed to effectively support animal searches. The server begins collecting information about animals by first acquiring image data using a recognition device. Common input devices such as smartphones and digital cameras can be used for this purpose. The server processes the acquired image data using analysis tools to extract features such as the animal's species, markings, and body shape. Computer vision technologies such as OpenCV and TensorFlow are used for the analysis.

[0379] The analyzed data is compared with information stored in a database on the server using search tools to find matching information. This process is efficiently carried out using SQL or NoSQL databases. The server then sends the results to relevant individuals or entities using notification methods. Notification methods can include email, SMS, or push notifications.

[0380] In addition, the device collects user voice data and sends it to a server. This data is analyzed using speech recognition technologies such as IBM Watson and Google Cloud Speech-to-Text. As a result, the emotion response system analyzes the user's emotions and adjusts the notification content accordingly. The emotion optimization system monitors the user's real-time emotional state and appropriately adjusts the system's overall response to provide a better user experience.

[0381] As a concrete example, a user could take a picture of a lost dog they see in a park with their smartphone and upload it to the system. This image would be analyzed on the server to find information about the dog. At the same time, if the user is expressing anxiety by phone, an emotion optimization mechanism would be activated to provide support by notifying them of specific next steps to help them feel at ease.

[0382] An example of a prompt message to be input to the generating AI model is: "I have uploaded a picture of a dog. Please check the breed and characteristics of this dog. For users who are unsure, please generate a message that explains the next steps in detail."

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The server receives image data from the terminal. The user uses the terminal's camera to take pictures of animals and uploads them to the system. This image data is used as input to identify the animal's species and location.

[0386] Step 2:

[0387] The server processes the acquired image data through analysis tools. Here, image processing technologies such as OpenCV and TensorFlow are used to extract animal features. Specifically, this involves analyzing the shape, pattern, and body type of the animals in the images. The data obtained from this analysis is then used as feature information for the next step.

[0388] Step 3:

[0389] The server uses search tools to compare the animal characteristic information obtained through analysis with a database. The database contains existing animal information, and techniques such as SQL queries are used to search for matching data. The output is either information on a matching animal or a result indicating no match.

[0390] Step 4:

[0391] The server sends the search results to the user via a notification system. This process delivers information to the user's device using email or push notifications. The notification includes detailed information about the discovered animal and the next steps to take.

[0392] Step 5:

[0393] The device collects voice data from the user and sends it to the server. This collected voice data is used as input for emotion recognition.

[0394] Step 6:

[0395] The server analyzes the audio data to understand the user's emotional state. Using IBM Watson or Google Cloud Speech-to-Text, it converts the audio to text and then performs emotional analysis. The output provides information on whether the user is feeling anxiety or joy.

[0396] Step 7:

[0397] The server adjusts notification content based on the emotional information it receives. Using emotional optimization techniques, detailed explanations and reassuring phrases are added. This adjustment allows users to more effectively utilize the information from the system.

[0398] (Application Example 2)

[0399] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0400] When an animal goes missing, there is a need to quickly and effectively locate it and provide appropriate information to those involved. Furthermore, a challenge is to improve the user experience by facilitating smooth communication through notifications that take into account the emotional state of the finder.

[0401] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0402] In this invention, the server includes a processing mechanism for acquiring image information via an input device and analyzing the characteristics of an animal from that image information; an identification means for searching for matching information by comparing it with existing information in an information storage medium based on the analyzed characteristics of the animal; a transmission means for notifying the relevant entity based on the matching result; an emotion analysis mechanism for analyzing voice data and detecting emotional state; and a response adjustment means for adjusting the notification content based on the detected emotional state. This enables the search for animals and the rapid and emotionally sensitive provision of information to the relevant entity.

[0403] "Image information" refers to visual data acquired by an input device to identify the characteristics of an animal.

[0404] An "input device" is a device used to acquire image information, specifically such as a camera or a smartphone.

[0405] A "processing mechanism" is a device or program that has the function of analyzing the characteristics of an animal using acquired image information.

[0406] An "information storage medium" is a database or storage system in which existing animal information is recorded.

[0407] "Identification means" refers to a function that identifies relevant information by comparing the analyzed characteristics of an animal with data on an information storage medium.

[0408] "Transmission means" refers to communication means for notifying the relevant entity of the results obtained by the identification means.

[0409] "Audio data" refers to recordings of voices obtained from relevant parties and is used for sentiment analysis.

[0410] An "emotion analysis mechanism" is a device or program that analyzes audio data and has the function of detecting the emotional state of the relevant subject.

[0411] The "response adjustment mechanism" is a function that optimizes the content of notifications based on the detected emotional state and provides information tailored to the recipient.

[0412] This invention aims to process and quickly identify animal images taken by citizens using their smartphones in an animal search support system. The system first acquires image information from the smartphone and sends it to a server. On the server, it uses computer vision libraries such as OpenCV to analyze the animal's characteristics from the image information. The analyzed characteristics are compared with information storage media in a cloud database such as Firebase to identify the corresponding animal.

[0413] Based on the matching results, notifications are sent to the relevant parties, namely the animal's owner or finder. At this time, voice analysis APIs such as IBM Watson are used to detect emotional states from the voice data, and the notification content is adjusted according to the user's emotional state. For example, a finder feeling anxious will receive a reassuring notification that clearly indicates what to do.

[0414] As a concrete example, imagine a citizen who finds a lost dog in a park, takes a picture of the dog with their smartphone, and uploads it to the system. The server analyzes this image and matches it with a database to send a detailed notification to the dog's owner, such as "The dog is being cared for at a nearby animal hospital." Based on audio data, if the system analyzes sounds indicating anxiety, it will add specific instructions for reuniting the dog to the notification to reassure the owner.

[0415] An example of a prompt message is as follows:

[0416] "Analyze the characteristics of the animal in the photo and find similar registration information. Then, analyze the citizen's sentiment from the following audio data and adjust the notification content. Audio data: 'I found a lost dog, and I'm worried about what to do with it.'"

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The user takes a picture of an animal with their smartphone and uploads the image information to the system via the device. The input is image data of the animal, and the output is data to be sent to the server. At this stage, the image data is sent from the input device to the cloud server for preparation for analysis.

[0420] Step 2:

[0421] The server uses a computer vision library (OpenCV) to extract animal features based on the received image information. The input is the image data acquired in step 1, and the output is feature data. This process analyzes the animal's species, pattern, body shape, etc., and generates feature vectors for database searching.

[0422] Step 3:

[0423] The server compares the feature data with the information storage medium in the cloud database (Firebase) to search for matching animal information. The input is the feature data obtained in step 2, and the output is the matching animal information. This matching process compares the image with the registered information in the database to identify the corresponding record.

[0424] Step 4:

[0425] Based on the acquired matching information, the server prepares to send a notification to the relevant entity, namely the animal's owner or discoverer. The input is the matching information acquired in step 3, and the output is the notification data prepared for transmission. At this stage, the content to be sent is generated and the notification method is determined.

[0426] Step 5:

[0427] The server analyzes the audio data received from the terminal and detects the user's emotional state using an audio analysis API (IBM Watson). The input is audio data, and the output is emotional information. In this process, the tone and speed of the voice are analyzed to determine the emotional state.

[0428] Step 6:

[0429] The server adjusts the notification content based on emotional information and sends the notification to the subject in the most optimal form. The input is the emotional information obtained in step 5 and the notification data prepared in step 4, and the output is the final notification data. The notification content is customized according to the user's emotional state, and reassuring information is added.

[0430] Step 7:

[0431] The user receives coordinated notifications through their device to check the animal's location and the next action to take. The input is the final notification data sent from the server, and the output is information that helps the user decide what to do. In this step, the user can review the notification and take the necessary action.

[0432] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0433] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0434] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0435] [Third Embodiment]

[0436] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0437] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0438] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0439] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0440] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0441] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0442] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0443] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0444] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0445] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0446] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0447] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0448] In the animal search support system according to the present invention, image data acquired from an input device is analyzed in a processing device, and animal characteristics are extracted from the analysis results. The extracted characteristics are compared with existing information in a database on a server to determine possible matches. This process is mainly carried out using image recognition technology and machine learning algorithms. Based on the results identified by the matching means, the server sends a notification to the relevant user, and when the animal is confirmed, the user can provide feedback via a mobile device.

[0449] Furthermore, in the animal adoption matching process, the server analyzes suitability based on animal information entered via the terminal and information on already registered potential adopters. Based on the established criteria, the server selects the most suitable candidate and notifies the corresponding user. This promotes the smooth transfer of animals.

[0450] During operation, users input additional information, such as questions, through their devices. The server then uses a natural language processing unit to analyze the request and retrieve relevant breeding and health information from its knowledge base. This allows users to efficiently obtain the necessary information, supporting the maintenance of healthy lives for their animals.

[0451] As a concrete example, if a user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the system, the server analyzes the image to identify the species and characteristics, and quickly matches it with the database. Based on this result, if the animal is registered, the owner is notified, and the animal can be returned home quickly. In addition, when recruiting foster homes for newly rescued animals, the server can provide necessary information to candidates who match the animal's characteristics and confirm their willingness to adopt. This makes it possible to smoothly and quickly transition animals to new homes.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] Users take photos of lost animals with their mobile devices and upload the image data to the system.

[0455] Step 2:

[0456] The server receives the uploaded image data and automatically starts the image analysis process. Here, computer vision technology is used to extract animal features in the image (e.g., species, color, body shape, pattern, etc.).

[0457] Step 3:

[0458] The server compares the analyzed feature data with an existing database. The matching algorithm searches the database for animal information with similar features and identifies the most likely matches.

[0459] Step 4:

[0460] Based on the matching results, the server sends a notification message to users who appear to be the animal's owner. Users receive this notification on their mobile devices and can check the information needed to reunite with their animal.

[0461] Step 5:

[0462] Users provide feedback on notifications, and if the found animal is correct, the information is fed back to the server. This feedback information is used to improve the platform's performance in the future.

[0463] Step 6:

[0464] When a foster parent matching is needed, users register animal information using their devices. This includes detailed information such as species, temperament, and health status.

[0465] Step 7:

[0466] The server compares the registered animal information with existing foster parent candidate information and selects the most suitable foster parent candidate from the database for each animal.

[0467] Step 8:

[0468] The server sends a notification containing details about the animal to the selected foster parent candidates and confirms the adoption process. This notification is delivered to the candidates via their devices.

[0469] Step 9:

[0470] Based on the notification received by the user, the system confirms their intention to adopt the animal. If necessary, the user can query the server with additional questions using natural language processing.

[0471] Step 10:

[0472] The server analyzes user inquiries, extracts relevant breeding information from its knowledge base, and provides users with accurate information.

[0473] This will allow the "AnimalGuardian" system to function smoothly and enable the development of harmonious relationships between animals and humans.

[0474] (Example 1)

[0475] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0476] There is a need for a system that can respond quickly and effectively when animals get lost or when new adoptive homes are needed. However, current methods have challenges, such as the time-consuming process of analyzing and matching animal characteristics and finding adoptive homes, and the difficulty in providing appropriate information and collecting feedback.

[0477] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0478] In this invention, the server includes a data processing unit for acquiring image data using an information acquisition device and analyzing the characteristics of animals from the image data; a data matching unit for searching for matching data by comparing the analyzed animal characteristics with existing data in a storage device; an information notification unit for notifying relevant users based on the matching results; a data analysis unit for analyzing requests using natural language processing technology and searching for relevant information from a knowledge database; and a feedback collection unit for enabling relevant users to quickly provide feedback when an animal is found based on the analysis results. This enables a series of processes such as the rapid identification and return of animals, provision of appropriate information, and smooth recommendation to potential adoptive parents.

[0479] "Image data" refers to the digital representation of visual information collected by an information acquisition device.

[0480] An "information acquisition device" is a device operated by a user to collect image data.

[0481] A "data processing unit" is a device that has processing functions for analyzing specific features from collected image data.

[0482] A "storage device" is a data storage device that stores existing information, such as a database, and uses it for verification purposes.

[0483] A "data matching unit" is a device that compares analyzed feature data with information in a storage device and searches for matching information.

[0484] An "information notification unit" is a device with communication capabilities that transmits information to relevant users based on the matching results.

[0485] A "data analysis unit" is a device that analyzes input question data and retrieves necessary information from a knowledge database.

[0486] A "feedback collection unit" is a device that enables the rapid collection of feedback from users.

[0487] "Natural language processing technology" is a technology that analyzes user input and processes information in a text-based manner.

[0488] A "knowledge database" is a collection of data that structures and stores information, making it searchable as needed.

[0489] This animal search support system is implemented by combining a data processing unit that primarily analyzes image data, a data matching unit that compares it with existing data in a database, an information notification unit that notifies users of relevant information, a data analysis unit that uses natural language processing technology, and a feedback collection unit that collects feedback.

[0490] When a user finds a lost animal, they use a mobile device, which is an information acquisition device, to take a picture of the animal. The device provides the image data by uploading the captured image to a server. At this time, the user can also input supplementary information such as the animal's condition and the location where it was found.

[0491] The server analyzes the received image data using a data processing unit. Specifically, it uses image analysis software, a common tool for image recognition technology, to extract animal features from the images. In this process, machine learning algorithms are used to identify visual features such as species and patterns.

[0492] The extracted features are compared with data stored in the storage device. The server uses a data matching unit to compare this feature data with existing data in the internal database and search for matching information.

[0493] Next, the server promptly notifies the relevant users based on the database matching results. This provides a means of contact for the animal's safe return, if an owner exists.

[0494] This system also handles animals seeking foster homes. Users input data on rescued animals into their terminals and send it to the server. The server analyzes the registered foster parent candidate data and the animal's information to perform appropriate matching.

[0495] If the user has further questions, they can send requests to the server in text format through their terminal. By applying natural language processing technology, the server can retrieve relevant information from a knowledge database through a data analysis unit and provide specific advice and information to the user.

[0496] A concrete example is when a user enters a prompt such as, "Describe the characteristics of this animal and retrieve information that matches the database," and the server then processes the request and provides the information. This entire process aims to make the discovery and protection of animals faster and more efficient.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] The user takes a picture of the lost animal using a mobile device and uploads it to the system. The input is the image data of the animal taken by the user and additional information (e.g., location where it was found). The output of this step is the image data sent to the server. The device functions as an information acquisition device and performs initial data collection.

[0500] Step 2:

[0501] The server analyzes the received image data using a data processing unit. The input is the image data obtained in step 1. The server processes the image data using image recognition technology to extract the animal species and characteristics. The output is characteristic data of the identified animals. Machine learning algorithms are used in this process.

[0502] Step 3:

[0503] The server uses feature data to match it with a database in storage. The input is the animal feature data generated in step 2. The server uses a data matching unit to compare the features with existing data and search for matches. The output is the matching results and related information. Here, if matching information for an animal is found, its details are identified.

[0504] Step 4:

[0505] The server notifies the relevant users through the information notification unit based on the matching results. The input is the matching results from step 3. If the owner is found, the server uses their contact information to send a notification to the owner. The output is the notification to the owner and the provision of a means of contact. This notification enables quick reporting.

[0506] Step 5:

[0507] Users input animal rescue data and information on potential new adoptive families into a terminal and send it to the server. Input consists of detailed information about the animals. The terminal supplies this data to the server in digital format. Output is the transmission of information to the server.

[0508] Step 6:

[0509] The server processes the input data in its data analysis unit and compares it with foster parent candidate information to perform appropriate matching. The input consists of detailed animal information and foster parent candidate information obtained in step 5. The output is a list of the most suitable foster parent candidates. The server selects candidates based on their suitability and prepares to notify them.

[0510] Step 7:

[0511] If the user has a question, they send it from the terminal to the server as a text prompt. The input is the user's question data. The output of this step is the prompt sent to the server.

[0512] Step 8:

[0513] The server uses a data analysis unit and applies natural language processing technology to analyze the question and retrieve relevant information from the knowledge database. The input is the question data from step 7. The output is the retrieved relevant information. The server then prepares the information requested by the user.

[0514] Step 9:

[0515] The server provides knowledge to the user based on the analysis results. The input is the relevant information obtained in step 8. The output is the information provided to the user. Through this, the server helps to strengthen the user's knowledge and solve problems quickly.

[0516] (Application Example 1)

[0517] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0518] In animal search and adoption programs, there are challenges in quickly and accurately obtaining animal information and providing appropriate notifications and matching. In particular, when using robots to assist pets, improving the accuracy of on-site animal recognition and information provision is essential.

[0519] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0520] In this invention, the server includes a computing device for acquiring data from an input device and analyzing the characteristics of animals from that data; a search means for searching for matching information by comparing it with existing information in an information storage device based on the analyzed characteristics of animals; a notification means for notifying relevant individuals based on the search results; and a means for acquiring the characteristics of animals with a video acquisition device and transmitting them to the computing device in real time. This makes it possible to quickly recognize the characteristics of animals and efficiently provide relevant information.

[0521] "Data" refers to a collection of information acquired by an input device for analyzing the characteristics of an animal.

[0522] An "input device" is a device used to acquire data from an external source and plays a role in transmitting necessary information to the computing device.

[0523] A "calculation unit" is a device that has the ability to perform calculations to analyze acquired data and extract the characteristics of animals.

[0524] An "information storage device" is a device that stores existing information as a database and provides a foundation for comparing it with new data.

[0525] A "search method" refers to a method or device for comparing existing information and data within an information storage device to find matching information.

[0526] "Notification means" refers to a method or device for notifying relevant individuals of the results obtained by the search means.

[0527] An "image acquisition device" is a device such as a camera or sensor used to capture the characteristics of animals, and it acquires this data in real time.

[0528] The system implementing this invention mainly includes the following components. Data is acquired using an image acquisition device, such as a high-resolution camera, and functions as an input device. This captures the characteristics of the animal. The acquired data is then transmitted to a computing device, specifically a computing device such as a Raspberry Pi. The computing device performs characteristic analysis of the animal using image processing software such as OpenCV or TensorFlow.

[0529] The server receives the analyzed data and compares it with the information storage device, which is an SQLite database in the cloud. During this process, a Python program runs as a search tool to find information that matches specific criteria. If the comparison is successful, a notification is sent to the user via their mobile device or other notification device.

[0530] As a concrete example, suppose a pet monitoring robot patrols a park and finds a dog that appears to be lost. The robot captures an image of the dog and immediately sends it to a server. The server analyzes the image and checks its database for similar information. If a match is found, the dog's owner is promptly notified. In this way, the robot can be effectively utilized, enabling rapid problem resolution.

[0531] An example of a prompt message might be, "Identify the animal species and characteristics from this image and verify that it matches the information in the database." In this way, the system sequentially analyzes the characteristics of the target animal and quickly links related information, enabling efficient search support.

[0532] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0533] Step 1:

[0534] The camera mounted on the device captures images of the surroundings and recognizes animals. The input is high-resolution image data acquired by the camera. This data is preprocessed to remove noise and normalize the images. This prepares the data for analysis.

[0535] Step 2:

[0536] The terminal's computing power extracts animal features using the pre-processed image data from the previous step. Image processing using OpenCV is performed here, analyzing key features such as the animal's outline, color, and patterns. The output is presented as a characteristics dataset, which is used in the next matching step.

[0537] Step 3:

[0538] The server receives the characteristic dataset transmitted from the computing unit and compares it with a database in an information storage device located in the cloud. Here, a generative AI model is used to analyze the correlation between the received dataset and existing records in the database. The output is the result of the matching determination with the animal information stored in the database.

[0539] Step 4:

[0540] The server uses search tools based on the matching results to extract relevant information. For example, if the found animal is registered in the database, it extracts the registered owner information. At this stage, the program uses the prompt statement "Identify the animal species and characteristics from this image and verify that it matches the information in the database."

[0541] Step 5:

[0542] The server, through a notification system, notifies the relevant owner or associated individual if the data matches based on the matching results. The notification is sent via a terminal application, and the owner is provided with specific characteristic and location information. The output is a notification message to the user.

[0543] Step 6:

[0544] Users check notifications received via their devices and provide feedback or take action as needed. This user feedback is sent back to the server as information useful for updating the database and improving data accuracy. This interaction through feedback improves the overall accuracy and efficiency of the system.

[0545] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0546] In the animal search support system according to the present invention, image data is acquired by an input device, and the characteristics of the animal are analyzed from the image data by a processing device. This analysis uses computer vision technology to extract characteristics such as the animal's species, pattern, and body shape, and the server searches for matching information by comparing it with existing information in a database. The results identified based on the matching means are notified to the relevant users. This notification is promptly made to the animal's owner or related parties, and the user confirms the information via a terminal.

[0547] Furthermore, this system incorporates an emotion engine that can recognize emotions from the user's voice data. This emotion recognition device analyzes the user's voice tone, speed, and word choice to understand the user's emotional state. Based on the analysis results, the server adjusts the notification content using emotion-responsive means. For example, if the user is in an anxious situation, the system will provide a more detailed and reassuring notification.

[0548] Furthermore, by monitoring the user's emotional state in real time and optimizing the overall system response through emotion optimization methods, the user experience can be improved. This process is also effective as a means of providing emotional support when reuniting with animals, enabling the rapid and appropriate provision of information to support the complex emotions the user may be experiencing.

[0549] For example, when a user finds a lost animal, they take a picture of the animal's characteristics with their device and upload it to the system. The server then performs image analysis and matches it against a database. Simultaneously, if anxiety is detected from the user's voice, the emotion optimization mechanism activates, and the server sends a notification with particularly detailed explanations and clear instructions for the next steps to support the user. In this way, the present invention enables animal search support and optimization of the user experience.

[0550] The following describes the processing flow.

[0551] Step 1:

[0552] A user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the "Animal Search Support System."

[0553] Step 2:

[0554] The server receives the uploaded image data and uses an image analysis engine to extract animal features. These features include species, markings, and body shape.

[0555] Step 3:

[0556] The server compares the extracted feature data with information in existing databases to identify potential matches.

[0557] Step 4:

[0558] Based on the matching results, the server sends a notification to the user who is believed to be the animal's owner. The content of this notification is optimized according to the user's situation by the emotion engine within the server.

[0559] Step 5:

[0560] Users receive notifications on their devices and confirm necessary actions based on the sentiment analysis results. If anxiety is detected, the notification will include additional information to provide reassurance.

[0561] Step 6:

[0562] When a user is reunited with an animal based on a notification, the server acquires the user's voice data and analyzes it with an emotion recognition device to detect the emotions associated with the reunion.

[0563] Step 7:

[0564] Based on the detected emotional state, the server uses emotional support tools to provide the user with appropriate information and determines whether further support is needed.

[0565] Step 8:

[0566] Users register information about their animals, and if adoption matching is needed, they send detailed information to the server via their device.

[0567] Step 9:

[0568] The server analyzes existing foster parent candidate information based on registered animal information and selects the most suitable candidate.

[0569] Step 10:

[0570] The server sends notifications to selected foster parent candidates, confirming details about the animal and the adoption process. This process also utilizes an emotional engine to appropriately adjust the notification content.

[0571] This process allows the entire system, from animal search assistance to foster care matching and emotional support, to operate effectively and humanely.

[0572] (Example 2)

[0573] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0574] When an animal goes missing, there is a need to quickly and accurately locate its whereabouts and provide information to its owner and other relevant parties. However, conventional methods lack sufficient systems for efficiently analyzing and matching animal characteristics, and furthermore, they do not allow for notifications to be adjusted according to the user's emotions. Therefore, the challenge is to improve the efficiency of animal searches while optimizing the user experience.

[0575] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0576] In this invention, the server includes an analysis means for acquiring image data using a recognition device and analyzing the characteristics of animals from the image data; a search means for searching for matching information by comparing it with existing information in the information resource based on the analyzed characteristics of animals; a notification means for notifying relevant users based on the search results; an emotion response means for recognizing emotions from the user's voice data and adjusting the notification content; and an emotion optimization means for optimizing the overall response based on the results of the emotion recognition. This enables rapid and accurate searching of animals and flexible responses that respond to the user's emotions.

[0577] A "recognition device" is a device that acquires image data using an input device and supplements the information required by the system.

[0578] "Analysis means" refers to technologies and devices that extract animal characteristics from acquired image data to identify species, patterns, body shape, etc.

[0579] A "search method" is a process or device that searches for matching information by comparing the analyzed characteristics of an animal with existing information within an information resource.

[0580] "Notification means" refers to communication methods or means for conveying search results and related information to the person or system that should receive them.

[0581] "Emotional response means" refers to technologies and devices that analyze a user's emotional state based on their voice data and adjust the content of notifications based on the results obtained.

[0582] "Emotional optimization methods" refer to methods and tools for monitoring users' emotional states in real time and optimizing the overall system response.

[0583] This system is designed to effectively support animal searches. The server begins collecting information about animals by first acquiring image data using a recognition device. Common input devices such as smartphones and digital cameras can be used for this purpose. The server processes the acquired image data using analysis tools to extract features such as the animal's species, markings, and body shape. Computer vision technologies such as OpenCV and TensorFlow are used for the analysis.

[0584] The analyzed data is compared with information stored in a database on the server using search tools to find matching information. This process is efficiently carried out using SQL or NoSQL databases. The server then sends the results to relevant individuals or entities using notification methods. Notification methods can include email, SMS, or push notifications.

[0585] In addition, the device collects user voice data and sends it to a server. This data is analyzed using speech recognition technologies such as IBM Watson and Google Cloud Speech-to-Text. As a result, the emotion response system analyzes the user's emotions and adjusts the notification content accordingly. The emotion optimization system monitors the user's real-time emotional state and appropriately adjusts the system's overall response to provide a better user experience.

[0586] As a concrete example, a user could take a picture of a lost dog they see in a park with their smartphone and upload it to the system. This image would be analyzed on the server to find information about the dog. At the same time, if the user is expressing anxiety by phone, an emotion optimization mechanism would be activated to provide support by notifying them of specific next steps to help them feel at ease.

[0587] An example of a prompt message to be input to the generating AI model is: "I have uploaded a picture of a dog. Please check the breed and characteristics of this dog. For users who are unsure, please generate a message that explains the next steps in detail."

[0588] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0589] Step 1:

[0590] The server receives image data from the terminal. The user uses the terminal's camera to take pictures of animals and uploads them to the system. This image data is used as input to identify the animal's species and location.

[0591] Step 2:

[0592] The server processes the acquired image data through analysis tools. Here, image processing technologies such as OpenCV and TensorFlow are used to extract animal features. Specifically, this involves analyzing the shape, pattern, and body type of the animals in the images. The data obtained from this analysis is then used as feature information for the next step.

[0593] Step 3:

[0594] The server uses search tools to compare the animal characteristic information obtained through analysis with a database. The database contains existing animal information, and techniques such as SQL queries are used to search for matching data. The output is either information on a matching animal or a result indicating no match.

[0595] Step 4:

[0596] The server sends the search results to the user via a notification system. This process delivers information to the user's device using email or push notifications. The notification includes detailed information about the discovered animal and the next steps to take.

[0597] Step 5:

[0598] The device collects voice data from the user and sends it to the server. This collected voice data is used as input for emotion recognition.

[0599] Step 6:

[0600] The server analyzes the audio data to understand the user's emotional state. Using IBM Watson or Google Cloud Speech-to-Text, it converts the audio to text and then performs emotional analysis. The output provides information on whether the user is feeling anxiety or joy.

[0601] Step 7:

[0602] The server adjusts notification content based on the emotional information it receives. Using emotional optimization techniques, detailed explanations and reassuring phrases are added. This adjustment allows users to more effectively utilize the information from the system.

[0603] (Application Example 2)

[0604] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0605] When an animal goes missing, there is a need to quickly and effectively locate it and provide appropriate information to those involved. Furthermore, a challenge is to improve the user experience by facilitating smooth communication through notifications that take into account the emotional state of the finder.

[0606] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0607] In this invention, the server includes a processing mechanism for acquiring image information via an input device and analyzing the characteristics of an animal from that image information; an identification means for searching for matching information by comparing it with existing information in an information storage medium based on the analyzed characteristics of the animal; a transmission means for notifying the relevant entity based on the matching result; an emotion analysis mechanism for analyzing voice data and detecting emotional state; and a response adjustment means for adjusting the notification content based on the detected emotional state. This enables the search for animals and the rapid and emotionally sensitive provision of information to the relevant entity.

[0608] "Image information" refers to visual data acquired by an input device to identify the characteristics of an animal.

[0609] An "input device" is a device used to acquire image information, specifically such as a camera or a smartphone.

[0610] A "processing mechanism" is a device or program that has the function of analyzing the characteristics of an animal using acquired image information.

[0611] An "information storage medium" is a database or storage system in which existing animal information is recorded.

[0612] "Identification means" refers to a function that identifies relevant information by comparing the analyzed characteristics of an animal with data on an information storage medium.

[0613] "Transmission means" refers to communication means for notifying the relevant entity of the results obtained by the identification means.

[0614] "Audio data" refers to recordings of voices obtained from relevant parties and is used for sentiment analysis.

[0615] An "emotion analysis mechanism" is a device or program that analyzes audio data and has the function of detecting the emotional state of the relevant subject.

[0616] The "response adjustment mechanism" is a function that optimizes the content of notifications based on the detected emotional state and provides information tailored to the recipient.

[0617] This invention aims to process and quickly identify animal images taken by citizens using their smartphones in an animal search support system. The system first acquires image information from the smartphone and sends it to a server. On the server, it uses computer vision libraries such as OpenCV to analyze the animal's characteristics from the image information. The analyzed characteristics are compared with information storage media in a cloud database such as Firebase to identify the corresponding animal.

[0618] Based on the matching results, notifications are sent to the relevant parties, namely the animal's owner or finder. At this time, voice analysis APIs such as IBM Watson are used to detect emotional states from the voice data, and the notification content is adjusted according to the user's emotional state. For example, a finder feeling anxious will receive a reassuring notification that clearly indicates what to do.

[0619] As a concrete example, imagine a citizen who finds a lost dog in a park, takes a picture of the dog with their smartphone, and uploads it to the system. The server analyzes this image and matches it with a database to send a detailed notification to the dog's owner, such as "The dog is being cared for at a nearby animal hospital." Based on audio data, if the system analyzes sounds indicating anxiety, it will add specific instructions for reuniting the dog to the notification to reassure the owner.

[0620] An example of a prompt message is as follows:

[0621] "Analyze the characteristics of the animal in the photo and find similar registration information. Then, analyze the citizen's sentiment from the following audio data and adjust the notification content. Audio data: 'I found a lost dog, and I'm worried about what to do with it.'"

[0622] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0623] Step 1:

[0624] The user takes a picture of an animal with their smartphone and uploads the image information to the system via the device. The input is image data of the animal, and the output is data to be sent to the server. At this stage, the image data is sent from the input device to the cloud server for preparation for analysis.

[0625] Step 2:

[0626] The server uses a computer vision library (OpenCV) to extract animal features based on the received image information. The input is the image data acquired in step 1, and the output is feature data. This process analyzes the animal's species, pattern, body shape, etc., and generates feature vectors for database searching.

[0627] Step 3:

[0628] The server compares the feature data with the information storage medium in the cloud database (Firebase) to search for matching animal information. The input is the feature data obtained in step 2, and the output is the matching animal information. This matching process compares the image with the registered information in the database to identify the corresponding record.

[0629] Step 4:

[0630] Based on the acquired matching information, the server prepares to send a notification to the relevant entity, namely the animal's owner or discoverer. The input is the matching information acquired in step 3, and the output is the notification data prepared for transmission. At this stage, the content to be sent is generated and the notification method is determined.

[0631] Step 5:

[0632] The server analyzes the audio data received from the terminal and detects the user's emotional state using an audio analysis API (IBM Watson). The input is audio data, and the output is emotional information. In this process, the tone and speed of the voice are analyzed to determine the emotional state.

[0633] Step 6:

[0634] The server adjusts the notification content based on emotional information and sends the notification to the subject in the most optimal form. The input is the emotional information obtained in step 5 and the notification data prepared in step 4, and the output is the final notification data. The notification content is customized according to the user's emotional state, and reassuring information is added.

[0635] Step 7:

[0636] The user receives coordinated notifications through their device to check the animal's location and the next action to take. The input is the final notification data sent from the server, and the output is information that helps the user decide what to do. In this step, the user can review the notification and take the necessary action.

[0637] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0638] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0639] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0640] [Fourth Embodiment]

[0641] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0642] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0643] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0644] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0645] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0646] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0647] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0648] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0649] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0650] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0651] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0652] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0653] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] In the animal search support system according to the present invention, image data acquired from an input device is analyzed in a processing device, and animal characteristics are extracted from the analysis results. The extracted characteristics are compared with existing information in a database on a server to determine possible matches. This process is mainly carried out using image recognition technology and machine learning algorithms. Based on the results identified by the matching means, the server sends a notification to the relevant user, and when the animal is confirmed, the user can provide feedback via a mobile device.

[0655] Furthermore, in the animal adoption matching process, the server analyzes suitability based on animal information entered via the terminal and information on already registered potential adopters. Based on the established criteria, the server selects the most suitable candidate and notifies the corresponding user. This promotes the smooth transfer of animals.

[0656] During operation, users input additional information, such as questions, through their devices. The server then uses a natural language processing unit to analyze the request and retrieve relevant breeding and health information from its knowledge base. This allows users to efficiently obtain the necessary information, supporting the maintenance of healthy lives for their animals.

[0657] As a concrete example, if a user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the system, the server analyzes the image to identify the species and characteristics, and quickly matches it with the database. Based on this result, if the animal is registered, the owner is notified, and the animal can be returned home quickly. In addition, when recruiting foster homes for newly rescued animals, the server can provide necessary information to candidates who match the animal's characteristics and confirm their willingness to adopt. This makes it possible to smoothly and quickly transition animals to new homes.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] Users take photos of lost animals with their mobile devices and upload the image data to the system.

[0661] Step 2:

[0662] The server receives the uploaded image data and automatically starts the image analysis process. Here, computer vision technology is used to extract animal features in the image (e.g., species, color, body shape, pattern, etc.).

[0663] Step 3:

[0664] The server compares the analyzed feature data with an existing database. The matching algorithm searches the database for animal information with similar features and identifies the most likely matches.

[0665] Step 4:

[0666] Based on the matching results, the server sends a notification message to users who appear to be the animal's owner. Users receive this notification on their mobile devices and can check the information needed to reunite with their animal.

[0667] Step 5:

[0668] Users provide feedback on notifications, and if the found animal is correct, the information is fed back to the server. This feedback information is used to improve the platform's performance in the future.

[0669] Step 6:

[0670] When a foster parent matching is needed, users register animal information using their devices. This includes detailed information such as species, temperament, and health status.

[0671] Step 7:

[0672] The server matches registered animal information with existing foster parent candidate information and selects the most suitable foster parent candidate from the database for each animal.

[0673] Step 8:

[0674] The server sends a notification containing details about the animal to the selected foster parent candidates and confirms the adoption process. This notification is delivered to the candidates via their devices.

[0675] Step 9:

[0676] Based on the notification received by the user, the system confirms their intention to adopt the animal. If necessary, the user can query the server with additional questions using natural language processing.

[0677] Step 10:

[0678] The server analyzes user inquiries, extracts relevant breeding information from its knowledge base, and provides users with accurate information.

[0679] This will allow the "AnimalGuardian" system to function smoothly and enable the development of harmonious relationships between animals and humans.

[0680] (Example 1)

[0681] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0682] There is a need for a system that can respond quickly and effectively when animals get lost or when new adoptive homes are needed. However, current methods have challenges, such as the time-consuming process of analyzing and matching animal characteristics and finding adoptive homes, and the difficulty in providing appropriate information and collecting feedback.

[0683] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0684] In this invention, the server includes a data processing unit for acquiring image data using an information acquisition device and analyzing the characteristics of animals from the image data; a data matching unit for searching for matching data by comparing the analyzed animal characteristics with existing data in a storage device; an information notification unit for notifying relevant users based on the matching results; a data analysis unit for analyzing requests using natural language processing technology and searching for relevant information from a knowledge database; and a feedback collection unit for enabling relevant users to quickly provide feedback when an animal is found based on the analysis results. This enables a series of processes such as the rapid identification and return of animals, provision of appropriate information, and smooth recommendation to potential adoptive parents.

[0685] "Image data" refers to the digital representation of visual information collected by an information acquisition device.

[0686] An "information acquisition device" is a device operated by a user to collect image data.

[0687] A "data processing unit" is a device that has processing functions for analyzing specific features from collected image data.

[0688] A "storage device" is a data storage device that stores existing information, such as a database, and uses it for verification purposes.

[0689] A "data matching unit" is a device that compares analyzed feature data with information in a storage device and searches for matching information.

[0690] An "information notification unit" is a device with communication capabilities that transmits information to relevant users based on the matching results.

[0691] A "data analysis unit" is a device that analyzes input question data and retrieves necessary information from a knowledge database.

[0692] A "feedback collection unit" is a device that enables the rapid collection of feedback from users.

[0693] "Natural language processing technology" is a technology that analyzes user input and processes information in a text-based manner.

[0694] A "knowledge database" is a collection of data that structures and stores information, making it searchable as needed.

[0695] This animal search support system is implemented by combining a data processing unit that primarily analyzes image data, a data matching unit that compares it with existing data in a database, an information notification unit that notifies users of relevant information, a data analysis unit that uses natural language processing technology, and a feedback collection unit that collects feedback.

[0696] When a user finds a lost animal, they use a mobile device, which is an information acquisition device, to take a picture of the animal. The device provides the image data by uploading the captured image to a server. At this time, the user can also input supplementary information such as the animal's condition and the location where it was found.

[0697] The server analyzes the received image data using a data processing unit. Specifically, it uses image analysis software, a common tool for image recognition technology, to extract animal features from the images. In this process, machine learning algorithms are used to identify visual features such as species and patterns.

[0698] The extracted features are compared with data stored in the storage device. The server uses a data matching unit to compare this feature data with existing data in the internal database and search for matching information.

[0699] Next, the server promptly notifies the relevant users based on the database matching results. This provides a means of contact for the animal's safe return, if an owner exists.

[0700] This system also handles animals seeking foster homes. Users input data on rescued animals into their terminals and send it to the server. The server analyzes the registered foster parent candidate data and the animal's information to perform appropriate matching.

[0701] If the user has further questions, they can send requests to the server in text format through their terminal. By applying natural language processing technology, the server can retrieve relevant information from a knowledge database through a data analysis unit and provide specific advice and information to the user.

[0702] A concrete example is when a user enters a prompt such as, "Describe the characteristics of this animal and retrieve information that matches the database," and the server then processes the request and provides the information. This entire process aims to make the discovery and protection of animals faster and more efficient.

[0703] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0704] Step 1:

[0705] The user takes a picture of the lost animal using a mobile device and uploads it to the system. The input is the image data of the animal taken by the user and additional information (e.g., location where it was found). The output of this step is the image data sent to the server. The device functions as an information acquisition device and performs initial data collection.

[0706] Step 2:

[0707] The server analyzes the received image data using a data processing unit. The input is the image data obtained in step 1. The server processes the image data using image recognition technology to extract the animal species and characteristics. The output is characteristic data of the identified animals. Machine learning algorithms are used in this process.

[0708] Step 3:

[0709] The server uses feature data to match it with a database in storage. The input is the animal feature data generated in step 2. The server uses a data matching unit to compare the features with existing data and search for matches. The output is the matching results and related information. Here, if matching information for an animal is found, its details are identified.

[0710] Step 4:

[0711] The server notifies the relevant users through the information notification unit based on the matching results. The input is the matching results from step 3. If the owner is found, the server uses their contact information to send a notification to the owner. The output is the notification to the owner and the provision of a means of contact. This notification enables quick reporting.

[0712] Step 5:

[0713] Users input animal rescue data and information on potential adoptive families into a terminal and send it to the server. Input consists of detailed information about the animals. The terminal supplies this data to the server in digital format. Output is the transmission of information to the server.

[0714] Step 6:

[0715] The server processes the input data in its data analysis unit and compares it with foster parent candidate information to perform appropriate matching. The input consists of detailed animal information and foster parent candidate information obtained in step 5. The output is a list of the most suitable foster parent candidates. The server selects candidates based on their suitability and prepares to notify them.

[0716] Step 7:

[0717] If the user has a question, they send it from the terminal to the server as a text prompt. The input is the user's question data. The output of this step is the prompt sent to the server.

[0718] Step 8:

[0719] The server uses a data analysis unit and applies natural language processing technology to analyze the question and retrieve relevant information from the knowledge database. The input is the question data from step 7. The output is the retrieved relevant information. The server then prepares the information requested by the user.

[0720] Step 9:

[0721] The server provides knowledge to the user based on the analysis results. The input is the relevant information obtained in step 8. The output is the information provided to the user. Through this, the server helps to strengthen the user's knowledge and solve problems quickly.

[0722] (Application Example 1)

[0723] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0724] In animal search and adoption programs, there are challenges in quickly and accurately obtaining animal information and providing appropriate notifications and matching. In particular, when using robots to assist pets, improving the accuracy of on-site animal recognition and information provision is essential.

[0725] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0726] In this invention, the server includes a computing device for acquiring data from an input device and analyzing the characteristics of animals from that data; a search means for searching for matching information by comparing it with existing information in an information storage device based on the analyzed characteristics of animals; a notification means for notifying relevant individuals based on the search results; and a means for acquiring the characteristics of animals with a video acquisition device and transmitting them to the computing device in real time. This makes it possible to quickly recognize the characteristics of animals and efficiently provide relevant information.

[0727] "Data" refers to a collection of information acquired by an input device for analyzing the characteristics of an animal.

[0728] An "input device" is a device used to acquire data from an external source and plays a role in transmitting necessary information to the computing device.

[0729] A "calculation unit" is a device that has the ability to perform calculations to analyze acquired data and extract the characteristics of animals.

[0730] An "information storage device" is a device that stores existing information as a database and provides a foundation for comparing it with new data.

[0731] A "search method" refers to a method or device for comparing existing information and data within an information storage device to find matching information.

[0732] "Notification means" refers to a method or device for notifying relevant individuals of the results obtained by the search means.

[0733] An "image acquisition device" is a device such as a camera or sensor used to capture the characteristics of animals, and it acquires that data in real time.

[0734] The system implementing this invention mainly includes the following components. Data is acquired using an image acquisition device, such as a high-resolution camera, and functions as an input device. This captures the characteristics of the animal. The acquired data is then transmitted to a computing device, specifically a computing device such as a Raspberry Pi. The computing device performs characteristic analysis of the animal using image processing software such as OpenCV or TensorFlow.

[0735] The server receives the analyzed data and compares it with the information storage device, which is an SQLite database in the cloud. During this process, a Python program runs as a search tool to find information that matches specific criteria. If the comparison is successful, a notification is sent to the user via their mobile device or other notification device.

[0736] As a concrete example, suppose a pet monitoring robot patrols a park and finds a dog that appears to be lost. The robot captures an image of the dog and immediately sends it to a server. The server analyzes the image and checks its database for similar information. If a match is found, the dog's owner is promptly notified. In this way, the robot can be effectively utilized, enabling rapid problem resolution.

[0737] An example of a prompt message might be, "Identify the animal species and characteristics from this image and verify that it matches the information in the database." In this way, the system sequentially analyzes the characteristics of the target animal and quickly links related information, enabling efficient search support.

[0738] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0739] Step 1:

[0740] The camera mounted on the device captures images of the surroundings and recognizes animals. The input is high-resolution image data acquired by the camera. This data is preprocessed to remove noise and normalize the images. This prepares the data for analysis.

[0741] Step 2:

[0742] The terminal's computing power extracts animal features using the pre-processed image data from the previous step. Image processing using OpenCV is performed here, analyzing key features such as the animal's outline, color, and patterns. The output is presented as a characteristics dataset, which is used in the next matching step.

[0743] Step 3:

[0744] The server receives the characteristic dataset transmitted from the computing unit and compares it with a database in an information storage device located in the cloud. Here, a generative AI model is used to analyze the correlation between the received dataset and existing records in the database. The output is the result of the matching determination with the animal information stored in the database.

[0745] Step 4:

[0746] The server uses search tools based on the matching results to extract relevant information. For example, if the found animal is registered in the database, it extracts the registered owner information. At this stage, the program uses the prompt statement "Identify the animal species and characteristics from this image and verify that it matches the information in the database."

[0747] Step 5:

[0748] The server, through a notification system, notifies the relevant owner or associated individual if the data matches based on the matching results. The notification is sent via a terminal application, and the owner is provided with specific characteristic and location information. The output is a notification message to the user.

[0749] Step 6:

[0750] Users check notifications received via their devices and provide feedback or take action as needed. This user feedback is sent back to the server as information useful for updating the database and improving data accuracy. This interaction through feedback improves the overall accuracy and efficiency of the system.

[0751] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0752] In the animal search support system according to the present invention, image data is acquired by an input device, and the characteristics of the animal are analyzed from the image data by a processing device. This analysis uses computer vision technology to extract characteristics such as the animal's species, pattern, and body shape, and the server searches for matching information by comparing it with existing information in a database. The results identified based on the matching means are notified to the relevant users. This notification is promptly made to the animal's owner or related parties, and the user confirms the information via a terminal.

[0753] Furthermore, this system incorporates an emotion engine that can recognize emotions from the user's voice data. This emotion recognition device analyzes the user's voice tone, speed, and word choice to understand the user's emotional state. Based on the analysis results, the server adjusts the notification content using emotion-responsive means. For example, if the user is in an anxious situation, the system will provide a more detailed and reassuring notification.

[0754] Furthermore, by monitoring the user's emotional state in real time and optimizing the overall system response through emotion optimization methods, the user experience can be improved. This process is also effective as a means of providing emotional support when reuniting with animals, enabling the rapid and appropriate provision of information to support the complex emotions the user may be experiencing.

[0755] For example, when a user finds a lost animal, they take a picture of the animal's characteristics with their device and upload it to the system. The server then performs image analysis and matches it against a database. Simultaneously, if anxiety is detected from the user's voice, the emotion optimization mechanism activates, and the server sends a notification with particularly detailed explanations and clear instructions for the next steps to support the user. In this way, the present invention enables animal search support and optimization of the user experience.

[0756] The following describes the processing flow.

[0757] Step 1:

[0758] A user finds a lost animal, takes a picture of it with their mobile device, and uploads it to the "Animal Search Support System."

[0759] Step 2:

[0760] The server receives the uploaded image data and uses an image analysis engine to extract animal features. These features include species, markings, and body shape.

[0761] Step 3:

[0762] The server compares the extracted feature data with information in existing databases to identify potential matches.

[0763] Step 4:

[0764] Based on the matching results, the server sends a notification to the user who is believed to be the animal's owner. The content of this notification is optimized according to the user's situation by the emotion engine within the server.

[0765] Step 5:

[0766] Users receive notifications on their devices and confirm necessary actions based on the sentiment analysis results. If anxiety is detected, the notification will include additional information to provide reassurance.

[0767] Step 6:

[0768] When a user is reunited with an animal based on a notification, the server acquires the user's voice data and analyzes it with an emotion recognition device to detect the emotions associated with the reunion.

[0769] Step 7:

[0770] Based on the detected emotional state, the server uses emotional support tools to provide the user with appropriate information and determines whether further support is needed.

[0771] Step 8:

[0772] Users register information about their animals, and if adoption matching is needed, they send detailed information to the server via their device.

[0773] Step 9:

[0774] The server analyzes existing foster parent candidate information based on registered animal information and selects the most suitable candidate.

[0775] Step 10:

[0776] The server sends notifications to selected foster parent candidates, confirming details about the animal and the adoption process. This process also utilizes an emotional engine to appropriately adjust the notification content.

[0777] This process allows the entire system, from animal search assistance to foster care matching and emotional support, to operate effectively and humanely.

[0778] (Example 2)

[0779] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] When an animal goes missing, there is a need to quickly and accurately locate its whereabouts and provide information to its owner and other relevant parties. However, conventional methods lack sufficient systems for efficiently analyzing and matching animal characteristics, and furthermore, they do not allow for notifications to be adjusted according to the user's emotions. Therefore, the challenge is to improve the efficiency of animal searches while optimizing the user experience.

[0781] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0782] In this invention, the server includes an analysis means for acquiring image data using a recognition device and analyzing the characteristics of animals from the image data; a search means for searching for matching information by comparing it with existing information in the information resource based on the analyzed characteristics of animals; a notification means for notifying relevant users based on the search results; an emotion response means for recognizing emotions from the user's voice data and adjusting the notification content; and an emotion optimization means for optimizing the overall response based on the results of the emotion recognition. This enables rapid and accurate searching of animals and flexible responses that respond to the user's emotions.

[0783] A "recognition device" is a device that acquires image data using an input device and supplements the information required by the system.

[0784] "Analysis means" refers to technologies and devices that extract animal characteristics from acquired image data to identify species, patterns, body shape, etc.

[0785] A "search method" is a process or device that searches for matching information by comparing the analyzed characteristics of an animal with existing information within an information resource.

[0786] "Notification means" refers to communication methods or means for conveying search results and related information to the person or system that should receive them.

[0787] "Emotional response means" refers to technologies and devices that analyze a user's emotional state based on their voice data and adjust the content of notifications based on the results obtained.

[0788] "Emotional optimization methods" refer to methods and tools for monitoring users' emotional states in real time and optimizing the overall system response.

[0789] This system is designed to effectively support animal searches. The server begins collecting information about animals by first acquiring image data using a recognition device. Common input devices such as smartphones and digital cameras can be used for this purpose. The server processes the acquired image data using analysis tools to extract features such as the animal's species, markings, and body shape. Computer vision technologies such as OpenCV and TensorFlow are used for the analysis.

[0790] The analyzed data is compared with information stored in a database on the server using search tools to find matching information. This process is efficiently carried out using SQL or NoSQL databases. The server then sends the results to relevant individuals or entities using notification methods. Notification methods can include email, SMS, or push notifications.

[0791] In addition, the device collects user voice data and sends it to a server. This data is analyzed using speech recognition technologies such as IBM Watson and Google Cloud Speech-to-Text. As a result, the emotion response system analyzes the user's emotions and adjusts the notification content accordingly. The emotion optimization system monitors the user's real-time emotional state and appropriately adjusts the system's overall response to provide a better user experience.

[0792] As a concrete example, a user could take a picture of a lost dog they see in a park with their smartphone and upload it to the system. This image would be analyzed on the server to find information about the dog. At the same time, if the user is expressing anxiety by phone, an emotion optimization mechanism would be activated to provide support by notifying them of specific next steps to help them feel at ease.

[0793] An example of a prompt message to be input to the generating AI model is: "I have uploaded a picture of a dog. Please check the breed and characteristics of this dog. For users who are unsure, please generate a message that explains the next steps in detail."

[0794] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0795] Step 1:

[0796] The server receives image data from the terminal. The user uses the terminal's camera to take pictures of animals and uploads them to the system. This image data is used as input to identify the animal's species and location.

[0797] Step 2:

[0798] The server processes the acquired image data through analysis tools. Here, image processing technologies such as OpenCV and TensorFlow are used to extract animal features. Specifically, this involves analyzing the shape, pattern, and body type of the animals in the images. The data obtained from this analysis is then used as feature information for the next step.

[0799] Step 3:

[0800] The server uses search tools to compare the animal characteristic information obtained through analysis with a database. The database contains existing animal information, and techniques such as SQL queries are used to search for matching data. The output is either information on a matching animal or a result indicating no match.

[0801] Step 4:

[0802] The server sends the search results to the user via a notification system. This process delivers information to the user's device using email or push notifications. The notification includes detailed information about the discovered animal and the next steps to take.

[0803] Step 5:

[0804] The device collects voice data from the user and sends it to the server. This collected voice data is used as input for emotion recognition.

[0805] Step 6:

[0806] The server analyzes the audio data to understand the user's emotional state. Using IBM Watson or Google Cloud Speech-to-Text, it converts the audio to text and then performs emotional analysis. The output provides information on whether the user is feeling anxiety or joy.

[0807] Step 7:

[0808] The server adjusts notification content based on the emotional information it receives. Using emotional optimization techniques, detailed explanations and reassuring phrases are added. This adjustment allows users to more effectively utilize the information from the system.

[0809] (Application Example 2)

[0810] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0811] When an animal goes missing, there is a need to quickly and effectively locate it and provide appropriate information to those involved. Furthermore, a challenge is to improve the user experience by facilitating smooth communication through notifications that take into account the emotional state of the finder.

[0812] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0813] In this invention, the server includes a processing mechanism for acquiring image information via an input device and analyzing the characteristics of an animal from that image information; an identification means for searching for matching information by comparing it with existing information in an information storage medium based on the analyzed characteristics of the animal; a transmission means for notifying the relevant entity based on the matching result; an emotion analysis mechanism for analyzing voice data and detecting emotional state; and a response adjustment means for adjusting the notification content based on the detected emotional state. This enables the search for animals and the rapid and emotionally sensitive provision of information to the relevant entity.

[0814] "Image information" refers to visual data acquired by an input device to identify the characteristics of an animal.

[0815] An "input device" is a device used to acquire image information, specifically such as a camera or a smartphone.

[0816] A "processing mechanism" is a device or program that has the function of analyzing the characteristics of an animal using acquired image information.

[0817] An "information storage medium" is a database or storage system in which existing animal information is recorded.

[0818] "Identification means" refers to a function that identifies relevant information by comparing the analyzed characteristics of an animal with data on an information storage medium.

[0819] "Transmission means" refers to communication means for notifying the relevant entity of the results obtained by the identification means.

[0820] "Audio data" refers to recordings of voices obtained from relevant parties and is used for sentiment analysis.

[0821] An "emotion analysis mechanism" is a device or program that analyzes audio data and has the function of detecting the emotional state of the relevant subject.

[0822] The "response adjustment mechanism" is a function that optimizes the content of notifications based on the detected emotional state and provides information tailored to the recipient.

[0823] This invention aims to process and quickly identify animal images taken by citizens using their smartphones in an animal search support system. The system first acquires image information from the smartphone and sends it to a server. On the server, it uses computer vision libraries such as OpenCV to analyze the animal's characteristics from the image information. The analyzed characteristics are compared with information storage media in a cloud database such as Firebase to identify the corresponding animal.

[0824] Based on the matching results, notifications are sent to the relevant parties, namely the animal's owner or finder. At this time, voice analysis APIs such as IBM Watson are used to detect emotional states from the voice data, and the notification content is adjusted according to the user's emotional state. For example, a finder feeling anxious will receive a reassuring notification that clearly indicates what to do.

[0825] As a concrete example, imagine a citizen who finds a lost dog in a park, takes a picture of the dog with their smartphone, and uploads it to the system. The server analyzes this image and matches it with a database to send a detailed notification to the dog's owner, such as "The dog is being cared for at a nearby animal hospital." Based on audio data, if the system analyzes sounds indicating anxiety, it will add specific instructions for reuniting the dog to the notification to reassure the owner.

[0826] An example of a prompt message is as follows:

[0827] "Analyze the characteristics of the animal in the photo and find similar registration information. Then, analyze the citizen's sentiment from the following audio data and adjust the notification content. Audio data: 'I found a lost dog, and I'm worried about what to do with it.'"

[0828] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0829] Step 1:

[0830] The user takes a picture of an animal with their smartphone and uploads the image information to the system via the device. The input is image data of the animal, and the output is data to be sent to the server. At this stage, the image data is sent from the input device to the cloud server for preparation for analysis.

[0831] Step 2:

[0832] The server uses a computer vision library (OpenCV) to extract animal features based on the received image information. The input is the image data acquired in step 1, and the output is feature data. This process analyzes the animal's species, pattern, body shape, etc., and generates feature vectors for database searching.

[0833] Step 3:

[0834] The server compares the feature data with the information storage medium in the cloud database (Firebase) to search for matching animal information. The input is the feature data obtained in step 2, and the output is the matching animal information. This matching process compares the image with the registered information in the database to identify the corresponding record.

[0835] Step 4:

[0836] Based on the acquired matching information, the server prepares to send a notification to the relevant entity, namely the animal's owner or discoverer. The input is the matching information acquired in step 3, and the output is the notification data prepared for transmission. At this stage, the content to be sent is generated and the notification method is determined.

[0837] Step 5:

[0838] The server analyzes the audio data received from the terminal and detects the user's emotional state using an audio analysis API (IBM Watson). The input is audio data, and the output is emotional information. In this process, the tone and speed of the voice are analyzed to determine the emotional state.

[0839] Step 6:

[0840] The server adjusts the notification content based on emotional information and sends the notification to the subject in the most optimal form. The input is the emotional information obtained in step 5 and the notification data prepared in step 4, and the output is the final notification data. The notification content is customized according to the user's emotional state, and reassuring information is added.

[0841] Step 7:

[0842] The user receives coordinated notifications through their device to check the animal's location and the next action to take. The input is the final notification data sent from the server, and the output is information that helps the user decide what to do. In this step, the user can review the notification and take the necessary action.

[0843] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0844] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0845] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0846] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0847] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0848] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0849] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0850] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0851] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0852] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0853] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0854] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0855] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0856] 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.

[0857] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0858] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0859] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0860] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0861] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0862] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0863] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0864] The following is further disclosed regarding the embodiments described above.

[0865] (Claim 1)

[0866] A processing device for acquiring image data using an input device and analyzing the characteristics of an animal from that image data,

[0867] A matching means that searches for matching information by comparing it with existing information in a database based on the characteristics of the analyzed animal,

[0868] A notification method for notifying relevant individuals based on the matching results,

[0869] An animal search support system that includes [unclear / unclear].

[0870] (Claim 2)

[0871] An analytical method for determining suitability by comparing registered animal information with information on potential foster parents,

[0872] A notification method to provide matching results to highly suitable foster parent candidates,

[0873] The system according to claim 1, including the following:

[0874] (Claim 3)

[0875] An information extraction means that analyzes input question data using a natural language processing unit and searches for related information from a knowledge base,

[0876] An output device for providing the extracted information to the user,

[0877] The system according to claim 1, including the following:

[0878] "Example 1"

[0879] (Claim 1)

[0880] A data processing unit for acquiring image data using an information acquisition device and analyzing the characteristics of animals from that image data,

[0881] A data matching unit searches for matching data by comparing it with existing data in the storage device based on the characteristics of the analyzed animal,

[0882] An information notification unit that notifies relevant users based on the matching results,

[0883] A data analysis unit analyzes the user's request using natural language processing technology when the user inputs question data, and searches for relevant information from a knowledge database.

[0884] An output device that provides the analyzed information to the user,

[0885] A system that includes this.

[0886] (Claim 2)

[0887] A data analysis unit that compares registered animal data with foster parent candidate data to determine suitability,

[0888] An information notification unit that provides matching results to highly suitable foster parent candidates,

[0889] The system according to claim 1, including the following:

[0890] (Claim 3)

[0891] Based on the analysis results, a feedback collection unit is provided that enables relevant users to quickly provide feedback when they find an animal.

[0892] The system according to claim 1, including the following:

[0893] "Application Example 1"

[0894] (Claim 1)

[0895] A computing device that acquires data using an input device and analyzes the characteristics of animals from that data,

[0896] A search means that searches for matching information by comparing it with existing information in an information storage device based on the characteristics of the analyzed animal,

[0897] A notification means that notifies relevant individuals based on the search results,

[0898] A means including a method for acquiring the characteristics of an animal using an image acquisition device and transmitting them to a computing device in real time,

[0899] A system that includes this.

[0900] (Claim 2)

[0901] An evaluation method for determining suitability by comparing registered animal information with information on potential agents,

[0902] A notification method to provide matching results to highly suitable agent candidates,

[0903] The system according to claim 1, which presents information to the most suitable agent candidate based on added animal characteristic information.

[0904] (Claim 3)

[0905] An information retrieval method that analyzes input query data using natural language processing functions and searches for relevant information from a knowledge database,

[0906] An output device for providing the acquired information to the user,

[0907] The system according to claim 1, comprising means for providing feedback to the user based on characteristics collected by the data transmission means.

[0908] "Example 2 of combining an emotion engine"

[0909] (Claim 1)

[0910] An analytical means for acquiring image data using a recognition device and analyzing the characteristics of animals from that image data,

[0911] A search method that searches for matching information by comparing it with existing information in the information resource based on the characteristics of the analyzed animal,

[0912] A notification mechanism that notifies relevant users based on the search results,

[0913] An emotion-responsive system that recognizes emotions from the user's voice data and adjusts the content of notifications accordingly.

[0914] An emotion optimization means that optimizes the overall response based on the results of the emotion recognition,

[0915] A system that includes this.

[0916] (Claim 2)

[0917] An analytical method for comparing registered animal information with information on potential foster parents to determine suitability,

[0918] A notification method to provide results to highly suitable foster parent candidates,

[0919] The system according to claim 1, including the following:

[0920] (Claim 3)

[0921] An information extraction means that analyzes input question data using a natural language processing unit and extracts relevant information from a knowledge base,

[0922] Output setup to provide the extracted information to users,

[0923] The system according to claim 1, including the following:

[0924] "Application example 2 when combining with an emotional engine"

[0925] (Claim 1)

[0926] A processing mechanism for acquiring image information using an input device and analyzing the characteristics of animals from that image information,

[0927] An identification means that searches for matching information by comparing it with existing information in an information storage medium based on the characteristics of the analyzed animal,

[0928] A means of transmission that notifies the relevant entities based on the matching results,

[0929] An emotion analysis mechanism that analyzes voice data to detect emotional states,

[0930] A response adjustment means that adjusts the notification content based on the detected emotional state,

[0931] A system that includes this.

[0932] (Claim 2)

[0933] An information processing organization that compares registered animal information with information on potential foster parents to determine suitability,

[0934] A means of sending matching results to highly suitable foster parent candidates,

[0935] The system according to claim 1, including the following:

[0936] (Claim 3)

[0937] An information extraction mechanism that analyzes input question data using a natural language processing unit and retrieves relevant information from a knowledge storage medium,

[0938] An output mechanism for providing the extracted information to the subject,

[0939] The system according to claim 1, including the following: [Explanation of Symbols]

[0940] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computing device that acquires data using an input device and analyzes the characteristics of animals from that data, A search means that searches for matching information by comparing it with existing information in an information storage device based on the characteristics of the analyzed animal, A notification means that notifies relevant individuals based on the search results, A means including a method for acquiring the characteristics of an animal using an image acquisition device and transmitting them to a computing device in real time, A system that includes this.

2. An evaluation method for determining suitability by comparing registered animal information with information on potential agents, A notification method to provide matching results to highly suitable agent candidates, The system according to claim 1, which presents information to the most suitable agent candidate based on the added animal characteristics information.

3. An information retrieval method that analyzes input query data using natural language processing functions and searches for relevant information from a knowledge database, An output device for providing the acquired information to the user, The system according to claim 1, comprising means for providing feedback to the user based on characteristics collected by the data transmission means.

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