system

Abstract

Provide a system. A first database containing information on protected animals; a second database containing information representing a desire to own a pet; A means for constructing an AI model that matches protected animals with user desired conditions based on information from the first database and the second database; A means for identifying the most suitable protected animal based on a user's desired criteria using the AI ​​model; A means for providing a user with information about the identified protected animals; A system including:

Description

[Technical Field]

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

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

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

[0004] In recent years, while the number of dogs and cats being taken into public health centers has increased, the number of households wanting to keep pets has also increased, making it difficult to find the pet of your choice. The problem is that the process of matching information about protected animals with the user's desired conditions takes time, resulting in many animals staying in shelters for long periods of time. Furthermore, in order to ensure that protected animals are handed over to appropriate owners, the desired conditions of both parties must match, but this matching process takes a great deal of time and effort. There is a need for a method to solve these issues and efficiently and quickly match protected animals with owners. [Means for solving the problem]

[0005] The present invention provides a system that provides a first database containing information about rescued animals and a second database containing information expressing users' desires for pet ownership. It then uses these databases to build an AI model that matches rescued animals with users' desired conditions. Specifically, information about the characteristics, size, and age of rescued animals provided by rescue facilities is stored in the first database, and information about the characteristics, size, and age desired by users is stored in the second database. The AI ​​model is then used to identify the rescued animal that best meets the user's desired conditions and provide that information to the user, enabling efficient and rapid matching of rescued animals with owners. This reduces the number of rescued animals staying in shelters for long periods of time, reduces the number of unhappy animals, and increases the number of happy homes for rescued animals.

[0006] The "first database" is a database that stores information about protected animals, including data such as characteristics, size, and age provided by the shelter.

[0007] The "second database" is a database that stores information about users who wish to keep a pet, and includes data about the personality, size, and age of the pet the user desires.

[0008] "AI Model" means a mathematical model built using machine learning techniques to match rescued animals with user preferences. Specifically, it uses the Nearest Neighbors algorithm to identify rescued animals that best meet the user's preferences.

[0009] "User's desired conditions" refers to the characteristics, size, age, and other conditions desired by users who want to keep a pet, and this information is stored in a second database.

[0010] "Protected animals" refers to animals such as dogs and cats housed in shelters, and information about such animals is stored in a first database.

[0011] "Matching" refers to the process of comparing information about protected animals stored in a first database with the user's desired conditions stored in a second database to identify the most suitable pair.

[0012] "Information on identified protected animals" is data containing detailed information on protected animals that best meet the user's desired criteria, and this data is provided to the user. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram illustrating a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0035] Specifically, data on information provided by the shelter (e.g., personality, size, age) is stored in a first database, and data on desired conditions provided by users who want to keep a pet (e.g., desired personality, size, age) is stored in a second database.

[0036] The server builds an AI model based on the first and second databases, which uses a Nearest Neighbors algorithm and is designed to identify rescued animals that best meet the user's desired criteria.

[0037] When a user enters their desired conditions, they use their device to send this information to a server. The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model then matches them based on specific characteristics (personality, size, age).

[0038] For example, suppose User A wants a pet that is "gentle, medium-sized, and under three years old." This preference is registered in a second database, and the server uses an AI model to find the most suitable protected animal based on this. If the protected animal identified by the server is ID: 102, the server notifies the user's device of this information.

[0039] Users can then review the details of the identified rescued animals and, after gaining a full understanding, contact the rescue facility, allowing them to quickly and efficiently find the pet that best suits their needs.

[0040] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0041] The processing flow will be explained below.

[0042] Step 1:

[0043] The server reads information about the animals provided by the shelter as the first database. Specifically, it obtains data on their characteristics, size, and age in a format such as a CSV file and stores it in the system.

[0044] Step 2:

[0045] The server reads the desired characteristics, size, and age of the pet provided by the user as a second database, also in the form of a CSV file, and stores the data in the system.

[0046] Step 3:

[0047] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to learn data including features (personality, size, age) and creates a predictive model based on the user's desired conditions.

[0048] Step 4:

[0049] The user inputs their desired conditions on the terminal, for example, selecting and inputting conditions such as "personality: calm," "size: medium," and "age: under 3 years old."

[0050] Step 5:

[0051] The terminal sends the input user's desired conditions to the server, using a communication protocol (e.g., HTTP POST) to send the data.

[0052] Step 6:

[0053] The server receives the user's desired conditions and inputs them into the AI ​​model. Specifically, the desired conditions are passed to the AI ​​model as features, and the model identifies the protected animal that best matches the conditions.

[0054] Step 7:

[0055] The server retrieves the ID of the best protected animal identified by the AI ​​model and notifies the user's device of that information. For example, if the best match is "dog ID: 102," the server transfers the details to the device.

[0056] Step 8:

[0057] The device then displays the received information about the shelter animal to the user, visually presenting the animal's personality, size, age, and other details to the user, allowing the user to decide whether to contact the shelter.

[0058] In this way, the system can efficiently match rescued animals with users who want to keep pets, reducing the number of unhappy animals and increasing the number of happy homes.

[0059] Example 1

[0060] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0061] In modern society, the number of protected animals is increasing, and the needs of owners for these animals are becoming more diverse. Conventional methods have made it difficult to efficiently match protected animals with users' desired conditions, requiring a lot of time and effort. As a result, appropriate protected animals may not be handed over to owners promptly, and the welfare of the animals may decline. To solve this problem, a system that can efficiently match protected animals with users' desired conditions is needed.

[0062] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0063] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to keep a pet, means for constructing an AI model that matches protected animals with the user's desired conditions based on the information in the first and second databases, means for the user to input the desired conditions using a terminal and send the information to the server, means for identifying the most suitable protected animal based on the user's desired conditions using the AI ​​model, and means for notifying the user's terminal of information on the identified protected animal. This enables the user to quickly and efficiently find the protected animal that best suits their desired conditions.

[0064] "Shelter animals" are animals that have been rescued by animal shelters or rescue organizations and are looking for new homes.

[0065] A "database" is a collection of information structured in a particular format that can be easily accessed, managed, and updated.

[0066] An "AI model" is a computational model that uses artificial intelligence algorithms to analyze data and make predictions.

[0067] A "server" is a computer system that provides services to other computers (clients) over a network.

[0068] A "terminal" is a device that a user uses to communicate with a server and input or receive information, examples of which include a PC or smartphone.

[0069] "User" means an individual or organization using the System to search for protected animals.

[0070] "Desired conditions" are the specific characteristics or requirements a user desires in a new pet, including personality, size, age, etc.

[0071] The "Nearest Neighbors Algorithm" is a machine learning algorithm for searching for the nearest data point to a given data point.

[0072] "Matching" is the process of comparing information about protected animals contained in a first database with the user's desired conditions contained in a second database to identify a suitable pair.

[0073] "Notification" refers to the act of the server sending specific information to the user's terminal to inform the user.

[0074] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0075] The server stores information provided by the shelter (e.g., personality, size, age) in a first database, and stores desired conditions provided by the user (e.g., desired personality, size, age) in a second database. A database management system (e.g., MySQL (registered trademark), PostgreSQL) is used to store this data.

[0076] The user inputs their desired conditions using a device (e.g., PC or smartphone). For example, they input a desired condition such as "I'm looking for a calm, medium-sized pet under 3 years old." This information is sent from the device to the server, which then receives it.

[0077] The server builds an AI model based on the data from the first and second databases using the Nearest Neighbors algorithm, specifically using Python's scikit-learn library, to identify the protected animals that best meet the user's desired criteria.

[0078] The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model performs matching based on specific characteristics (personality, size, age). For example, if User A requests a pet that is "gentle, medium-sized, and under three years old," this desired condition is registered in the second database. The server uses the AI ​​model to find the most suitable rescue animal based on these conditions. If the rescue animal identified by the server is ID: 102, the server notifies User A's device of this information.

[0079] Users can then use their devices to view detailed information about the identified rescued animal, such as its personality, size, and age, and contact the rescue facility after gaining a thorough understanding of the animal. This allows users to quickly and efficiently find the pet that best suits their needs.

[0080] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0081] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0082] Step 1: Store protected animal information in the first database

[0083] The server collects information about protected animals provided by the shelters. The input is data such as personality, size, and age provided by the shelters. Specifically, the server reads data uploaded to the server in formats such as CSV files using the Python pandas library.

[0084] The server stores the read data in a first database. The output is information about protected animals neatly stored in the first database. A database management system such as MySQL is used.

[0085] Step 2: Store the user's preferences in a second database

[0086] The user uses the terminal to input desired conditions (e.g., personality, size, age). The input is data received through the user interface. Specifically, the user enters the desired conditions into a form on the web browser and presses the "Submit" button.

[0087] The terminal sends the entered desired conditions to the server, and the data is transferred to the server as an HTTP request.

[0088] The server stores the received desired conditions in a second database, and the output is information on the desired conditions stored in an orderly manner in the second database.

[0089] Step 3: Building an AI model

[0090] The server builds an AI model based on data from the first and second databases. The input is information about protected animals and the user's desired conditions. Specifically, the model is built using the Nearest Neighbors algorithm using Python's scikit-learn library. Data preprocessing, such as standardization and normalization, is also performed.

[0091] The server stores the constructed AI model, and the output is an AI model optimized for matching rescued animals with the user's desired conditions.

[0092] Step 4: Matching your desired conditions

[0093] The server inputs the user's desired conditions into the AI ​​model and identifies the most suitable animal for protection. The input is the user's desired conditions stored in a second database. Specifically, it uses the Python scikit-learn library to search for the most suitable animal for protection using the Nearest Neighbors algorithm.

[0094] The server obtains information about the identified protected animals, and outputs the IDs of the identified protected animals and their details.

[0095] Step 5: Notification of match results

[0096] The server notifies the user's device of the information about the identified protected animal. The input is detailed information about the identified protected animal. Specifically, the server generates a JSON response and sends it to the user's device as an HTTP response.

[0097] The terminal displays the received information, and the output is detailed information about the protected animals displayed in a format that can be viewed by the user.

[0098] Step 6: Confirm details and contact

[0099] The user uses the terminal to check the detailed information of the identified protected animal, and the input is the protected animal information sent from the server.

[0100] The user can contact the shelter based on the details. The output is that the user can contact the shelter and take specific action to adopt a new pet by clicking on a link on the device to view the shelter's contact information and contact them.

[0101] Through this process, users can efficiently find the rescued animal that is best suited to them, and the rescued animal can be quickly handed over to the right owner.

[0102] (Application example 1)

[0103] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0104] In modern society, matching users who want to keep protected animals with protected animals is not done efficiently. This results in long times until protected animals are handed over to suitable owners, which compromises the welfare of the animals, and makes it difficult for users who want to keep pets to find the right animal. In addition, handling requests in physical stores takes time and effort, so there is a need for more efficient matching.

[0105] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0106] In this invention, the server includes: means for constructing an AI model that matches protected animals with users' desired conditions based on information from a first database containing information about protected animals and a second database containing information expressing the desire to own a pet; means for the user to input their desired conditions using a smartphone application and identify the most suitable protected animal based on the AI ​​model; and means for providing detailed information about the identified protected animal to the user's smartphone. This allows users to input their desired conditions using their smartphone before visiting a physical store and to check in advance information about the most suitable protected animal. This allows protected animals to be quickly transferred to appropriate owners, improving the welfare of the animals and improving user convenience.

[0107] The "first database" refers to a database that stores information about the characteristics, size, and age of protected animals.

[0108] The "second database" is a database that stores information about the personality, size, and age of a pet desired by a user who wants to keep a pet.

[0109] The "AI model" is an artificial intelligence that matches protected animals with the user's desired conditions based on information from the first and second databases.

[0110] The "smartphone application" is application software that allows users to input their desired criteria and use an AI model to identify the most suitable protected animals.

[0111] "Detailed information on identified protected animals" refers to specific information on protected animals that the AI ​​model matches based on the user's desired conditions, and is provided through a smartphone application.

[0112] The system for implementing this invention is designed to efficiently match rescued animals with the requirements of users who want to keep pets.

[0113] The system includes the following main elements:

[0114] 1. First database: stores information about the characteristics, size, and age of the protected animals.

[0115] 2. A second database: stores information about the personality, size, and age of the user's desired pet.

[0116] 3. AI model: Matches protected animals with the user's desired conditions based on information from the first and second databases.

[0117] Users use a smartphone application to input their desired conditions. The detailed flow is explained below.

[0118] Explaining the program's processing in natural language

[0119] 1. Data Entry

[0120] Users launch a smartphone application and enter information about the pet they want, such as its personality, size, and age.

[0121] 2. Data Transmission

[0122] The smartphone application sends the desired conditions entered by the user to the server in JSON format using an HTTP POST request.

[0123] 3. Matching process

[0124] The server receives the request data containing the user's desired conditions and performs matching using an AI model based on the information from the first and second databases, which uses the Nearest Neighbors algorithm.

[0125] 4. Obtaining the results

[0126] The server uses an AI model to identify the most suitable protected animal and obtains its details.

[0127] 5. Data provision

[0128] The server provides detailed information about identified protected animals to the user's smartphone in real time.

[0129] Implementation example

[0130] For example, if a user wants a pet that is "gentle, medium-sized, and under three years old," they can enter these requirements into a smartphone application. The application then sends these requirements to a server, which uses an AI model to identify the most suitable rescue animal. Details of the identified rescue animal are then sent to the user's smartphone, allowing them to review the information before visiting a physical store.

[0131] Prompt Sentence Examples

[0132] "Based on data from a first database containing information on the characteristics, size, and age of rescued animals, and a second database containing the desired conditions of users looking to adopt a pet, please find the rescued animal that best meets the user's desired conditions. For example, if user A wants a pet that is 'gentle, medium-sized, and under 3 years old,' please identify the best rescued animal."

[0133] This system allows users to check information about the most suitable rescued animals before visiting a physical store, enabling efficient matching. The server uses a database and AI models to match the user's preferences with the characteristics of the rescued animals and find the optimal match.

[0134] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0135] Step 1:

[0136] Users launch a smartphone application and enter the desired pet's characteristics, size, age, and other criteria.

[0137] Input: Desired conditions entered by the user (personality, size, age)

[0138] Output: JSON format of input data

[0139] Specific operation: The user enters desired conditions into the input form of the smartphone application and presses the submit button. The application converts this data into JSON format.

[0140] Step 2:

[0141] The terminal transmits the input desired condition data to the server.

[0142] Input: User preferences in JSON format

[0143] Output: HTTP POST request to the server

[0144] Specific operation: The smartphone application sends JSON data containing the user's desired conditions to the server as an HTTP POST request.

[0145] Step 3:

[0146] The server analyzes the received desired condition data and stores it in a second database.

[0147] Input: JSON format data received by the server

[0148] Output: User preferences stored in a second database

[0149] Specific operation: The server receives the HTTP POST request, parses the data, and stores it in a second database.

[0150] Step 4:

[0151] The server uses an AI model based on the information from the first and second databases to identify the most suitable animals for protection.

[0152] Input: Protected animal information from the first database, user preferences from the second database

[0153] Output: Details of the best protected animals

[0154] How it works: The server uses an AI model (Nearest Neighbors algorithm) to match the protected animals that best meet the user's desired criteria.

[0155] Step 5:

[0156] The server obtains detailed information about the identified protected animals and provides it to the user's smartphone.

[0157] Input: Details of the best rescue animal

[0158] Output: Detailed information about protected animals sent to your smartphone

[0159] Specific operation: The server obtains detailed information about the identified protected animals and sends the data to the user's smartphone. The user's smartphone application receives a notification and displays the information.

[0160] Step 6:

[0161] Users can check detailed information on their smartphone application and prepare to visit the physical store.

[0162] Input: Details of the protected animal sent from the server

[0163] Output: Information on identified rescue animals and preparation for store visits

[0164] Specific actions: The user opens the smartphone application, checks the notification, views the details, and prepares to visit the physical store based on the information they view.

[0165] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0166] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system combines an emotion engine that recognizes the user's emotions to correct the user's desired conditions and make it possible to match more optimal rescued animals.

[0167] The system consists of a first database that stores information about protected animals provided by shelters, a second database that stores the desired conditions of users who want to keep pets, and an AI model that matches protected animals with the desired conditions of users based on these databases.In addition, by incorporating an emotion engine, the system can analyze the user's emotions and adjust the user's desired conditions based on the results.

[0168] Specifically, the server stores information about the characteristics, size, and age of rescued animals in a first database. For example, it reads information provided by rescue facilities in CSV file format and saves it in the database. A second database stores information about the characteristics, size, and age of pets desired by users.

[0169] The user uses the terminal to input desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," and then submits the input. The terminal then sends this information to the server.

[0170] The server builds an AI model based on the first and second databases, using the Nearest Neighbors algorithm to predict the best animal to rescue based on the user's desired conditions.

[0171] Next, the emotion engine analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page). As the user browses pet information, the emotion engine monitors their emotional state in real time and automatically adjusts their preferences accordingly if they become excited or anxious.

[0172] For example, if User A inputs "calm, medium-sized, under 3 years old" as their desired criteria, and the emotion engine detects that the user is particularly excited while browsing the information, the server will fine-tune the desired criteria based on this information and re-identify the most suitable protected animal. The server will then obtain the ID of the re-identified protected animal and notify the information to the user's device.

[0173] The device receives detailed information about the sheltered animal from the server and displays it to the user, allowing the user to decide whether to contact the shelter.

[0174] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion engine, matching can be done with consideration for the user's feelings.

[0175] The processing flow will be explained below.

[0176] Step 1:

[0177] The server loads information about the animals provided by the shelter as a first database. Specifically, it loads data about their personalities, sizes, and ages in CSV file format and stores the data in the database.

[0178] Step 2:

[0179] The server reads the desired pet's characteristics, size, and age, which are provided by the user as a second database, also in CSV file format, and stores the data in the database.

[0180] Step 3:

[0181] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to generate a predictive model using features based on the protected animals and the user's desired conditions.

[0182] Step 4:

[0183] Users input their desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," using a web browser or a dedicated application.

[0184] Step 5:

[0185] The terminal sends the input user's desired conditions to the server, and transfers the data to the server using a communication protocol (e.g., HTTP POST).

[0186] Step 6:

[0187] The server receives the user's desired conditions, inputs them into the AI ​​model, and identifies the most suitable protected animal. The AI ​​model then obtains the ID of the protected animal and temporarily stores its details.

[0188] Step 7:

[0189] The server runs an emotion engine that recognizes users' emotions in real time. The emotion engine collects data on users' operations and behavior while they browse web pages and applications, and estimates their emotional state. For example, it analyzes emotions from page viewing time, click frequency, and viewed content.

[0190] Step 8:

[0191] The emotion engine analyzes the user's emotional state and adjusts the user's preferences based on the analysis results. If the user is excited, it automatically adjusts the desired pet's personality, size, and other conditions. At the same time, the server re-inputs the adjusted preferences into the AI ​​model to re-identify the most suitable rescue animal.

[0192] Step 9:

[0193] The server notifies the user's device of the ID and detailed information of the re-identified protected animal based on the desired conditions corrected by the emotion engine.

[0194] Step 10:

[0195] The device then displays the received information about the shelter animal to the user, visually presenting the animal's characteristics, size, age, and other details to the user, allowing the user to decide whether to contact the shelter based on this information.

[0196] In this way, the system efficiently matches rescued animals with users who want to keep pets, and by using an emotion engine, it can achieve optimal matching that takes into account the emotional state of the user, thereby reducing the number of unhappy animals and increasing the number of happy homes.

[0197] Example 2

[0198] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0199] Conventional systems for matching rescued animals with users do not take into account the user's emotional state, making it difficult to recommend the most suitable rescued animal based on the user's true preferences. Furthermore, because the user's preferences are fixed, a variety of options are not offered, resulting in a decrease in the chances of a rescued animal being handed over to a suitable owner.

[0200] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0201] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to have a pet, means for constructing an AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, means including an emotion analysis engine that analyzes the user's emotional state based on the user's input method and browsing behavior, means for correcting the user's desired conditions based on the results of the emotion analysis engine, means for using the AI ​​model to identify the most suitable protected animal based on the corrected desired conditions, and means for providing the user with information on the identified protected animal. This enables flexible and optimal matching that takes the user's emotional state into consideration.

[0202] definition statement

[0203] The "first database" is a database that stores information about protected animals.

[0204] The "second database" is a database that stores information about the desired conditions of users who want to keep a pet.

[0205] The "artificial intelligence model" is a learning system that matches protected animals with the user's desired conditions based on information from the first database and the second database.

[0206] An "emotion analysis engine" is a system that analyzes a user's emotional state based on their input method and browsing behavior.

[0207] The "means for correcting the user's desired conditions" is a method for adjusting the desired conditions input by the user based on the results of the emotion analysis engine.

[0208] The "means for identifying the most suitable protected animal" is a method that uses an artificial intelligence model to select a protected animal that is suitable for the user based on the adjusted desired conditions.

[0209] "Means for providing information about protected animals" refers to a method for conveying detailed information about identified protected animals to users.

[0210] MODE FOR CARRYING OUT THE INVENTION

[0211] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system is combined with an emotion analysis engine that recognizes the user's emotions, allowing it to correct the user's desired conditions and match them with more optimal rescued animals.

[0212] System Configuration

[0213] The system consists of the following elements:

[0214] 1. Database 1 (Protected Animal Information):

[0215] The first database contains information about the characteristics, size, and age of the animals in the shelter. Using the information provided by the shelter, a CSV file is read using the Python pandas library and stored in the database. For example, information such as "character: gentle," "size: small," and "age: 2 years old" is saved.

[0216] 2. Secondary database (user preference):

[0217] The second database contains information about the pet's personality, size, and age, which users input via their smartphone or computer. This information is also stored in the database and managed for each user.

[0218] 3. Artificial Intelligence Model:

[0219] Based on the information in the first and second databases, an artificial intelligence model is constructed to match rescued animals with the user's desired conditions. This model uses the Nearest Neighbors algorithm from the scikit-learn library. For example, it predicts the best rescued animals for the user's desired conditions.

[0220] 4. Sentiment Analysis Engine:

[0221] The emotion analysis engine analyzes a user's emotional state based on their input method and browsing behavior. This engine uses Microsoft's Azure Emotion API. For example, it monitors the user's emotional state in real time when viewing information about a specific protected animal, and obtains emotional information.

[0222] 5. How to adjust user preferences:

[0223] It includes a means for adjusting a user's preferences based on the results of the sentiment analysis engine. For example, if a user enters a preference of "calm, medium-sized, under 3 years old" but shows great excitement while viewing information about a particular pet, the preference will be adjusted to "small."

[0224] 6. How to identify the best shelter animals:

[0225] An artificial intelligence model is used to identify the best rescue animals based on the adjusted preferences, and then rematch based on preferences that reflect the user's emotional state.

[0226] 7. Means of providing information about protected animals:

[0227] Information about the identified rescued animals is provided to the user and displayed on the device, allowing the user to decide whether or not to contact the rescue facility.

[0228] Specific examples

[0229] For example, consider a situation where a user enters the following criteria into a smartphone app:

[0230] "I'm looking for a calm, medium-sized pet under the age of 3."

[0231] The system stores the user's desired criteria in a second database and uses an artificial intelligence model to identify the most suitable rescue animal. As the user browses information about a specific pet, an emotional analysis engine monitors the user's emotional state. If the user is overly agitated, the system reevaluates the criteria and performs a new match based on the revised criteria. As a result, the most suitable rescue animal information is re-identified and displayed on the user's device. The user can then contact the shelter using the provided information. This process streamlines the user's selection of the pet that is best for them.

[0232] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion analysis engine, matching can be done while taking into consideration the feelings of the user.

[0233] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0234] Program processing flow

[0235] Step 1: Store data in the first database

[0236] The server receives information about protected animals provided by the shelter in the form of a CSV file. It reads the received CSV file using the Python pandas library and stores the data in the first database. The input is the CSV file, and the output is the information about protected animals stored in the database. The specific operations include reading the CSV file, cleansing the data, and inserting it into the database.

[0237] Step 2: Store user preferences in a second database

[0238] The user uses a terminal to input the desired pet's conditions. For example, they input specific prompts such as "personality: gentle," "size: medium," and "age: under 3 years old." This input information is sent from the terminal to the server, which stores it in a second database. The input is the user's desired conditions, and the output is the user's desired conditions stored in the database. Specific operations include sending the input form, receiving the data, and inserting it into the database.

[0239] Step 3: Building an artificial intelligence model

[0240] The server retrieves data from the first and second databases and uses this data to build an AI model that matches protected animals with the user's desired conditions. In this example, the Nearest Neighbors algorithm from the scikit-learn library is used. The input is the data retrieved from the first and second databases, and the output is a trained AI model. Specific operations include data standardization, model training, and model saving.

[0241] Step 4: Identifying the best rescue animals

[0242] The server uses the constructed artificial intelligence model to identify the most suitable animal for rescue based on the user's desired conditions. The input is the user's desired conditions, and the output is information on the most suitable animal for rescue. Specific operations include prediction by the model and filtering of the results.

[0243] Step 5: Applying the sentiment analysis engine

[0244] When a user is browsing information about protected animals on their device, the server uses an emotion analysis engine to analyze the user's emotional state. This analysis is performed using Microsoft's Azure Emotion API. The input is the user's browsing behavior data, and the output is the user's emotional state. Specific operations include collecting the user's input method and browsing behavior data, calling the emotion analysis API, and obtaining emotional information.

[0245] Step 6: Adjust your preferences

[0246] The server corrects the user's desired conditions based on the results of the emotion analysis engine. For example, if the user inputs the desired conditions of "calm, medium size, under 3 years old," but becomes particularly excited while browsing, the desired conditions will be adjusted to "small." The input is the result of the emotion analysis, and the output is the corrected desired conditions. Specific operations include analyzing the emotion data and updating the desired conditions.

[0247] Step 7: Re-identify the best animals to protect

[0248] Based on the revised preferences, the server again uses the AI ​​model to identify the most suitable animal for protection. The input is the revised preferences, and the output is the updated information on the most suitable animal for protection. Specifically, the model again predicts and filters the results.

[0249] Step 8: Inform users

[0250] The server transmits detailed information about the identified protected animal to the user's terminal. The terminal displays the received information to the user. The input is the information about the re-identified protected animal, and the output is the details of the protected animal displayed on the terminal. Specific operations include transmitting and displaying the information.

[0251] (Application example 2)

[0252] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0253] Conventional matching systems for rescued animals and users looking to adopt pets simply search for a partner based on desired criteria, without taking into account the user's feelings, which often resulted in an inadequate match. Furthermore, users' true desires can change based on their emotions, so it's necessary to adjust the desired criteria. Furthermore, the system lacks functionality for providing users with detailed information about pets, making the process for users to view or inquire complicated.

[0254] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0255] In this invention, the server includes a first database containing information about protected animals, a second database containing information expressing the desire to own a pet, a means for constructing a generative AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, a means equipped with an emotion engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis, a means for identifying the most suitable protected animal based on the user's desired conditions using the generative AI model and the emotion engine, and a means for providing the user with information about the identified protected animal. This allows the desired conditions to be adjusted to reflect the user's emotions, resulting in more accurate matching. It also allows users to smoothly make inquiries and make reservations for tours.

[0256] The "first database" is a database for storing information on protected animals.

[0257] The "second database" is a database for storing the desired conditions of users who want to keep a pet.

[0258] A "generative AI model" is an artificial intelligence model that makes optimal matches based on the user's desired conditions and information about protected animals.

[0259] The "emotion engine" is an engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis results.

[0260] "Matching means" means a means that provides the functionality to identify the most suitable protected animal based on the user's desired criteria using a generative AI model.

[0261] "Means for providing" refers to means for providing the function of delivering information about identified protected animals to users.

[0262] To implement this invention, a system is constructed that uses a server, a user terminal, a first database, a second database, a generative AI model, and an emotion engine. Specific embodiments are described below.

[0263] First, the server has a first database containing information about rescued animals and a second database containing the desired conditions of users who want to keep a pet. The first database stores detailed information about rescued animals, such as their personality, size, and age. The second database stores information about the personality, size, and age of the pet desired by the user.

[0264] The server then uses this information to build a generative AI model that uses a Nearest Neighbors algorithm to match users' preferences with information about rescued animals.

[0265] The server also has an emotion engine that analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page, facial expression analysis, voice analysis, etc.). This emotion engine monitors the user's emotions in real time while they are browsing information about protected animals, and if the user becomes excited or anxious, it adjusts their desired conditions accordingly.

[0266] For example, if User A desires a "gentle, medium-sized, under 3-year-old" pet and shows particular excitement while browsing the information, the emotion engine will use this information to appropriately fine-tune the desired conditions. The server will then use the generative AI model again to identify the most suitable rescue animal based on these adjusted conditions. Information about the identified rescue animal is sent to the user's device, where detailed information, photos, and videos are displayed. Using this information, users can directly contact the shelter or make a reservation for a tour.

[0267] For example, if a user inputs the desired criteria of "gentle, medium-sized, under 3 years old" and the emotion engine predicts that it is "fun," the generative AI model will suggest the most suitable rescue animal based on these desired criteria. An example of a prompt is as follows:

[0268] "The user's desired conditions were entered as 'personality: gentle, medium size, age: under 3', and the user's emotion was estimated to be 'fun'. Please recommend the most suitable rescue animal based on these conditions."

[0269] This system allows users to adjust their desired conditions to reflect their feelings, resulting in more accurate matching. It also allows users to smoothly make inquiries and reservations for viewings, increasing the chances that rescued animals will be quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes.

[0270] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0271] Step 1:

[0272] The user inputs desired conditions for the pet into the terminal, such as "personality, size, age," and presses the send button on the terminal.

[0273] Input: Personality: Gentle, Size: Medium, Age: Under 3 years old

[0274] Output: Desired condition data sent from the terminal to the server

[0275] Step 2:

[0276] The server receives the desired condition data transmitted from the terminal and stores the received data in a second database.

[0277] Input: Desired condition data sent from the terminal

[0278] Output: User preference data stored in a second database

[0279] Step 3:

[0280] The server retrieves information about the shelter animals from the first database, including the characteristics, size, age, etc. of the shelter animals.

[0281] Input: First database

[0282] Output: Protected animal information

[0283] Step 4:

[0284] The server reads the user's desired conditions from the second database and builds a generative AI model based on the protected animal information obtained from the first database, using the Nearest Neighbors algorithm to identify the most suitable protected animals.

[0285] Input: User's desired conditions, protected animal information from the first database

[0286] Output: Generated AI model, identifying the best animals to protect

[0287] Step 5:

[0288] The server uses an emotion engine to analyze the user's emotional state, inferring their emotions based on their input method and browsing behavior, and adjusts their desired conditions based on the results.

[0289] Input: User input methods and browsing behavior data

[0290] Output: Corrected user preferences

[0291] Step 6:

[0292] Based on the desired conditions corrected by the emotion engine, the server again uses the generative AI model to identify the most suitable protected animal. The protected animal is identified using a proximity search algorithm.

[0293] Input: Corrected user preferences

[0294] Output: Identification of the best animals to protect

[0295] Step 7:

[0296] The server sends detailed information about the identified protected animal to the user's device, including the animal's characteristics, size, age, photos, videos, etc.

[0297] Input: Information about the identified protected animal

[0298] Output: Details of the protected animal sent to the user's device

[0299] Step 8:

[0300] The user can view the detailed information about the protected animals received on their device, and after checking the information, make inquiries to the shelter or make reservations for a tour.

[0301] Input:Detailed information about protected animals

[0302] Output: Availability of inquiries and tour reservations

[0303] In this way, a system will be created that optimally matches protected animals with users, allowing users to smoothly make inquiries and make reservations for tours.

[0304] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0305] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0306] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0307] [Second embodiment]

[0308] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0309] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0310] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0311] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0312] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0313] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0314] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0315] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0316] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0317] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0318] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0319] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0320] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0321] Specifically, data on information provided by the shelter (e.g., personality, size, age) is stored in a first database, and data on desired conditions provided by users who want to keep a pet (e.g., desired personality, size, age) is stored in a second database.

[0322] The server builds an AI model based on the first and second databases, which uses a Nearest Neighbors algorithm and is designed to identify rescued animals that best meet the user's desired criteria.

[0323] When a user enters their desired conditions, they use their device to send this information to a server. The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model then matches them based on specific characteristics (personality, size, age).

[0324] For example, suppose User A wants a pet that is "gentle, medium-sized, and under three years old." This preference is registered in a second database, and the server uses an AI model to find the most suitable protected animal based on this. If the protected animal identified by the server is ID: 102, the server notifies the user's device of this information.

[0325] Users can then review the details of the identified rescued animals and, after gaining a full understanding, contact the rescue facility, allowing them to quickly and efficiently find the pet that best suits their needs.

[0326] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0327] The processing flow will be explained below.

[0328] Step 1:

[0329] The server reads information about the animals provided by the shelter as the first database. Specifically, it obtains data on their characteristics, size, and age in a format such as a CSV file and stores it in the system.

[0330] Step 2:

[0331] The server reads the desired characteristics, size, and age of the pet provided by the user as a second database, also in the form of a CSV file, and stores the data in the system.

[0332] Step 3:

[0333] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to learn data including features (personality, size, age) and creates a predictive model based on the user's desired conditions.

[0334] Step 4:

[0335] The user inputs their desired conditions on the terminal, for example, selecting and inputting conditions such as "personality: calm," "size: medium," and "age: under 3 years old."

[0336] Step 5:

[0337] The terminal sends the input user's desired conditions to the server, using a communication protocol (e.g., HTTP POST) to send the data.

[0338] Step 6:

[0339] The server receives the user's desired conditions and inputs them into the AI ​​model. Specifically, the desired conditions are passed to the AI ​​model as features, and the model identifies the protected animal that best matches the conditions.

[0340] Step 7:

[0341] The server retrieves the ID of the best protected animal identified by the AI ​​model and notifies the user's device of that information. For example, if the best match is "dog ID: 102," the server transfers the details to the device.

[0342] Step 8:

[0343] The device then displays the received information about the shelter animal to the user, visually presenting the animal's personality, size, age, and other details to the user, allowing the user to decide whether to contact the shelter.

[0344] In this way, the system can efficiently match rescued animals with users who want to keep pets, reducing the number of unhappy animals and increasing the number of happy homes.

[0345] Example 1

[0346] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0347] In modern society, the number of protected animals is increasing, and the needs of owners for these animals are becoming more diverse. Conventional methods have made it difficult to efficiently match protected animals with users' desired conditions, requiring a lot of time and effort. As a result, appropriate protected animals may not be handed over to owners promptly, and the welfare of the animals may decline. To solve this problem, a system that can efficiently match protected animals with users' desired conditions is needed.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0349] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to keep a pet, means for constructing an AI model that matches protected animals with the user's desired conditions based on the information in the first and second databases, means for the user to input the desired conditions using a terminal and send the information to the server, means for identifying the most suitable protected animal based on the user's desired conditions using the AI ​​model, and means for notifying the user's terminal of information on the identified protected animal. This enables the user to quickly and efficiently find the protected animal that best suits their desired conditions.

[0350] "Shelter animals" are animals that have been rescued by animal shelters or rescue organizations and are looking for new homes.

[0351] A "database" is a collection of information structured in a particular format that can be easily accessed, managed, and updated.

[0352] An "AI model" is a computational model that uses artificial intelligence algorithms to analyze data and make predictions.

[0353] A "server" is a computer system that provides services to other computers (clients) over a network.

[0354] A "terminal" is a device that a user uses to communicate with a server and input or receive information, examples of which include a PC or smartphone.

[0355] "User" means an individual or organization using the System to search for protected animals.

[0356] "Desired conditions" are the specific characteristics or requirements a user desires in a new pet, including personality, size, age, etc.

[0357] The "Nearest Neighbors Algorithm" is a machine learning algorithm for searching for the nearest data point to a given data point.

[0358] "Matching" is the process of comparing information about protected animals contained in a first database with the user's desired conditions contained in a second database to identify a suitable pair.

[0359] "Notification" refers to the act of the server sending specific information to the user's terminal to inform the user.

[0360] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0361] The server stores information provided by the shelter (e.g., personality, size, age) in a first database, and stores desired conditions provided by the user (e.g., desired personality, size, age) in a second database. A database management system (e.g., MySQL, PostgreSQL) is used to store this data.

[0362] The user inputs their desired conditions using a device (e.g., PC or smartphone). For example, they input a desired condition such as "I'm looking for a calm, medium-sized pet under 3 years old." This information is sent from the device to the server, which then receives it.

[0363] The server builds an AI model based on the data from the first and second databases using the Nearest Neighbors algorithm, specifically using Python's scikit-learn library, to identify the protected animals that best meet the user's desired criteria.

[0364] The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model performs matching based on specific characteristics (personality, size, age). For example, if User A requests a pet that is "gentle, medium-sized, and under three years old," this desired condition is registered in the second database. The server uses the AI ​​model to find the most suitable rescue animal based on these conditions. If the rescue animal identified by the server is ID: 102, the server notifies User A's device of this information.

[0365] Users can then use their devices to view detailed information about the identified rescued animal, such as its personality, size, and age, and contact the rescue facility after gaining a thorough understanding of the animal. This allows users to quickly and efficiently find the pet that best suits their needs.

[0366] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0367] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0368] Step 1: Store protected animal information in the first database

[0369] The server collects information about protected animals provided by the shelters. The input is data such as personality, size, and age provided by the shelters. Specifically, the server reads data uploaded to the server in formats such as CSV files using the Python pandas library.

[0370] The server stores the read data in a first database. The output is information about protected animals neatly stored in the first database. A database management system such as MySQL is used.

[0371] Step 2: Store the user's preferences in a second database

[0372] The user uses the terminal to input desired conditions (e.g., personality, size, age). The input is data received through the user interface. Specifically, the user enters the desired conditions into a form on the web browser and presses the "Submit" button.

[0373] The terminal sends the entered desired conditions to the server, and the data is transferred to the server as an HTTP request.

[0374] The server stores the received desired conditions in a second database, and the output is information on the desired conditions stored in an orderly manner in the second database.

[0375] Step 3: Building an AI model

[0376] The server builds an AI model based on data from the first and second databases. The input is information about protected animals and the user's desired conditions. Specifically, the model is built using the Nearest Neighbors algorithm using Python's scikit-learn library. Data preprocessing, such as standardization and normalization, is also performed.

[0377] The server stores the constructed AI model, and the output is an AI model optimized for matching rescued animals with the user's desired conditions.

[0378] Step 4: Matching your desired conditions

[0379] The server inputs the user's desired conditions into the AI ​​model and identifies the most suitable animal for protection. The input is the user's desired conditions stored in a second database. Specifically, it uses the Python scikit-learn library to search for the most suitable animal for protection using the Nearest Neighbors algorithm.

[0380] The server obtains information about the identified protected animals, and outputs the IDs of the identified protected animals and their details.

[0381] Step 5: Notification of match results

[0382] The server notifies the user's device of the information about the identified protected animal. The input is detailed information about the identified protected animal. Specifically, the server generates a JSON response and sends it to the user's device as an HTTP response.

[0383] The terminal displays the received information, and the output is detailed information about the protected animals displayed in a format that can be viewed by the user.

[0384] Step 6: Confirm details and contact

[0385] The user uses the terminal to check the detailed information of the identified protected animal, and the input is the protected animal information sent from the server.

[0386] The user can contact the shelter based on the details. The output is that the user can contact the shelter and take specific action to adopt a new pet by clicking on a link on the device to view the shelter's contact information and contact them.

[0387] Through this process, users can efficiently find the rescued animal that is best suited to them, and the rescued animal can be quickly handed over to the right owner.

[0388] (Application example 1)

[0389] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0390] In modern society, matching users who want to keep protected animals with protected animals is not done efficiently. This results in long times until protected animals are handed over to suitable owners, which compromises the welfare of the animals, and makes it difficult for users who want to keep pets to find the right animal. In addition, handling requests in physical stores takes time and effort, so there is a need for more efficient matching.

[0391] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0392] In this invention, the server includes: means for constructing an AI model that matches protected animals with users' desired conditions based on information from a first database containing information about protected animals and a second database containing information expressing the desire to own a pet; means for the user to input their desired conditions using a smartphone application and identify the most suitable protected animal based on the AI ​​model; and means for providing detailed information about the identified protected animal to the user's smartphone. This allows users to input their desired conditions using their smartphone before visiting a physical store and to check in advance information about the most suitable protected animal. This allows protected animals to be quickly transferred to appropriate owners, improving the welfare of the animals and improving user convenience.

[0393] The "first database" refers to a database that stores information about the characteristics, size, and age of protected animals.

[0394] The "second database" is a database that stores information about the personality, size, and age of a pet desired by a user who wants to keep a pet.

[0395] The "AI model" is an artificial intelligence that matches protected animals with the user's desired conditions based on information from the first and second databases.

[0396] The "smartphone application" is application software that allows users to input their desired criteria and use an AI model to identify the most suitable protected animals.

[0397] "Detailed information on identified protected animals" refers to specific information on protected animals that the AI ​​model matches based on the user's desired conditions, and is provided through a smartphone application.

[0398] The system for implementing this invention is designed to efficiently match rescued animals with the requirements of users who want to keep pets.

[0399] The system includes the following main elements:

[0400] 1. First database: stores information about the characteristics, size, and age of the protected animals.

[0401] 2. A second database: stores information about the personality, size, and age of the user's desired pet.

[0402] 3. AI model: Matches protected animals with the user's desired conditions based on information from the first and second databases.

[0403] Users use a smartphone application to input their desired conditions. The detailed flow is explained below.

[0404] Explaining the program's processing in natural language

[0405] 1. Data Entry

[0406] Users launch a smartphone application and enter information about the pet they want, such as its personality, size, and age.

[0407] 2. Data Transmission

[0408] The smartphone application sends the desired conditions entered by the user to the server in JSON format using an HTTP POST request.

[0409] 3. Matching process

[0410] The server receives the request data containing the user's desired conditions and performs matching using an AI model based on the information from the first and second databases, which uses the Nearest Neighbors algorithm.

[0411] 4. Obtaining the results

[0412] The server uses an AI model to identify the most suitable protected animal and obtains its details.

[0413] 5. Data provision

[0414] The server provides detailed information about identified protected animals to the user's smartphone in real time.

[0415] Implementation example

[0416] For example, if a user wants a pet that is "gentle, medium-sized, and under three years old," they can enter these requirements into a smartphone application. The application then sends these requirements to a server, which uses an AI model to identify the most suitable rescue animal. Details of the identified rescue animal are then sent to the user's smartphone, allowing them to review the information before visiting a physical store.

[0417] Prompt Sentence Examples

[0418] "Based on data from a first database containing information on the characteristics, size, and age of rescued animals, and a second database containing the desired conditions of users looking to adopt a pet, please find the rescued animal that best meets the user's desired conditions. For example, if user A wants a pet that is 'gentle, medium-sized, and under 3 years old,' please identify the best rescued animal."

[0419] This system allows users to check information about the most suitable rescued animals before visiting a physical store, enabling efficient matching. The server uses a database and AI models to match the user's preferences with the characteristics of the rescued animals and find the optimal match.

[0420] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0421] Step 1:

[0422] Users launch a smartphone application and enter the desired pet's characteristics, size, age, and other criteria.

[0423] Input: Desired conditions entered by the user (personality, size, age)

[0424] Output: JSON format of input data

[0425] Specific operation: The user enters desired conditions into the input form of the smartphone application and presses the submit button. The application converts this data into JSON format.

[0426] Step 2:

[0427] The terminal transmits the input desired condition data to the server.

[0428] Input: User preferences in JSON format

[0429] Output: HTTP POST request to the server

[0430] Specific operation: The smartphone application sends JSON data containing the user's desired conditions to the server as an HTTP POST request.

[0431] Step 3:

[0432] The server analyzes the received desired condition data and stores it in a second database.

[0433] Input: JSON format data received by the server

[0434] Output: User preferences stored in a second database

[0435] Specific operation: The server receives the HTTP POST request, parses the data, and stores it in a second database.

[0436] Step 4:

[0437] The server uses an AI model based on the information from the first and second databases to identify the most suitable animals for protection.

[0438] Input: Protected animal information from the first database, user preferences from the second database

[0439] Output: Details of the best protected animals

[0440] How it works: The server uses an AI model (Nearest Neighbors algorithm) to match the protected animals that best meet the user's desired criteria.

[0441] Step 5:

[0442] The server obtains detailed information about the identified protected animals and provides it to the user's smartphone.

[0443] Input: Details of the best rescue animal

[0444] Output: Detailed information about protected animals sent to your smartphone

[0445] Specific operation: The server obtains detailed information about the identified protected animals and sends the data to the user's smartphone. The user's smartphone application receives a notification and displays the information.

[0446] Step 6:

[0447] Users can check detailed information on their smartphone application and prepare to visit the physical store.

[0448] Input: Details of the protected animal sent from the server

[0449] Output: Information on identified rescue animals and preparation for store visits

[0450] Specific actions: The user opens the smartphone application, checks the notification, views the details, and prepares to visit the physical store based on the information they view.

[0451] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0452] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system combines an emotion engine that recognizes the user's emotions to correct the user's desired conditions and make it possible to match more optimal rescued animals.

[0453] The system consists of a first database that stores information about protected animals provided by shelters, a second database that stores the desired conditions of users who want to keep pets, and an AI model that matches protected animals with the desired conditions of users based on these databases.In addition, by incorporating an emotion engine, the system can analyze the user's emotions and adjust the user's desired conditions based on the results.

[0454] Specifically, the server stores information about the characteristics, size, and age of rescued animals in a first database. For example, it reads information provided by rescue facilities in CSV file format and saves it in the database. A second database stores information about the characteristics, size, and age of pets desired by users.

[0455] The user uses the terminal to input desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," and then submits the input. The terminal then sends this information to the server.

[0456] The server builds an AI model based on the first and second databases, using the Nearest Neighbors algorithm to predict the best animal to rescue based on the user's desired conditions.

[0457] Next, the emotion engine analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page). As the user browses pet information, the emotion engine monitors their emotional state in real time and automatically adjusts their preferences accordingly if they become excited or anxious.

[0458] For example, if User A inputs "calm, medium-sized, under 3 years old" as their desired criteria, and the emotion engine detects that the user is particularly excited while browsing the information, the server will fine-tune the desired criteria based on this information and re-identify the most suitable protected animal. The server will then obtain the ID of the re-identified protected animal and notify the information to the user's device.

[0459] The device receives detailed information about the sheltered animal from the server and displays it to the user, allowing the user to decide whether to contact the shelter.

[0460] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion engine, matching can be done with consideration for the user's feelings.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] The server loads information about the animals provided by the shelter as a first database. Specifically, it loads data about their personalities, sizes, and ages in CSV file format and stores the data in the database.

[0464] Step 2:

[0465] The server reads the desired pet's characteristics, size, and age, which are provided by the user as a second database, also in CSV file format, and stores the data in the database.

[0466] Step 3:

[0467] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to generate a predictive model using features based on the protected animals and the user's desired conditions.

[0468] Step 4:

[0469] Users input their desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," using a web browser or a dedicated application.

[0470] Step 5:

[0471] The terminal sends the input user's desired conditions to the server, and transfers the data to the server using a communication protocol (e.g., HTTP POST).

[0472] Step 6:

[0473] The server receives the user's desired conditions, inputs them into the AI ​​model, and identifies the most suitable protected animal. The AI ​​model then obtains the ID of the protected animal and temporarily stores its details.

[0474] Step 7:

[0475] The server runs an emotion engine that recognizes users' emotions in real time. The emotion engine collects data on users' operations and behavior while they browse web pages and applications, and estimates their emotional state. For example, it analyzes emotions from page viewing time, click frequency, and viewed content.

[0476] Step 8:

[0477] The emotion engine analyzes the user's emotional state and adjusts the user's preferences based on the analysis results. If the user is excited, it automatically adjusts the desired pet's personality, size, and other conditions. At the same time, the server re-inputs the adjusted preferences into the AI ​​model to re-identify the most suitable rescue animal.

[0478] Step 9:

[0479] The server notifies the user's device of the ID and detailed information of the re-identified protected animal based on the desired conditions corrected by the emotion engine.

[0480] Step 10:

[0481] The device then displays the received information about the shelter animal to the user, visually presenting the animal's characteristics, size, age, and other details to the user, allowing the user to decide whether to contact the shelter based on this information.

[0482] In this way, the system efficiently matches rescued animals with users who want to keep pets, and by using an emotion engine, it can achieve optimal matching that takes into account the emotional state of the user, thereby reducing the number of unhappy animals and increasing the number of happy homes.

[0483] Example 2

[0484] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0485] Conventional systems for matching rescued animals with users do not take into account the user's emotional state, making it difficult to recommend the most suitable rescued animal based on the user's true preferences. Furthermore, because the user's preferences are fixed, a variety of options are not offered, resulting in a decrease in the chances of a rescued animal being handed over to a suitable owner.

[0486] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0487] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to have a pet, means for constructing an AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, means including an emotion analysis engine that analyzes the user's emotional state based on the user's input method and browsing behavior, means for correcting the user's desired conditions based on the results of the emotion analysis engine, means for using the AI ​​model to identify the most suitable protected animal based on the corrected desired conditions, and means for providing the user with information on the identified protected animal. This enables flexible and optimal matching that takes the user's emotional state into consideration.

[0488] definition statement

[0489] The "first database" is a database that stores information about protected animals.

[0490] The "second database" is a database that stores information about the desired conditions of users who want to keep a pet.

[0491] The "artificial intelligence model" is a learning system that matches protected animals with the user's desired conditions based on information from the first database and the second database.

[0492] An "emotion analysis engine" is a system that analyzes a user's emotional state based on their input method and browsing behavior.

[0493] The "means for correcting the user's desired conditions" is a method for adjusting the desired conditions input by the user based on the results of the emotion analysis engine.

[0494] The "means for identifying the most suitable protected animal" is a method that uses an artificial intelligence model to select a protected animal that is suitable for the user based on the adjusted desired conditions.

[0495] "Means for providing information about protected animals" refers to a method for conveying detailed information about identified protected animals to users.

[0496] MODE FOR CARRYING OUT THE INVENTION

[0497] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system is combined with an emotion analysis engine that recognizes the user's emotions, allowing it to correct the user's desired conditions and match them with more optimal rescued animals.

[0498] System Configuration

[0499] The system consists of the following elements:

[0500] 1. Database 1 (Protected Animal Information):

[0501] The first database contains information about the characteristics, size, and age of the animals in the shelter. Using the information provided by the shelter, a CSV file is read using the Python pandas library and stored in the database. For example, information such as "character: gentle," "size: small," and "age: 2 years old" is saved.

[0502] 2. Secondary database (user preference):

[0503] The second database contains information about the pet's personality, size, and age, which users input via their smartphone or computer. This information is also stored in the database and managed for each user.

[0504] 3. Artificial Intelligence Model:

[0505] Based on the information in the first and second databases, an artificial intelligence model is constructed to match rescued animals with the user's desired conditions. This model uses the Nearest Neighbors algorithm from the scikit-learn library. For example, it predicts the best rescued animals for the user's desired conditions.

[0506] 4. Sentiment Analysis Engine:

[0507] The emotion analysis engine analyzes a user's emotional state based on their input and browsing behavior. It uses Microsoft's Azure Emotion API. For example, it can monitor a user's emotional state in real time when viewing information about a specific protected animal, and obtain emotional information.

[0508] 5. How to adjust user preferences:

[0509] It includes a means for adjusting a user's preferences based on the results of the sentiment analysis engine. For example, if a user enters a preference of "calm, medium-sized, under 3 years old" but shows great excitement while viewing information about a particular pet, the preference will be adjusted to "small."

[0510] 6. How to identify the best shelter animals:

[0511] An artificial intelligence model is used to identify the best rescue animals based on the adjusted preferences, and then rematch based on preferences that reflect the user's emotional state.

[0512] 7. Means of providing information about protected animals:

[0513] Information about the identified rescued animals is provided to the user and displayed on the device, allowing the user to decide whether or not to contact the rescue facility.

[0514] Specific examples

[0515] For example, consider a situation where a user enters the following criteria into a smartphone app:

[0516] "I'm looking for a calm, medium-sized pet under the age of 3."

[0517] The system stores the user's desired criteria in a second database and uses an artificial intelligence model to identify the most suitable rescue animal. As the user browses information about a specific pet, an emotional analysis engine monitors the user's emotional state. If the user is overly agitated, the system reevaluates the criteria and performs a new match based on the revised criteria. As a result, the most suitable rescue animal information is re-identified and displayed on the user's device. The user can then contact the shelter using the provided information. This process streamlines the user's selection of the pet that is best for them.

[0518] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion analysis engine, matching can be done while taking into consideration the feelings of the user.

[0519] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0520] Program processing flow

[0521] Step 1: Store data in the first database

[0522] The server receives information about protected animals provided by the shelter in the form of a CSV file. It reads the received CSV file using the Python pandas library and stores the data in the first database. The input is the CSV file, and the output is the information about protected animals stored in the database. The specific operations include reading the CSV file, cleansing the data, and inserting it into the database.

[0523] Step 2: Store user preferences in a second database

[0524] The user uses a terminal to input the desired pet's conditions. For example, they input specific prompts such as "personality: gentle," "size: medium," and "age: under 3 years old." This input information is sent from the terminal to the server, which stores it in a second database. The input is the user's desired conditions, and the output is the user's desired conditions stored in the database. Specific operations include sending the input form, receiving the data, and inserting it into the database.

[0525] Step 3: Building an artificial intelligence model

[0526] The server retrieves data from the first and second databases and uses this data to build an AI model that matches protected animals with the user's desired conditions. In this example, the Nearest Neighbors algorithm from the scikit-learn library is used. The input is the data retrieved from the first and second databases, and the output is a trained AI model. Specific operations include data standardization, model training, and model saving.

[0527] Step 4: Identifying the best rescue animals

[0528] The server uses the constructed artificial intelligence model to identify the most suitable animal for rescue based on the user's desired conditions. The input is the user's desired conditions, and the output is information on the most suitable animal for rescue. Specific operations include prediction by the model and filtering of the results.

[0529] Step 5: Applying the sentiment analysis engine

[0530] When a user is browsing information about protected animals on their device, the server uses an emotion analysis engine to analyze the user's emotional state. This analysis is performed using Microsoft's Azure Emotion API. The input is the user's browsing behavior data, and the output is the user's emotional state. Specific operations include collecting the user's input method and browsing behavior data, calling the emotion analysis API, and obtaining emotional information.

[0531] Step 6: Adjust your preferences

[0532] The server corrects the user's desired conditions based on the results of the emotion analysis engine. For example, if the user inputs the desired conditions of "calm, medium size, under 3 years old," but becomes particularly excited while browsing, the desired conditions will be adjusted to "small." The input is the result of the emotion analysis, and the output is the corrected desired conditions. Specific operations include analyzing the emotion data and updating the desired conditions.

[0533] Step 7: Re-identify the best animals to protect

[0534] Based on the revised preferences, the server again uses the AI ​​model to identify the most suitable animal for protection. The input is the revised preferences, and the output is the updated information on the most suitable animal for protection. Specifically, the model again predicts and filters the results.

[0535] Step 8: Inform users

[0536] The server transmits detailed information about the identified protected animal to the user's terminal. The terminal displays the received information to the user. The input is the information about the re-identified protected animal, and the output is the details of the protected animal displayed on the terminal. Specific operations include transmitting and displaying the information.

[0537] (Application example 2)

[0538] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0539] Conventional matching systems for rescued animals and users looking to adopt pets simply search for a partner based on desired criteria, without taking into account the user's feelings, which often resulted in an inadequate match. Furthermore, users' true desires can change based on their emotions, so it's necessary to adjust the desired criteria. Furthermore, the system lacks functionality for providing users with detailed information about pets, making the process for users to view or inquire complicated.

[0540] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0541] In this invention, the server includes a first database containing information about protected animals, a second database containing information expressing the desire to own a pet, a means for constructing a generative AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, a means equipped with an emotion engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis, a means for identifying the most suitable protected animal based on the user's desired conditions using the generative AI model and the emotion engine, and a means for providing the user with information about the identified protected animal. This allows the desired conditions to be adjusted to reflect the user's emotions, resulting in more accurate matching. It also allows users to smoothly make inquiries and make reservations for tours.

[0542] The "first database" is a database for storing information on protected animals.

[0543] The "second database" is a database for storing the desired conditions of users who want to keep a pet.

[0544] A "generative AI model" is an artificial intelligence model that makes optimal matches based on the user's desired conditions and information about protected animals.

[0545] The "emotion engine" is an engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis results.

[0546] "Matching means" means a means that provides the functionality to identify the most suitable protected animal based on the user's desired criteria using a generative AI model.

[0547] "Means for providing" refers to means for providing the function of delivering information about identified protected animals to users.

[0548] To implement this invention, a system is constructed that uses a server, a user terminal, a first database, a second database, a generative AI model, and an emotion engine. Specific embodiments are described below.

[0549] First, the server has a first database containing information about rescued animals and a second database containing the desired conditions of users who want to keep a pet. The first database stores detailed information about rescued animals, such as their personality, size, and age. The second database stores information about the personality, size, and age of the pet desired by the user.

[0550] The server then uses this information to build a generative AI model that uses a Nearest Neighbors algorithm to match users' preferences with information about rescued animals.

[0551] The server also has an emotion engine that analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page, facial expression analysis, voice analysis, etc.). This emotion engine monitors the user's emotions in real time while they are browsing information about protected animals, and if the user becomes excited or anxious, it adjusts their desired conditions accordingly.

[0552] For example, if User A desires a "gentle, medium-sized, under 3-year-old" pet and shows particular excitement while browsing the information, the emotion engine will use this information to appropriately fine-tune the desired conditions. The server will then use the generative AI model again to identify the most suitable rescue animal based on these adjusted conditions. Information about the identified rescue animal is sent to the user's device, where detailed information, photos, and videos are displayed. Using this information, users can directly contact the shelter or make a reservation for a tour.

[0553] For example, if a user inputs the desired criteria of "gentle, medium-sized, under 3 years old" and the emotion engine predicts that it is "fun," the generative AI model will suggest the most suitable rescue animal based on these desired criteria. An example of a prompt is as follows:

[0554] "The user's desired conditions were entered as 'personality: gentle, medium size, age: under 3', and the user's emotion was estimated to be 'fun'. Please recommend the most suitable rescue animal based on these conditions."

[0555] This system allows users to adjust their desired conditions to reflect their feelings, resulting in more accurate matching. It also allows users to smoothly make inquiries and reservations for viewings, increasing the chances that rescued animals will be quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes.

[0556] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0557] Step 1:

[0558] The user inputs desired conditions for the pet into the terminal, such as "personality, size, age," and presses the send button on the terminal.

[0559] Input: Personality: Gentle, Size: Medium, Age: Under 3 years old

[0560] Output: Desired condition data sent from the terminal to the server

[0561] Step 2:

[0562] The server receives the desired condition data transmitted from the terminal and stores the received data in a second database.

[0563] Input: Desired condition data sent from the terminal

[0564] Output: User preference data stored in a second database

[0565] Step 3:

[0566] The server retrieves information about the shelter animals from the first database, including the characteristics, size, age, etc. of the shelter animals.

[0567] Input: First database

[0568] Output: Protected animal information

[0569] Step 4:

[0570] The server reads the user's desired conditions from the second database and builds a generative AI model based on the protected animal information obtained from the first database, using the Nearest Neighbors algorithm to identify the most suitable protected animals.

[0571] Input: User's desired conditions, protected animal information from the first database

[0572] Output: Generated AI model, identifying the best animals to protect

[0573] Step 5:

[0574] The server uses an emotion engine to analyze the user's emotional state, inferring their emotions based on their input method and browsing behavior, and adjusts their desired conditions based on the results.

[0575] Input: User input methods and browsing behavior data

[0576] Output: Corrected user preferences

[0577] Step 6:

[0578] Based on the desired conditions corrected by the emotion engine, the server again uses the generative AI model to identify the most suitable protected animal. The protected animal is identified using a proximity search algorithm.

[0579] Input: Corrected user preferences

[0580] Output: Identification of the best animals to protect

[0581] Step 7:

[0582] The server sends detailed information about the identified protected animal to the user's device, including the animal's characteristics, size, age, photos, videos, etc.

[0583] Input: Information about the identified protected animal

[0584] Output: Details of the protected animal sent to the user's device

[0585] Step 8:

[0586] The user can view the detailed information about the protected animals received on their device, and after checking the information, make inquiries to the shelter or make reservations for a tour.

[0587] Input:Detailed information about protected animals

[0588] Output: Availability of inquiries and tour reservations

[0589] In this way, a system will be created that optimally matches protected animals with users, allowing users to smoothly make inquiries and make reservations for tours.

[0590] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0591] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0592] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0593] [Third embodiment]

[0594] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0595] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0596] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0597] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0598] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0599] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0600] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0601] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0602] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0603] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0604] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0605] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0606] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0607] Specifically, data on information provided by the shelter (e.g., personality, size, age) is stored in a first database, and data on desired conditions provided by users who want to keep a pet (e.g., desired personality, size, age) is stored in a second database.

[0608] The server builds an AI model based on the first and second databases, which uses a Nearest Neighbors algorithm and is designed to identify rescued animals that best meet the user's desired criteria.

[0609] When a user enters their desired conditions, they use their device to send this information to a server. The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model then matches them based on specific characteristics (personality, size, age).

[0610] For example, suppose User A wants a pet that is "gentle, medium-sized, and under three years old." This preference is registered in a second database, and the server uses an AI model to find the most suitable protected animal based on this. If the protected animal identified by the server is ID: 102, the server notifies the user's device of this information.

[0611] Users can then review the details of the identified rescued animals and, after gaining a full understanding, contact the rescue facility, allowing them to quickly and efficiently find the pet that best suits their needs.

[0612] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0613] The processing flow will be explained below.

[0614] Step 1:

[0615] The server reads information about the animals provided by the shelter as the first database. Specifically, it obtains data on their characteristics, size, and age in a format such as a CSV file and stores it in the system.

[0616] Step 2:

[0617] The server reads the desired characteristics, size, and age of the pet provided by the user as a second database, also in the form of a CSV file, and stores the data in the system.

[0618] Step 3:

[0619] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to learn data including features (personality, size, age) and creates a predictive model based on the user's desired conditions.

[0620] Step 4:

[0621] The user inputs their desired conditions on the terminal, for example, selecting and inputting conditions such as "personality: calm," "size: medium," and "age: under 3 years old."

[0622] Step 5:

[0623] The terminal sends the input user's desired conditions to the server, using a communication protocol (e.g., HTTP POST) to send the data.

[0624] Step 6:

[0625] The server receives the user's desired conditions and inputs them into the AI ​​model. Specifically, the desired conditions are passed to the AI ​​model as features, and the model identifies the protected animal that best matches the conditions.

[0626] Step 7:

[0627] The server retrieves the ID of the best protected animal identified by the AI ​​model and notifies the user's device of that information. For example, if the best match is "dog ID: 102," the server transfers the details to the device.

[0628] Step 8:

[0629] The device then displays the received information about the shelter animal to the user, visually presenting the animal's personality, size, age, and other details to the user, allowing the user to decide whether to contact the shelter.

[0630] In this way, the system can efficiently match rescued animals with users who want to keep pets, reducing the number of unhappy animals and increasing the number of happy homes.

[0631] Example 1

[0632] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0633] In modern society, the number of protected animals is increasing, and the needs of owners for these animals are becoming more diverse. Conventional methods have made it difficult to efficiently match protected animals with users' desired conditions, requiring a lot of time and effort. As a result, appropriate protected animals may not be handed over to owners promptly, and the welfare of the animals may decline. To solve this problem, a system that can efficiently match protected animals with users' desired conditions is needed.

[0634] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0635] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to keep a pet, means for constructing an AI model that matches protected animals with the user's desired conditions based on the information in the first and second databases, means for the user to input the desired conditions using a terminal and send the information to the server, means for identifying the most suitable protected animal based on the user's desired conditions using the AI ​​model, and means for notifying the user's terminal of information on the identified protected animal. This enables the user to quickly and efficiently find the protected animal that best suits their desired conditions.

[0636] "Shelter animals" are animals that have been rescued by animal shelters or rescue organizations and are looking for new homes.

[0637] A "database" is a collection of information structured in a particular format that can be easily accessed, managed, and updated.

[0638] An "AI model" is a computational model that uses artificial intelligence algorithms to analyze data and make predictions.

[0639] A "server" is a computer system that provides services to other computers (clients) over a network.

[0640] A "terminal" is a device that a user uses to communicate with a server and input or receive information, examples of which include a PC or smartphone.

[0641] "User" means an individual or organization using the System to search for protected animals.

[0642] "Desired conditions" are the specific characteristics or requirements a user desires in a new pet, including personality, size, age, etc.

[0643] The "Nearest Neighbors Algorithm" is a machine learning algorithm for searching for the nearest data point to a given data point.

[0644] "Matching" is the process of comparing information about protected animals contained in a first database with the user's desired conditions contained in a second database to identify a suitable pair.

[0645] "Notification" refers to the act of the server sending specific information to the user's terminal to inform the user.

[0646] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0647] The server stores information provided by the shelter (e.g., personality, size, age) in a first database, and stores desired conditions provided by the user (e.g., desired personality, size, age) in a second database. A database management system (e.g., MySQL, PostgreSQL) is used to store this data.

[0648] The user inputs their desired conditions using a device (e.g., PC or smartphone). For example, they input a desired condition such as "I'm looking for a calm, medium-sized pet under 3 years old." This information is sent from the device to the server, which then receives it.

[0649] The server builds an AI model based on the data from the first and second databases using the Nearest Neighbors algorithm, specifically using Python's scikit-learn library, to identify the protected animals that best meet the user's desired criteria.

[0650] The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model performs matching based on specific characteristics (personality, size, age). For example, if User A requests a pet that is "gentle, medium-sized, and under three years old," this desired condition is registered in the second database. The server uses the AI ​​model to find the most suitable rescue animal based on these conditions. If the rescue animal identified by the server is ID: 102, the server notifies User A's device of this information.

[0651] Users can then use their devices to view detailed information about the identified rescued animal, such as its personality, size, and age, and contact the rescue facility after gaining a thorough understanding of the animal. This allows users to quickly and efficiently find the pet that best suits their needs.

[0652] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0653] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0654] Step 1: Store protected animal information in the first database

[0655] The server collects information about protected animals provided by the shelters. The input is data such as personality, size, and age provided by the shelters. Specifically, the server reads data uploaded to the server in formats such as CSV files using the Python pandas library.

[0656] The server stores the read data in a first database. The output is information about protected animals neatly stored in the first database. A database management system such as MySQL is used.

[0657] Step 2: Store the user's preferences in a second database

[0658] The user uses the terminal to input desired conditions (e.g., personality, size, age). The input is data received through the user interface. Specifically, the user enters the desired conditions into a form on the web browser and presses the "Submit" button.

[0659] The terminal sends the entered desired conditions to the server, and the data is transferred to the server as an HTTP request.

[0660] The server stores the received desired conditions in a second database, and the output is information on the desired conditions stored in an orderly manner in the second database.

[0661] Step 3: Building an AI model

[0662] The server builds an AI model based on data from the first and second databases. The input is information about protected animals and the user's desired conditions. Specifically, the model is built using the Nearest Neighbors algorithm using Python's scikit-learn library. Data preprocessing, such as standardization and normalization, is also performed.

[0663] The server stores the constructed AI model, and the output is an AI model optimized for matching rescued animals with the user's desired conditions.

[0664] Step 4: Matching your desired conditions

[0665] The server inputs the user's desired conditions into the AI ​​model and identifies the most suitable animal for protection. The input is the user's desired conditions stored in a second database. Specifically, it uses the Python scikit-learn library to search for the most suitable animal for protection using the Nearest Neighbors algorithm.

[0666] The server obtains information about the identified protected animals, and outputs the IDs of the identified protected animals and their details.

[0667] Step 5: Notification of match results

[0668] The server notifies the user's device of the information about the identified protected animal. The input is detailed information about the identified protected animal. Specifically, the server generates a JSON response and sends it to the user's device as an HTTP response.

[0669] The terminal displays the received information, and the output is detailed information about the protected animals displayed in a format that can be viewed by the user.

[0670] Step 6: Confirm details and contact

[0671] The user uses the terminal to check the detailed information of the identified protected animal, and the input is the protected animal information sent from the server.

[0672] The user can contact the shelter based on the details. The output is that the user can contact the shelter and take specific action to adopt a new pet by clicking on a link on the device to view the shelter's contact information and contact them.

[0673] Through this process, users can efficiently find the rescued animal that is best suited to them, and the rescued animal can be quickly handed over to the right owner.

[0674] (Application example 1)

[0675] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0676] In modern society, matching users who want to keep protected animals with protected animals is not done efficiently. This results in long times until protected animals are handed over to suitable owners, which compromises the welfare of the animals, and makes it difficult for users who want to keep pets to find the right animal. In addition, handling requests in physical stores takes time and effort, so there is a need for more efficient matching.

[0677] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0678] In this invention, the server includes: means for constructing an AI model that matches protected animals with users' desired conditions based on information from a first database containing information about protected animals and a second database containing information expressing the desire to own a pet; means for the user to input their desired conditions using a smartphone application and identify the most suitable protected animal based on the AI ​​model; and means for providing detailed information about the identified protected animal to the user's smartphone. This allows users to input their desired conditions using their smartphone before visiting a physical store and to check in advance information about the most suitable protected animal. This allows protected animals to be quickly transferred to appropriate owners, improving the welfare of the animals and improving user convenience.

[0679] The "first database" refers to a database that stores information about the characteristics, size, and age of protected animals.

[0680] The "second database" is a database that stores information about the personality, size, and age of a pet desired by a user who wants to keep a pet.

[0681] The "AI model" is an artificial intelligence that matches protected animals with the user's desired conditions based on information from the first and second databases.

[0682] The "smartphone application" is application software that allows users to input their desired criteria and use an AI model to identify the most suitable protected animals.

[0683] "Detailed information on identified protected animals" refers to specific information on protected animals that the AI ​​model matches based on the user's desired conditions, and is provided through a smartphone application.

[0684] The system for implementing this invention is designed to efficiently match rescued animals with the requirements of users who want to keep pets.

[0685] The system includes the following main elements:

[0686] 1. First database: stores information about the characteristics, size, and age of the protected animals.

[0687] 2. A second database: stores information about the personality, size, and age of the user's desired pet.

[0688] 3. AI model: Matches protected animals with the user's desired conditions based on information from the first and second databases.

[0689] Users use a smartphone application to input their desired conditions. The detailed flow is explained below.

[0690] Explaining the program's processing in natural language

[0691] 1. Data Entry

[0692] Users launch a smartphone application and enter information about the pet they want, such as its personality, size, and age.

[0693] 2. Data Transmission

[0694] The smartphone application sends the desired conditions entered by the user to the server in JSON format using an HTTP POST request.

[0695] 3. Matching process

[0696] The server receives the request data containing the user's desired conditions and performs matching using an AI model based on the information from the first and second databases, which uses the Nearest Neighbors algorithm.

[0697] 4. Obtaining the results

[0698] The server uses an AI model to identify the most suitable protected animal and obtains its details.

[0699] 5. Data provision

[0700] The server provides detailed information about identified protected animals to the user's smartphone in real time.

[0701] Implementation example

[0702] For example, if a user wants a pet that is "gentle, medium-sized, and under three years old," they can enter these requirements into a smartphone application. The application then sends these requirements to a server, which uses an AI model to identify the most suitable rescue animal. Details of the identified rescue animal are then sent to the user's smartphone, allowing them to review the information before visiting a physical store.

[0703] Prompt Sentence Examples

[0704] "Based on data from a first database containing information on the characteristics, size, and age of rescued animals, and a second database containing the desired conditions of users looking to adopt a pet, please find the rescued animal that best meets the user's desired conditions. For example, if user A wants a pet that is 'gentle, medium-sized, and under 3 years old,' please identify the best rescued animal."

[0705] This system allows users to check information about the most suitable rescued animals before visiting a physical store, enabling efficient matching. The server uses a database and AI models to match the user's preferences with the characteristics of the rescued animals and find the optimal match.

[0706] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0707] Step 1:

[0708] Users launch a smartphone application and enter the desired pet's characteristics, size, age, and other criteria.

[0709] Input: Desired conditions entered by the user (personality, size, age)

[0710] Output: JSON format of input data

[0711] Specific operation: The user enters desired conditions into the input form of the smartphone application and presses the submit button. The application converts this data into JSON format.

[0712] Step 2:

[0713] The terminal transmits the input desired condition data to the server.

[0714] Input: User preferences in JSON format

[0715] Output: HTTP POST request to the server

[0716] Specific operation: The smartphone application sends JSON data containing the user's desired conditions to the server as an HTTP POST request.

[0717] Step 3:

[0718] The server analyzes the received desired condition data and stores it in a second database.

[0719] Input: JSON format data received by the server

[0720] Output: User preferences stored in a second database

[0721] Specific operation: The server receives the HTTP POST request, parses the data, and stores it in a second database.

[0722] Step 4:

[0723] The server uses an AI model based on the information from the first and second databases to identify the most suitable animals for protection.

[0724] Input: Protected animal information from the first database, user preferences from the second database

[0725] Output: Details of the best protected animals

[0726] How it works: The server uses an AI model (Nearest Neighbors algorithm) to match the protected animals that best meet the user's desired criteria.

[0727] Step 5:

[0728] The server obtains detailed information about the identified protected animals and provides it to the user's smartphone.

[0729] Input: Details of the best rescue animal

[0730] Output: Detailed information about protected animals sent to your smartphone

[0731] Specific operation: The server obtains detailed information about the identified protected animals and sends the data to the user's smartphone. The user's smartphone application receives a notification and displays the information.

[0732] Step 6:

[0733] Users can check detailed information on their smartphone application and prepare to visit the physical store.

[0734] Input: Details of the protected animal sent from the server

[0735] Output: Information on identified rescue animals and preparation for store visits

[0736] Specific actions: The user opens the smartphone application, checks the notification, views the details, and prepares to visit the physical store based on the information they view.

[0737] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0738] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system combines an emotion engine that recognizes the user's emotions to correct the user's desired conditions and make it possible to match more optimal rescued animals.

[0739] The system consists of a first database that stores information about protected animals provided by shelters, a second database that stores the desired conditions of users who want to keep pets, and an AI model that matches protected animals with the desired conditions of users based on these databases.In addition, by incorporating an emotion engine, the system can analyze the user's emotions and adjust the user's desired conditions based on the results.

[0740] Specifically, the server stores information about the characteristics, size, and age of rescued animals in a first database. For example, it reads information provided by rescue facilities in CSV file format and saves it in the database. A second database stores information about the characteristics, size, and age of pets desired by users.

[0741] The user uses the terminal to input desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," and then submits the input. The terminal then sends this information to the server.

[0742] The server builds an AI model based on the first and second databases, using the Nearest Neighbors algorithm to predict the best animal to rescue based on the user's desired conditions.

[0743] Next, the emotion engine analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page). As the user browses pet information, the emotion engine monitors their emotional state in real time and automatically adjusts their preferences accordingly if they become excited or anxious.

[0744] For example, if User A inputs "calm, medium-sized, under 3 years old" as their desired criteria, and the emotion engine detects that the user is particularly excited while browsing the information, the server will fine-tune the desired criteria based on this information and re-identify the most suitable protected animal. The server will then obtain the ID of the re-identified protected animal and notify the information to the user's device.

[0745] The device receives detailed information about the sheltered animal from the server and displays it to the user, allowing the user to decide whether to contact the shelter.

[0746] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion engine, matching can be done with consideration for the user's feelings.

[0747] The processing flow will be explained below.

[0748] Step 1:

[0749] The server loads information about the animals provided by the shelter as a first database. Specifically, it loads data about their personalities, sizes, and ages in CSV file format and stores the data in the database.

[0750] Step 2:

[0751] The server reads the desired pet's characteristics, size, and age, which are provided by the user as a second database, also in CSV file format, and stores the data in the database.

[0752] Step 3:

[0753] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to generate a predictive model using features based on the protected animals and the user's desired conditions.

[0754] Step 4:

[0755] Users input their desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," using a web browser or a dedicated application.

[0756] Step 5:

[0757] The terminal sends the input user's desired conditions to the server, and transfers the data to the server using a communication protocol (e.g., HTTP POST).

[0758] Step 6:

[0759] The server receives the user's desired conditions, inputs them into the AI ​​model, and identifies the most suitable protected animal. The AI ​​model then obtains the ID of the protected animal and temporarily stores its details.

[0760] Step 7:

[0761] The server runs an emotion engine that recognizes users' emotions in real time. The emotion engine collects data on users' operations and behavior while they browse web pages and applications, and estimates their emotional state. For example, it analyzes emotions from page viewing time, click frequency, and viewed content.

[0762] Step 8:

[0763] The emotion engine analyzes the user's emotional state and adjusts the user's preferences based on the analysis results. If the user is excited, it automatically adjusts the desired pet's personality, size, and other conditions. At the same time, the server re-inputs the adjusted preferences into the AI ​​model to re-identify the most suitable rescue animal.

[0764] Step 9:

[0765] The server notifies the user's device of the ID and detailed information of the re-identified protected animal based on the desired conditions corrected by the emotion engine.

[0766] Step 10:

[0767] The device then displays the received information about the shelter animal to the user, visually presenting the animal's characteristics, size, age, and other details to the user, allowing the user to decide whether to contact the shelter based on this information.

[0768] In this way, the system efficiently matches rescued animals with users who want to keep pets, and by using an emotion engine, it can achieve optimal matching that takes into account the emotional state of the user, thereby reducing the number of unhappy animals and increasing the number of happy homes.

[0769] Example 2

[0770] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0771] Conventional systems for matching rescued animals with users do not take into account the user's emotional state, making it difficult to recommend the most suitable rescued animal based on the user's true preferences. Furthermore, because the user's preferences are fixed, a variety of options are not offered, resulting in a decrease in the chances of a rescued animal being handed over to a suitable owner.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0773] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to have a pet, means for constructing an AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, means including an emotion analysis engine that analyzes the user's emotional state based on the user's input method and browsing behavior, means for correcting the user's desired conditions based on the results of the emotion analysis engine, means for using the AI ​​model to identify the most suitable protected animal based on the corrected desired conditions, and means for providing the user with information on the identified protected animal. This enables flexible and optimal matching that takes the user's emotional state into consideration.

[0774] definition statement

[0775] The "first database" is a database that stores information about protected animals.

[0776] The "second database" is a database that stores information about the desired conditions of users who want to keep a pet.

[0777] The "artificial intelligence model" is a learning system that matches protected animals with the user's desired conditions based on information from the first database and the second database.

[0778] An "emotion analysis engine" is a system that analyzes a user's emotional state based on their input method and browsing behavior.

[0779] The "means for correcting the user's desired conditions" is a method for adjusting the desired conditions input by the user based on the results of the emotion analysis engine.

[0780] The "means for identifying the most suitable protected animal" is a method that uses an artificial intelligence model to select a protected animal that is suitable for the user based on the adjusted desired conditions.

[0781] "Means for providing information about protected animals" refers to a method for conveying detailed information about identified protected animals to users.

[0782] MODE FOR CARRYING OUT THE INVENTION

[0783] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system is combined with an emotion analysis engine that recognizes the user's emotions, allowing it to correct the user's desired conditions and match them with more optimal rescued animals.

[0784] System Configuration

[0785] The system consists of the following elements:

[0786] 1. Database 1 (Protected Animal Information):

[0787] The first database contains information about the characteristics, size, and age of the animals in the shelter. Using the information provided by the shelter, a CSV file is read using the Python pandas library and stored in the database. For example, information such as "character: gentle," "size: small," and "age: 2 years old" is saved.

[0788] 2. Secondary database (user preference):

[0789] The second database contains information about the pet's personality, size, and age, which users input via their smartphone or computer. This information is also stored in the database and managed for each user.

[0790] 3. Artificial Intelligence Model:

[0791] Based on the information in the first and second databases, an artificial intelligence model is constructed to match rescued animals with the user's desired conditions. This model uses the Nearest Neighbors algorithm from the scikit-learn library. For example, it predicts the best rescued animals for the user's desired conditions.

[0792] 4. Sentiment Analysis Engine:

[0793] The emotion analysis engine analyzes a user's emotional state based on their input and browsing behavior. It uses Microsoft's Azure Emotion API. For example, it can monitor a user's emotional state in real time when viewing information about a specific protected animal, and obtain emotional information.

[0794] 5. How to adjust user preferences:

[0795] It includes a means for adjusting a user's preferences based on the results of the sentiment analysis engine. For example, if a user enters a preference of "calm, medium-sized, under 3 years old" but shows great excitement while viewing information about a particular pet, the preference will be adjusted to "small."

[0796] 6. How to identify the best shelter animals:

[0797] An artificial intelligence model is used to identify the best rescue animals based on the adjusted preferences, and then rematch based on preferences that reflect the user's emotional state.

[0798] 7. Means of providing information about protected animals:

[0799] Information about the identified rescued animals is provided to the user and displayed on the device, allowing the user to decide whether or not to contact the rescue facility.

[0800] Specific examples

[0801] For example, consider a situation where a user enters the following criteria into a smartphone app:

[0802] "I'm looking for a calm, medium-sized pet under the age of 3."

[0803] The system stores the user's desired criteria in a second database and uses an artificial intelligence model to identify the most suitable rescue animal. As the user browses information about a specific pet, an emotional analysis engine monitors the user's emotional state. If the user is overly agitated, the system reevaluates the criteria and performs a new match based on the revised criteria. As a result, the most suitable rescue animal information is re-identified and displayed on the user's device. The user can then contact the shelter using the provided information. This process streamlines the user's selection of the pet that is best for them.

[0804] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion analysis engine, matching can be done while taking into consideration the feelings of the user.

[0805] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0806] Program processing flow

[0807] Step 1: Store data in the first database

[0808] The server receives information about protected animals provided by the shelter in the form of a CSV file. It reads the received CSV file using the Python pandas library and stores the data in the first database. The input is the CSV file, and the output is the information about protected animals stored in the database. The specific operations include reading the CSV file, cleansing the data, and inserting it into the database.

[0809] Step 2: Store user preferences in a second database

[0810] The user uses a terminal to input the desired pet's conditions. For example, they input specific prompts such as "personality: gentle," "size: medium," and "age: under 3 years old." This input information is sent from the terminal to the server, which stores it in a second database. The input is the user's desired conditions, and the output is the user's desired conditions stored in the database. Specific operations include sending the input form, receiving the data, and inserting it into the database.

[0811] Step 3: Building an artificial intelligence model

[0812] The server retrieves data from the first and second databases and uses this data to build an AI model that matches protected animals with the user's desired conditions. In this example, the Nearest Neighbors algorithm from the scikit-learn library is used. The input is the data retrieved from the first and second databases, and the output is a trained AI model. Specific operations include data standardization, model training, and model saving.

[0813] Step 4: Identifying the best rescue animals

[0814] The server uses the constructed artificial intelligence model to identify the most suitable animal for rescue based on the user's desired conditions. The input is the user's desired conditions, and the output is information on the most suitable animal for rescue. Specific operations include prediction by the model and filtering of the results.

[0815] Step 5: Applying the sentiment analysis engine

[0816] When a user is browsing information about protected animals on their device, the server uses an emotion analysis engine to analyze the user's emotional state. This analysis is performed using Microsoft's Azure Emotion API. The input is the user's browsing behavior data, and the output is the user's emotional state. Specific operations include collecting the user's input method and browsing behavior data, calling the emotion analysis API, and obtaining emotional information.

[0817] Step 6: Adjust your preferences

[0818] The server corrects the user's desired conditions based on the results of the emotion analysis engine. For example, if the user inputs the desired conditions of "calm, medium size, under 3 years old," but becomes particularly excited while browsing, the desired conditions will be adjusted to "small." The input is the result of the emotion analysis, and the output is the corrected desired conditions. Specific operations include analyzing the emotion data and updating the desired conditions.

[0819] Step 7: Re-identify the best animals to protect

[0820] Based on the revised preferences, the server again uses the AI ​​model to identify the most suitable animal for protection. The input is the revised preferences, and the output is the updated information on the most suitable animal for protection. Specifically, the model again predicts and filters the results.

[0821] Step 8: Inform users

[0822] The server transmits detailed information about the identified protected animal to the user's terminal. The terminal displays the received information to the user. The input is the information about the re-identified protected animal, and the output is the details of the protected animal displayed on the terminal. Specific operations include transmitting and displaying the information.

[0823] (Application example 2)

[0824] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0825] Conventional matching systems for rescued animals and users looking to adopt pets simply search for a partner based on desired criteria, without taking into account the user's feelings, which often resulted in an inadequate match. Furthermore, users' true desires can change based on their emotions, so it's necessary to adjust the desired criteria. Furthermore, the system lacks functionality for providing users with detailed information about pets, making the process for users to view or inquire complicated.

[0826] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0827] In this invention, the server includes a first database containing information about protected animals, a second database containing information expressing the desire to own a pet, a means for constructing a generative AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, a means equipped with an emotion engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis, a means for identifying the most suitable protected animal based on the user's desired conditions using the generative AI model and the emotion engine, and a means for providing the user with information about the identified protected animal. This allows the desired conditions to be adjusted to reflect the user's emotions, resulting in more accurate matching. It also allows users to smoothly make inquiries and make reservations for tours.

[0828] The "first database" is a database for storing information on protected animals.

[0829] The "second database" is a database for storing the desired conditions of users who want to keep a pet.

[0830] A "generative AI model" is an artificial intelligence model that makes optimal matches based on the user's desired conditions and information about protected animals.

[0831] The "emotion engine" is an engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis results.

[0832] "Matching means" means a means that provides the functionality to identify the most suitable protected animal based on the user's desired criteria using a generative AI model.

[0833] "Means for providing" refers to means for providing the function of delivering information about identified protected animals to users.

[0834] To implement this invention, a system is constructed that uses a server, a user terminal, a first database, a second database, a generative AI model, and an emotion engine. Specific embodiments are described below.

[0835] First, the server has a first database containing information about rescued animals and a second database containing the desired conditions of users who want to keep a pet. The first database stores detailed information about rescued animals, such as their personality, size, and age. The second database stores information about the personality, size, and age of the pet desired by the user.

[0836] The server then uses this information to build a generative AI model that uses a Nearest Neighbors algorithm to match users' preferences with information about rescued animals.

[0837] The server also has an emotion engine that analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page, facial expression analysis, voice analysis, etc.). This emotion engine monitors the user's emotions in real time while they are browsing information about protected animals, and if the user becomes excited or anxious, it adjusts their desired conditions accordingly.

[0838] For example, if User A desires a "gentle, medium-sized, under 3-year-old" pet and shows particular excitement while browsing the information, the emotion engine will use this information to appropriately fine-tune the desired conditions. The server will then use the generative AI model again to identify the most suitable rescue animal based on these adjusted conditions. Information about the identified rescue animal is sent to the user's device, where detailed information, photos, and videos are displayed. Using this information, users can directly contact the shelter or make a reservation for a tour.

[0839] For example, if a user inputs the desired criteria of "gentle, medium-sized, under 3 years old" and the emotion engine predicts that it is "fun," the generative AI model will suggest the most suitable rescue animal based on these desired criteria. An example of a prompt is as follows:

[0840] "The user's desired conditions were entered as 'personality: gentle, medium size, age: under 3', and the user's emotion was estimated to be 'fun'. Please recommend the most suitable rescue animal based on these conditions."

[0841] This system allows users to adjust their desired conditions to reflect their feelings, resulting in more accurate matching. It also allows users to smoothly make inquiries and reservations for viewings, increasing the chances that rescued animals will be quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes.

[0842] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0843] Step 1:

[0844] The user inputs desired conditions for the pet into the terminal, such as "personality, size, age," and presses the send button on the terminal.

[0845] Input: Personality: Gentle, Size: Medium, Age: Under 3 years old

[0846] Output: Desired condition data sent from the terminal to the server

[0847] Step 2:

[0848] The server receives the desired condition data transmitted from the terminal and stores the received data in a second database.

[0849] Input: Desired condition data sent from the terminal

[0850] Output: User preference data stored in a second database

[0851] Step 3:

[0852] The server retrieves information about the shelter animals from the first database, including the characteristics, size, age, etc. of the shelter animals.

[0853] Input: First database

[0854] Output: Protected animal information

[0855] Step 4:

[0856] The server reads the user's desired conditions from the second database and builds a generative AI model based on the protected animal information obtained from the first database, using the Nearest Neighbors algorithm to identify the most suitable protected animals.

[0857] Input: User's desired conditions, protected animal information from the first database

[0858] Output: Generated AI model, identifying the best animals to protect

[0859] Step 5:

[0860] The server uses an emotion engine to analyze the user's emotional state, inferring their emotions based on their input method and browsing behavior, and adjusts their desired conditions based on the results.

[0861] Input: User input methods and browsing behavior data

[0862] Output: Corrected user preferences

[0863] Step 6:

[0864] Based on the desired conditions corrected by the emotion engine, the server again uses the generative AI model to identify the most suitable protected animal. The protected animal is identified using a proximity search algorithm.

[0865] Input: Corrected user preferences

[0866] Output: Identification of the best animals to protect

[0867] Step 7:

[0868] The server sends detailed information about the identified protected animal to the user's device, including the animal's characteristics, size, age, photos, videos, etc.

[0869] Input: Information about the identified protected animal

[0870] Output: Details of the protected animal sent to the user's device

[0871] Step 8:

[0872] The user can view the detailed information about the protected animals received on their device, and after checking the information, make inquiries to the shelter or make reservations for a tour.

[0873] Input:Detailed information about protected animals

[0874] Output: Availability of inquiries and tour reservations

[0875] In this way, a system will be created that optimally matches protected animals with users, allowing users to smoothly make inquiries and make reservations for tours.

[0876] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0877] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0878] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0879] [Fourth embodiment]

[0880] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0881] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0882] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0883] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0884] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0885] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0886] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0887] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0888] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0889] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0890] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0891] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0892] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0893] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0894] Specifically, data on information provided by the shelter (e.g., personality, size, age) is stored in a first database, and data on desired conditions provided by users who want to keep a pet (e.g., desired personality, size, age) is stored in a second database.

[0895] The server builds an AI model based on the first and second databases, which uses a Nearest Neighbors algorithm and is designed to identify rescued animals that best meet the user's desired criteria.

[0896] When a user enters their desired conditions, they use their device to send this information to a server. The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model then matches them based on specific characteristics (personality, size, age).

[0897] For example, suppose User A wants a pet that is "gentle, medium-sized, and under three years old." This preference is registered in a second database, and the server uses an AI model to find the most suitable protected animal based on this. If the protected animal identified by the server is ID: 102, the server notifies the user's device of this information.

[0898] Users can then review the details of the identified rescued animals and, after gaining a full understanding, contact the rescue facility, allowing them to quickly and efficiently find the pet that best suits their needs.

[0899] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0900] The processing flow will be explained below.

[0901] Step 1:

[0902] The server reads information about the animals provided by the shelter as the first database. Specifically, it obtains data on their characteristics, size, and age in a format such as a CSV file and stores it in the system.

[0903] Step 2:

[0904] The server reads the desired characteristics, size, and age of the pet provided by the user as a second database, also in the form of a CSV file, and stores the data in the system.

[0905] Step 3:

[0906] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to learn data including features (personality, size, age) and creates a predictive model based on the user's desired conditions.

[0907] Step 4:

[0908] The user inputs their desired conditions on the terminal, for example, selecting and inputting conditions such as "personality: calm," "size: medium," and "age: under 3 years old."

[0909] Step 5:

[0910] The terminal sends the input user's desired conditions to the server, using a communication protocol (e.g., HTTP POST) to send the data.

[0911] Step 6:

[0912] The server receives the user's desired conditions and inputs them into the AI ​​model. Specifically, the desired conditions are passed to the AI ​​model as features, and the model identifies the protected animal that best matches the conditions.

[0913] Step 7:

[0914] The server retrieves the ID of the best protected animal identified by the AI ​​model and notifies the user's device of that information. For example, if the best match is "dog ID: 102," the server transfers the details to the device.

[0915] Step 8:

[0916] The device then displays the received information about the shelter animal to the user, visually presenting the animal's personality, size, age, and other details to the user, allowing the user to decide whether to contact the shelter.

[0917] In this way, the system can efficiently match rescued animals with users who want to keep pets, reducing the number of unhappy animals and increasing the number of happy homes.

[0918] Example 1

[0919] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0920] In modern society, the number of protected animals is increasing, and the needs of owners for these animals are becoming more diverse. Conventional methods have made it difficult to efficiently match protected animals with users' desired conditions, requiring a lot of time and effort. As a result, appropriate protected animals may not be handed over to owners promptly, and the welfare of the animals may decline. To solve this problem, a system that can efficiently match protected animals with users' desired conditions is needed.

[0921] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0922] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to keep a pet, means for constructing an AI model that matches protected animals with the user's desired conditions based on the information in the first and second databases, means for the user to input the desired conditions using a terminal and send the information to the server, means for identifying the most suitable protected animal based on the user's desired conditions using the AI ​​model, and means for notifying the user's terminal of information on the identified protected animal. This enables the user to quickly and efficiently find the protected animal that best suits their desired conditions.

[0923] "Shelter animals" are animals that have been rescued by animal shelters or rescue organizations and are looking for new homes.

[0924] A "database" is a collection of information structured in a particular format that can be easily accessed, managed, and updated.

[0925] An "AI model" is a computational model that uses artificial intelligence algorithms to analyze data and make predictions.

[0926] A "server" is a computer system that provides services to other computers (clients) over a network.

[0927] A "terminal" is a device that a user uses to communicate with a server and input or receive information, examples of which include a PC or smartphone.

[0928] "User" means an individual or organization using the System to search for protected animals.

[0929] "Desired conditions" are the specific characteristics or requirements a user desires in a new pet, including personality, size, age, etc.

[0930] The "Nearest Neighbors Algorithm" is a machine learning algorithm for searching for the nearest data point to a given data point.

[0931] "Matching" is the process of comparing information about protected animals contained in a first database with the user's desired conditions contained in a second database to identify a suitable pair.

[0932] "Notification" refers to the act of the server sending specific information to the user's terminal to inform the user.

[0933] The present invention relates to a system that efficiently matches protected animals with the desired conditions of users who want to keep pets. This system is implemented using a first database containing information on protected animals, a second database containing the desired conditions of users, and an AI model that matches protected animals with the desired conditions of users based on these databases.

[0934] The server stores information provided by the shelter (e.g., personality, size, age) in a first database, and stores desired conditions provided by the user (e.g., desired personality, size, age) in a second database. A database management system (e.g., MySQL, PostgreSQL) is used to store this data.

[0935] The user inputs their desired conditions using a device (e.g., PC or smartphone). For example, they input a desired condition such as "I'm looking for a calm, medium-sized pet under 3 years old." This information is sent from the device to the server, which then receives it.

[0936] The server builds an AI model based on the data from the first and second databases using the Nearest Neighbors algorithm, specifically using Python's scikit-learn library, to identify the protected animals that best meet the user's desired criteria.

[0937] The server then inputs the user's desired conditions into an AI model to identify the most suitable rescue animal. The AI ​​model performs matching based on specific characteristics (personality, size, age). For example, if User A requests a pet that is "gentle, medium-sized, and under three years old," this desired condition is registered in the second database. The server uses the AI ​​model to find the most suitable rescue animal based on these conditions. If the rescue animal identified by the server is ID: 102, the server notifies User A's device of this information.

[0938] Users can then use their devices to view detailed information about the identified rescued animal, such as its personality, size, and age, and contact the rescue facility after gaining a thorough understanding of the animal. This allows users to quickly and efficiently find the pet that best suits their needs.

[0939] This system is expected to reduce the time it takes for many rescued animals to be handed over to suitable owners, improving the welfare of the animals and the happiness of families looking to adopt a pet.

[0940] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0941] Step 1: Store protected animal information in the first database

[0942] The server collects information about protected animals provided by the shelters. The input is data such as personality, size, and age provided by the shelters. Specifically, the server reads data uploaded to the server in formats such as CSV files using the Python pandas library.

[0943] The server stores the read data in a first database. The output is information about protected animals neatly stored in the first database. A database management system such as MySQL is used.

[0944] Step 2: Store the user's preferences in a second database

[0945] The user uses the terminal to input desired conditions (e.g., personality, size, age). The input is data received through the user interface. Specifically, the user enters the desired conditions into a form on the web browser and presses the "Submit" button.

[0946] The terminal sends the entered desired conditions to the server, and the data is transferred to the server as an HTTP request.

[0947] The server stores the received desired conditions in a second database, and the output is information on the desired conditions stored in an orderly manner in the second database.

[0948] Step 3: Building an AI model

[0949] The server builds an AI model based on data from the first and second databases. The input is information about protected animals and the user's desired conditions. Specifically, the model is built using the Nearest Neighbors algorithm using Python's scikit-learn library. Data preprocessing, such as standardization and normalization, is also performed.

[0950] The server stores the constructed AI model, and the output is an AI model optimized for matching rescued animals with the user's desired conditions.

[0951] Step 4: Matching your desired conditions

[0952] The server inputs the user's desired conditions into the AI ​​model and identifies the most suitable animal for protection. The input is the user's desired conditions stored in a second database. Specifically, it uses the Python scikit-learn library to search for the most suitable animal for protection using the Nearest Neighbors algorithm.

[0953] The server obtains information about the identified protected animals, and outputs the IDs of the identified protected animals and their details.

[0954] Step 5: Notification of match results

[0955] The server notifies the user's device of the information about the identified protected animal. The input is detailed information about the identified protected animal. Specifically, the server generates a JSON response and sends it to the user's device as an HTTP response.

[0956] The terminal displays the received information, and the output is detailed information about the protected animals displayed in a format that can be viewed by the user.

[0957] Step 6: Confirm details and contact

[0958] The user uses the terminal to check the detailed information of the identified protected animal, and the input is the protected animal information sent from the server.

[0959] The user can contact the shelter based on the details. The output is that the user can contact the shelter and take specific action to adopt a new pet by clicking on a link on the device to view the shelter's contact information and contact them.

[0960] Through this process, users can efficiently find the rescued animal that is best suited to them, and the rescued animal can be quickly handed over to the right owner.

[0961] (Application example 1)

[0962] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0963] In modern society, matching users who want to keep protected animals with protected animals is not done efficiently. This results in long times until protected animals are handed over to suitable owners, which compromises the welfare of the animals, and makes it difficult for users who want to keep pets to find the right animal. In addition, handling requests in physical stores takes time and effort, so there is a need for more efficient matching.

[0964] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0965] In this invention, the server includes: means for constructing an AI model that matches protected animals with users' desired conditions based on information from a first database containing information about protected animals and a second database containing information expressing the desire to own a pet; means for the user to input their desired conditions using a smartphone application and identify the most suitable protected animal based on the AI ​​model; and means for providing detailed information about the identified protected animal to the user's smartphone. This allows users to input their desired conditions using their smartphone before visiting a physical store and to check in advance information about the most suitable protected animal. This allows protected animals to be quickly transferred to appropriate owners, improving the welfare of the animals and improving user convenience.

[0966] The "first database" refers to a database that stores information about the characteristics, size, and age of protected animals.

[0967] The "second database" is a database that stores information about the personality, size, and age of a pet desired by a user who wants to keep a pet.

[0968] The "AI model" is an artificial intelligence that matches protected animals with the user's desired conditions based on information from the first and second databases.

[0969] The "smartphone application" is application software that allows users to input their desired criteria and use an AI model to identify the most suitable protected animals.

[0970] "Detailed information on identified protected animals" refers to specific information on protected animals that the AI ​​model matches based on the user's desired conditions, and is provided through a smartphone application.

[0971] The system for implementing this invention is designed to efficiently match rescued animals with the requirements of users who want to keep pets.

[0972] The system includes the following main elements:

[0973] 1. First database: stores information about the characteristics, size, and age of the protected animals.

[0974] 2. A second database: stores information about the personality, size, and age of the user's desired pet.

[0975] 3. AI model: Matches protected animals with the user's desired conditions based on information from the first and second databases.

[0976] Users use a smartphone application to input their desired conditions. The detailed flow is explained below.

[0977] Explaining the program's processing in natural language

[0978] 1. Data Entry

[0979] Users launch a smartphone application and enter information about the pet they want, such as its personality, size, and age.

[0980] 2. Data Transmission

[0981] The smartphone application sends the desired conditions entered by the user to the server in JSON format using an HTTP POST request.

[0982] 3. Matching process

[0983] The server receives the request data containing the user's desired conditions and performs matching using an AI model based on the information from the first and second databases, which uses the Nearest Neighbors algorithm.

[0984] 4. Obtaining the results

[0985] The server uses an AI model to identify the most suitable protected animal and obtains its details.

[0986] 5. Data provision

[0987] The server provides detailed information about identified protected animals to the user's smartphone in real time.

[0988] Implementation example

[0989] For example, if a user wants a pet that is "gentle, medium-sized, and under three years old," they can enter these requirements into a smartphone application. The application then sends these requirements to a server, which uses an AI model to identify the most suitable rescue animal. Details of the identified rescue animal are then sent to the user's smartphone, allowing them to review the information before visiting a physical store.

[0990] Prompt Sentence Examples

[0991] "Based on data from a first database containing information on the characteristics, size, and age of rescued animals, and a second database containing the desired conditions of users looking to adopt a pet, please find the rescued animal that best meets the user's desired conditions. For example, if user A wants a pet that is 'gentle, medium-sized, and under 3 years old,' please identify the best rescued animal."

[0992] This system allows users to check information about the most suitable rescued animals before visiting a physical store, enabling efficient matching. The server uses a database and AI models to match the user's preferences with the characteristics of the rescued animals and find the optimal match.

[0993] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0994] Step 1:

[0995] Users launch a smartphone application and enter the desired pet's characteristics, size, age, and other criteria.

[0996] Input: Desired conditions entered by the user (personality, size, age)

[0997] Output: JSON format of input data

[0998] Specific operation: The user enters desired conditions into the input form of the smartphone application and presses the submit button. The application converts this data into JSON format.

[0999] Step 2:

[1000] The terminal transmits the input desired condition data to the server.

[1001] Input: User preferences in JSON format

[1002] Output: HTTP POST request to the server

[1003] Specific operation: The smartphone application sends JSON data containing the user's desired conditions to the server as an HTTP POST request.

[1004] Step 3:

[1005] The server analyzes the received desired condition data and stores it in a second database.

[1006] Input: JSON format data received by the server

[1007] Output: User preferences stored in a second database

[1008] Specific operation: The server receives the HTTP POST request, parses the data, and stores it in a second database.

[1009] Step 4:

[1010] The server uses an AI model based on the information from the first and second databases to identify the most suitable animals for protection.

[1011] Input: Protected animal information from the first database, user preferences from the second database

[1012] Output: Details of the best protected animals

[1013] How it works: The server uses an AI model (Nearest Neighbors algorithm) to match the protected animals that best meet the user's desired criteria.

[1014] Step 5:

[1015] The server obtains detailed information about the identified protected animals and provides it to the user's smartphone.

[1016] Input: Details of the best rescue animal

[1017] Output: Detailed information about protected animals sent to your smartphone

[1018] Specific operation: The server obtains detailed information about the identified protected animals and sends the data to the user's smartphone. The user's smartphone application receives a notification and displays the information.

[1019] Step 6:

[1020] Users can check detailed information on their smartphone application and prepare to visit the physical store.

[1021] Input: Details of the protected animal sent from the server

[1022] Output: Information on identified rescue animals and preparation for store visits

[1023] Specific actions: The user opens the smartphone application, checks the notification, views the details, and prepares to visit the physical store based on the information they view.

[1024] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1025] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system combines an emotion engine that recognizes the user's emotions to correct the user's desired conditions and make it possible to match more optimal rescued animals.

[1026] The system consists of a first database that stores information about protected animals provided by shelters, a second database that stores the desired conditions of users who want to keep pets, and an AI model that matches protected animals with the desired conditions of users based on these databases.In addition, by incorporating an emotion engine, the system can analyze the user's emotions and adjust the user's desired conditions based on the results.

[1027] Specifically, the server stores information about the characteristics, size, and age of rescued animals in a first database. For example, it reads information provided by rescue facilities in CSV file format and saves it in the database. A second database stores information about the characteristics, size, and age of pets desired by users.

[1028] The user uses the terminal to input desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," and then submits the input. The terminal then sends this information to the server.

[1029] The server builds an AI model based on the first and second databases, using the Nearest Neighbors algorithm to predict the best animal to rescue based on the user's desired conditions.

[1030] Next, the emotion engine analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page). As the user browses pet information, the emotion engine monitors their emotional state in real time and automatically adjusts their preferences accordingly if they become excited or anxious.

[1031] For example, if User A inputs "calm, medium-sized, under 3 years old" as their desired criteria, and the emotion engine detects that the user is particularly excited while browsing the information, the server will fine-tune the desired criteria based on this information and re-identify the most suitable protected animal. The server will then obtain the ID of the re-identified protected animal and notify the information to the user's device.

[1032] The device receives detailed information about the sheltered animal from the server and displays it to the user, allowing the user to decide whether to contact the shelter.

[1033] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion engine, matching can be done with consideration for the user's feelings.

[1034] The processing flow will be explained below.

[1035] Step 1:

[1036] The server loads information about the animals provided by the shelter as a first database. Specifically, it loads data about their personalities, sizes, and ages in CSV file format and stores the data in the database.

[1037] Step 2:

[1038] The server reads the desired pet's characteristics, size, and age, which are provided by the user as a second database, also in CSV file format, and stores the data in the database.

[1039] Step 3:

[1040] The server builds an AI model based on the information in the first and second databases. Specifically, it uses the Nearest Neighbors algorithm to generate a predictive model using features based on the protected animals and the user's desired conditions.

[1041] Step 4:

[1042] Users input their desired conditions, such as "personality: calm," "size: medium," and "age: under 3 years old," using a web browser or a dedicated application.

[1043] Step 5:

[1044] The terminal sends the input user's desired conditions to the server, and transfers the data to the server using a communication protocol (e.g., HTTP POST).

[1045] Step 6:

[1046] The server receives the user's desired conditions, inputs them into the AI ​​model, and identifies the most suitable protected animal. The AI ​​model then obtains the ID of the protected animal and temporarily stores its details.

[1047] Step 7:

[1048] The server runs an emotion engine that recognizes users' emotions in real time. The emotion engine collects data on users' operations and behavior while they browse web pages and applications, and estimates their emotional state. For example, it analyzes emotions from page viewing time, click frequency, and viewed content.

[1049] Step 8:

[1050] The emotion engine analyzes the user's emotional state and adjusts the user's preferences based on the analysis results. If the user is excited, it automatically adjusts the desired pet's personality, size, and other conditions. At the same time, the server re-inputs the adjusted preferences into the AI ​​model to re-identify the most suitable rescue animal.

[1051] Step 9:

[1052] The server notifies the user's device of the ID and detailed information of the re-identified protected animal based on the desired conditions corrected by the emotion engine.

[1053] Step 10:

[1054] The device then displays the received information about the shelter animal to the user, visually presenting the animal's characteristics, size, age, and other details to the user, allowing the user to decide whether to contact the shelter based on this information.

[1055] In this way, the system efficiently matches rescued animals with users who want to keep pets, and by using an emotion engine, it can achieve optimal matching that takes into account the emotional state of the user, thereby reducing the number of unhappy animals and increasing the number of happy homes.

[1056] Example 2

[1057] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1058] Conventional systems for matching rescued animals with users do not take into account the user's emotional state, making it difficult to recommend the most suitable rescued animal based on the user's true preferences. Furthermore, because the user's preferences are fixed, a variety of options are not offered, resulting in a decrease in the chances of a rescued animal being handed over to a suitable owner.

[1059] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1060] In this invention, the server includes a first database containing information on protected animals, a second database containing information expressing the desire to have a pet, means for constructing an AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, means including an emotion analysis engine that analyzes the user's emotional state based on the user's input method and browsing behavior, means for correcting the user's desired conditions based on the results of the emotion analysis engine, means for using the AI ​​model to identify the most suitable protected animal based on the corrected desired conditions, and means for providing the user with information on the identified protected animal. This enables flexible and optimal matching that takes the user's emotional state into consideration.

[1061] definition statement

[1062] The "first database" is a database that stores information about protected animals.

[1063] The "second database" is a database that stores information about the desired conditions of users who want to keep a pet.

[1064] The "artificial intelligence model" is a learning system that matches protected animals with the user's desired conditions based on information from the first database and the second database.

[1065] An "emotion analysis engine" is a system that analyzes a user's emotional state based on their input method and browsing behavior.

[1066] The "means for correcting the user's desired conditions" is a method for adjusting the desired conditions input by the user based on the results of the emotion analysis engine.

[1067] The "means for identifying the most suitable protected animal" is a method that uses an artificial intelligence model to select a protected animal that is suitable for the user based on the adjusted desired conditions.

[1068] "Means for providing information about protected animals" refers to a method for conveying detailed information about identified protected animals to users.

[1069] MODE FOR CARRYING OUT THE INVENTION

[1070] This invention relates to a system that efficiently matches rescued animals with the desired conditions of users who want to keep pets. In particular, this system is combined with an emotion analysis engine that recognizes the user's emotions, allowing it to correct the user's desired conditions and match them with more optimal rescued animals.

[1071] System Configuration

[1072] The system consists of the following elements:

[1073] 1. Database 1 (Protected Animal Information):

[1074] The first database contains information about the characteristics, size, and age of the animals in the shelter. Using the information provided by the shelter, a CSV file is read using the Python pandas library and stored in the database. For example, information such as "character: gentle," "size: small," and "age: 2 years old" is saved.

[1075] 2. Secondary database (user preference):

[1076] The second database contains information about the pet's personality, size, and age, which users input via their smartphone or computer. This information is also stored in the database and managed for each user.

[1077] 3. Artificial Intelligence Model:

[1078] Based on the information in the first and second databases, an artificial intelligence model is constructed to match rescued animals with the user's desired conditions. This model uses the Nearest Neighbors algorithm from the scikit-learn library. For example, it predicts the best rescued animals for the user's desired conditions.

[1079] 4. Sentiment Analysis Engine:

[1080] The emotion analysis engine analyzes a user's emotional state based on their input and browsing behavior. It uses Microsoft's Azure Emotion API. For example, it can monitor a user's emotional state in real time when viewing information about a specific protected animal, and obtain emotional information.

[1081] 5. How to adjust user preferences:

[1082] It includes a means for adjusting a user's preferences based on the results of the sentiment analysis engine. For example, if a user enters a preference of "calm, medium-sized, under 3 years old" but shows great excitement while viewing information about a particular pet, the preference will be adjusted to "small."

[1083] 6. How to identify the best shelter animals:

[1084] An artificial intelligence model is used to identify the best rescue animals based on the adjusted preferences, and then rematch based on preferences that reflect the user's emotional state.

[1085] 7. Means of providing information about protected animals:

[1086] Information about the identified rescued animals is provided to the user and displayed on the device, allowing the user to decide whether or not to contact the rescue facility.

[1087] Specific examples

[1088] For example, consider a situation where a user enters the following criteria into a smartphone app:

[1089] "I'm looking for a calm, medium-sized pet under the age of 3."

[1090] The system stores the user's desired criteria in a second database and uses an artificial intelligence model to identify the most suitable rescue animal. As the user browses information about a specific pet, an emotional analysis engine monitors the user's emotional state. If the user is overly agitated, the system reevaluates the criteria and performs a new match based on the revised criteria. As a result, the most suitable rescue animal information is re-identified and displayed on the user's device. The user can then contact the shelter using the provided information. This process streamlines the user's selection of the pet that is best for them.

[1091] This system is expected to increase the chances of rescued animals being quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes. In addition, by using an emotion analysis engine, matching can be done while taking into consideration the feelings of the user.

[1092] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1093] Program processing flow

[1094] Step 1: Store data in the first database

[1095] The server receives information about protected animals provided by the shelter in the form of a CSV file. It reads the received CSV file using the Python pandas library and stores the data in the first database. The input is the CSV file, and the output is the information about protected animals stored in the database. The specific operations include reading the CSV file, cleansing the data, and inserting it into the database.

[1096] Step 2: Store user preferences in a second database

[1097] The user uses a terminal to input the desired pet's conditions. For example, they input specific prompts such as "personality: gentle," "size: medium," and "age: under 3 years old." This input information is sent from the terminal to the server, which stores it in a second database. The input is the user's desired conditions, and the output is the user's desired conditions stored in the database. Specific operations include sending the input form, receiving the data, and inserting it into the database.

[1098] Step 3: Building an artificial intelligence model

[1099] The server retrieves data from the first and second databases and uses this data to build an AI model that matches protected animals with the user's desired conditions. In this example, the Nearest Neighbors algorithm from the scikit-learn library is used. The input is the data retrieved from the first and second databases, and the output is a trained AI model. Specific operations include data standardization, model training, and model saving.

[1100] Step 4: Identifying the best rescue animals

[1101] The server uses the constructed artificial intelligence model to identify the most suitable animal for rescue based on the user's desired conditions. The input is the user's desired conditions, and the output is information on the most suitable animal for rescue. Specific operations include prediction by the model and filtering of the results.

[1102] Step 5: Applying the sentiment analysis engine

[1103] When a user is browsing information about protected animals on their device, the server uses an emotion analysis engine to analyze the user's emotional state. This analysis is performed using Microsoft's Azure Emotion API. The input is the user's browsing behavior data, and the output is the user's emotional state. Specific operations include collecting the user's input method and browsing behavior data, calling the emotion analysis API, and obtaining emotional information.

[1104] Step 6: Adjust your preferences

[1105] The server corrects the user's desired conditions based on the results of the emotion analysis engine. For example, if the user inputs the desired conditions of "calm, medium size, under 3 years old," but becomes particularly excited while browsing, the desired conditions will be adjusted to "small." The input is the result of the emotion analysis, and the output is the corrected desired conditions. Specific operations include analyzing the emotion data and updating the desired conditions.

[1106] Step 7: Re-identify the best animals to protect

[1107] Based on the revised preferences, the server again uses the AI ​​model to identify the most suitable animal for protection. The input is the revised preferences, and the output is the updated information on the most suitable animal for protection. Specifically, the model again predicts and filters the results.

[1108] Step 8: Inform users

[1109] The server transmits detailed information about the identified protected animal to the user's terminal. The terminal displays the received information to the user. The input is the information about the re-identified protected animal, and the output is the details of the protected animal displayed on the terminal. Specific operations include transmitting and displaying the information.

[1110] (Application example 2)

[1111] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1112] Conventional matching systems for rescued animals and users looking to adopt pets simply search for a partner based on desired criteria, without taking into account the user's feelings, which often resulted in an inadequate match. Furthermore, users' true desires can change based on their emotions, so it's necessary to adjust the desired criteria. Furthermore, the system lacks functionality for providing users with detailed information about pets, making the process for users to view or inquire complicated.

[1113] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1114] In this invention, the server includes a first database containing information about protected animals, a second database containing information expressing the desire to own a pet, a means for constructing a generative AI model that matches protected animals with a user's desired conditions based on the information in the first and second databases, a means equipped with an emotion engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis, a means for identifying the most suitable protected animal based on the user's desired conditions using the generative AI model and the emotion engine, and a means for providing the user with information about the identified protected animal. This allows the desired conditions to be adjusted to reflect the user's emotions, resulting in more accurate matching. It also allows users to smoothly make inquiries and make reservations for tours.

[1115] The "first database" is a database for storing information on protected animals.

[1116] The "second database" is a database for storing the desired conditions of users who want to keep a pet.

[1117] A "generative AI model" is an artificial intelligence model that makes optimal matches based on the user's desired conditions and information about protected animals.

[1118] The "emotion engine" is an engine that analyzes the user's emotional state and adjusts the desired conditions based on the analysis results.

[1119] "Matching means" means a means that provides the functionality to identify the most suitable protected animal based on the user's desired criteria using a generative AI model.

[1120] "Means for providing" refers to means for providing the function of delivering information about identified protected animals to users.

[1121] To implement this invention, a system is constructed that uses a server, a user terminal, a first database, a second database, a generative AI model, and an emotion engine. Specific embodiments are described below.

[1122] First, the server has a first database containing information about rescued animals and a second database containing the desired conditions of users who want to keep a pet. The first database stores detailed information about rescued animals, such as their personality, size, and age. The second database stores information about the personality, size, and age of the pet desired by the user.

[1123] The server then uses this information to build a generative AI model that uses a Nearest Neighbors algorithm to match users' preferences with information about rescued animals.

[1124] The server also has an emotion engine that analyzes the user's emotional state. The emotion engine infers emotions based on the user's input method and browsing behavior (e.g., how much time they spend on each page, facial expression analysis, voice analysis, etc.). This emotion engine monitors the user's emotions in real time while they are browsing information about protected animals, and if the user becomes excited or anxious, it adjusts their desired conditions accordingly.

[1125] For example, if User A desires a "gentle, medium-sized, under 3-year-old" pet and shows particular excitement while browsing the information, the emotion engine will use this information to appropriately fine-tune the desired conditions. The server will then use the generative AI model again to identify the most suitable rescue animal based on these adjusted conditions. Information about the identified rescue animal is sent to the user's device, where detailed information, photos, and videos are displayed. Using this information, users can directly contact the shelter or make a reservation for a tour.

[1126] For example, if a user inputs the desired criteria of "gentle, medium-sized, under 3 years old" and the emotion engine predicts that it is "fun," the generative AI model will suggest the most suitable rescue animal based on these desired criteria. An example of a prompt is as follows:

[1127] "The user's desired conditions were entered as 'personality: gentle, medium size, age: under 3', and the user's emotion was estimated to be 'fun'. Please recommend the most suitable rescue animal based on these conditions."

[1128] This system allows users to adjust their desired conditions to reflect their feelings, resulting in more accurate matching. It also allows users to smoothly make inquiries and reservations for viewings, increasing the chances that rescued animals will be quickly handed over to suitable owners, reducing the number of unhappy animals and increasing the number of happy homes.

[1129] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1130] Step 1:

[1131] The user inputs desired conditions for the pet into the terminal, such as "personality, size, age," and presses the send button on the terminal.

[1132] Input: Personality: Gentle, Size: Medium, Age: Under 3 years old

[1133] Output: Desired condition data sent from the terminal to the server

[1134] Step 2:

[1135] The server receives the desired condition data transmitted from the terminal and stores the received data in a second database.

[1136] Input: Desired condition data sent from the terminal

[1137] Output: User preference data stored in a second database

[1138] Step 3:

[1139] The server retrieves information about the shelter animals from the first database, including the characteristics, size, age, etc. of the shelter animals.

[1140] Input: First database

[1141] Output: Protected animal information

[1142] Step 4:

[1143] The server reads the user's desired conditions from the second database and builds a generative AI model based on the protected animal information obtained from the first database, using the Nearest Neighbors algorithm to identify the most suitable protected animals.

[1144] Input: User's desired conditions, protected animal information from the first database

[1145] Output: Generated AI model, identifying the best animals to protect

[1146] Step 5:

[1147] The server uses an emotion engine to analyze the user's emotional state, inferring their emotions based on their input method and browsing behavior, and adjusts their desired conditions based on the results.

[1148] Input: User input methods and browsing behavior data

[1149] Output: Corrected user preferences

[1150] Step 6:

[1151] Based on the desired conditions corrected by the emotion engine, the server again uses the generative AI model to identify the most suitable protected animal. The protected animal is identified using a proximity search algorithm.

[1152] Input: Corrected user preferences

[1153] Output: Identification of the best animals to protect

[1154] Step 7:

[1155] The server sends detailed information about the identified protected animal to the user's device, including the animal's characteristics, size, age, photos, videos, etc.

[1156] Input: Information about the identified protected animal

[1157] Output: Details of the protected animal sent to the user's device

[1158] Step 8:

[1159] The user can view the detailed information about the protected animals received on their device, and after checking the information, make inquiries to the shelter or make reservations for a tour.

[1160] Input:Detailed information about protected animals

[1161] Output: Availability of inquiries and tour reservations

[1162] In this way, a system will be created that optimally matches protected animals with users, allowing users to smoothly make inquiries and make reservations for tours.

[1163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1164] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1165] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1166] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1167] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1168] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1169] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1170] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1171] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1173] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1174] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1176] 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.

[1177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1178] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1179] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1183] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1184] The following is further disclosed regarding the above embodiment.

[1185] (Claim 1)

[1186] a first database containing information about protected animals;

[1187] a second database containing information representing a desire to own a pet;

[1188] A means for constructing an AI model that matches protected animals with user desired conditions based on information from the first database and the second database;

[1189] A means for identifying the most suitable protected animal based on a user's desired criteria using the AI ​​model;

[1190] A means for providing a user with information about the identified protected animals;

[1191] A system including:

[1192] (Claim 2)

[1193] 10. The system of claim 1, wherein the first database includes information about the characteristics, size, and age of shelter animals.

[1194] (Claim 3)

[1195] 10. The system of claim 1, wherein the second database includes information regarding the personality, size, and age of the user's desired pet.

[1196] "Example 1"

[1197] (Claim 1)

[1198] a first database containing information about protected animals;

[1199] a second database containing information representing a desire to own a pet;

[1200] A means for constructing an AI model that matches protected animals with user desired conditions based on information from the first database and the second database;

[1201] A means for a user to input desired conditions using a terminal and transmit the information to a server;

[1202] A means for identifying the most suitable protected animal based on a user's desired criteria using the AI ​​model;

[1203] a means for notifying a user of information about the identified protected animal on a terminal of the user;

[1204] A system including:

[1205] (Claim 2)

[1206] 10. The system of claim 1, wherein the first database includes information about the characteristics, size, and age of shelter animals.

[1207] (Claim 3)

[1208] 10. The system of claim 1, wherein the second database includes information regarding the personality, size, and age of the user's desired pet.

[1209] "Application Example 1"

[1210] (Claim 1)

[1211] a first database containing information about protected animals;

[1212] a second database containing information representing a desire to own a pet;

[1213] A means for constructing an AI model that matches protected animals with user desired conditions based on information from the first database and the second database;

[1214] A means for a user to input their desired conditions using a smartphone application and identify the most suitable protected animal based on the AI ​​model;

[1215] A means for providing detailed information of the identified protected animal to a user's smartphone;

[1216] A system including:

[1217] (Claim 2)

[1218] 10. The system of claim 1, wherein the first database includes information about the characteristics, size, and age of shelter animals.

[1219] (Claim 3)

[1220] 10. The system of claim 1, wherein the second database includes information regarding the personality, size, and age of the user's desired pet.

[1221] "Example 2: Combining Emotion Engines"

[1222] Claims

[1223] (Claim 1)

[1224] a first database containing information about protected animals;

[1225] a second database containing information representing a desire to own a pet;

[1226] A means for constructing an artificial intelligence model that matches protected animals with user desired conditions based on information in the first database and the second database;

[1227] a means including an emotion analysis engine for analyzing the user's emotional state based on the user's input method and browsing behavior;

[1228] A means for correcting the user's desired conditions based on the result of the emotion analysis engine;

[1229] a means for identifying the most suitable animal for protection based on the revised preferences using the artificial intelligence model;

[1230] A means for providing a user with information about the identified protected animals;

[1231] A system including:

[1232] (Claim 2)

[1233] 10. The system of claim 1, wherein the first database includes information about the characteristics, size, and age of shelter animals.

[1234] (Claim 3)

[1235] 10. The system of claim 1, wherein the second database includes information regarding the personality, size, and age of the user's desired pet.

[1236] "Application example 2 when combining emotion engines"

[1237] (Claim 1)

[1238] a first database containing information about protected animals;

[1239] a second database containing information representing a desire to own a pet;

[1240] A means for constructing a generative AI model that matches protected animals with user desired conditions based on information from the first database and the second database;

[1241] means for analyzing the emotional state of a user and adjusting the desired conditions based on the analysis;

[1242] A means for identifying the most suitable rescue animal based on a user's desired criteria using the generative AI model and the emotion engine;

[1243] A means for providing a user with information about the identified protected animals;

[1244] A system including:

[1245] (Claim 2)

[1246] 10. The system of claim 1, wherein the first database includes information about the characteristics, size, and age of shelter animals.

[1247] (Claim 3)

[1248] 10. The system of claim 1, wherein the second database includes information regarding the personality, size, and age of the user's desired pet. [Explanation of symbols]

[1249] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a first database containing information about protected animals; a second database containing information representing a desire to own a pet; A means for constructing an AI model that matches protected animals with user desired conditions based on information in the first database and the second database; A means for identifying the most suitable protected animal based on a user's desired criteria using the AI ​​model; A means for providing a user with information about the identified protected animals; A system including:

2. 2. The system of claim 1, wherein the first database includes information regarding the characteristics, size, and age of shelter animals.

3. 2. The system of claim 1, wherein the second database includes information regarding the personality, size, and age of the user's desired pet.

Images