system

Abstract

The system according to this embodiment aims to achieve efficient operation by digitizing and centrally managing the health status and characteristics of animals. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a digitization unit, a judgment unit, and a notification unit. The collection unit collects animal health data and characteristics. The analysis unit analyzes the data collected by the collection unit. The digitization unit digitizes care information and behavioral characteristics based on the analysis results obtained by the analysis unit. The judgment unit determines the suitability of the animal to the prospective foster parent based on the information digitized by the digitization unit. The notification unit notifies the prospective foster parent based on the suitability determined by the judgment unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the management of the health status and care information of animals in animal protection facilities is decentralized, and there is a risk of information inconsistency and update delay.

[0005] The system according to the embodiment aims to digitize and centrally manage the health status and characteristics of animals to achieve efficient operation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a digitization unit, a judgment unit, and a notification unit. The collection unit collects animal health data and characteristics. The analysis unit analyzes the data collected by the collection unit. The digitization unit digitizes care information and behavioral characteristics based on the analysis results obtained by the analysis unit. The judgment unit determines the suitability of the animal to the prospective foster parent based on the information digitized by the digitization unit. The notification unit notifies the prospective foster parent based on the suitability determined by the judgment unit. [Effects of the Invention]

[0007] The system according to this embodiment can digitize and centrally manage the health status and characteristics of animals, enabling efficient operation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An animal shelter management system according to an embodiment of the present invention is a system that digitizes and centrally manages the health status and characteristics of animals. This system uses generative AI to analyze animal health data and analyze care information and behavioral characteristics. Based on the animal's health data, the generative AI quickly and accurately determines the suitability of the animal with potential adoptive families and notifies the candidates. For example, the animal shelter management system analyzes the medical history of a dog named "Buddy" and notifies the candidate of the vaccination and health check schedule. Furthermore, the animal shelter management system evaluates whether Buddy is compatible with other pets and whether he is suitable for a family with children, and provides a list of the most suitable adoptive families. As a result, the animal shelter management system can grasp and update information in real time, significantly reducing the burden of manual work. The automated matching process improves suitability with adoptive families and increases the well-being of the animals. In addition, the animal shelter management system strengthens the support system for the facility by supporting community formation involving adoptive families and volunteers, enabling continuous care and support. As a result, the animal shelter management system can centrally manage the health status and characteristics of animals and achieve efficient operation.

[0029] The animal shelter management system according to this embodiment comprises a collection unit, an analysis unit, a digitization unit, a decision unit, and a notification unit. The collection unit collects animal health data and characteristics. The collection unit can collect data such as animal weight, body temperature, behavioral patterns, and diet using sensors, for example. The collection unit can also accept manual data input. For example, the collection unit can collect health data manually entered by the caretaker. Furthermore, the collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions of the animal. For example, if the collection unit is stressed, it can delay the collection timing and wait until the animal relaxes. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the collected data using generative AI and analyze care information and behavioral characteristics. For example, the analysis unit inputs animal health data into the generative AI, which analyzes the data and outputs care information and behavioral characteristics. The digitization unit digitizes care information and behavioral characteristics based on the analysis results obtained by the analysis unit. The digitization unit can, for example, input data into a database or save it to cloud storage. The digitization unit can also digitize the analysis results using a generative AI. For example, the digitization unit inputs the analysis results into the generative AI, and the generative AI outputs the digitized information. The judgment unit determines the suitability of the foster parent candidate based on the information digitized by the digitization unit. The judgment unit can determine suitability based on criteria such as health status, behavioral characteristics, and foster parent candidate requirements. The judgment unit can also determine suitability based on the digitized information using a generative AI. For example, the judgment unit inputs the digitized information into the generative AI, and the generative AI determines and outputs the suitability. The notification unit notifies the foster parent candidate based on the suitability determined by the judgment unit. The notification unit can notify by methods such as email, app, or phone. The notification unit can also notify the foster parent candidate based on suitability using AI. For example, the notification unit receives compatibility information as input to the AI, and the AI ​​generates and outputs notification content.As a result, the animal shelter management system according to this embodiment can centrally manage animal health data and characteristics, enabling efficient operation.

[0030] The data collection unit collects animal health data and characteristics. For example, it can collect data such as animal weight, body temperature, behavioral patterns, and diet using sensors. Specifically, sensors attached to the animal's collar or cage periodically measure the animal's weight and body temperature, and transmit this data wirelessly to a central database. Behavioral pattern data is collected using motion sensors and cameras that track the animal's movements, recording how the animal moves around and how much time it spends on specific activities. Dietary data is collected using sensor-equipped food bowls that record the amount and type of food the animal eats. This allows for a detailed understanding of the animal's health status and behavioral characteristics. The data collection unit can also accept manual data input. For example, information obtained when a caretaker performs a health check on an animal can be entered through a dedicated application or web interface. This allows for the incorporation of detailed health information and specific observations that cannot be obtained by sensors into the system. Furthermore, the data collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions of the animal. For example, the data collection unit can use an AI algorithm to estimate stress levels from an animal's facial expressions and behavior, temporarily suspending data collection if the animal is stressed and waiting until the animal relaxes. This minimizes the burden on the animal while ensuring accurate data collection. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For instance, collected data can be stored on a cloud server, making it accessible to the analysis and digitization units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, it can statistically analyze collected weight and body temperature data to detect changes in the animal's health. For example, it can detect rapid weight changes or abnormal fluctuations in body temperature and assess whether these are signs of illness. It can also analyze behavioral pattern data to detect abnormalities in the animal's activity level or behavior. For example, lower-than-normal activity or frequent repetition of specific behaviors can be evaluated as signs of stress or health problems. The analysis unit can also use generative AI to analyze collected data and analyze care information and behavioral characteristics. For example, the analysis unit inputs animal health data into the generative AI, which analyzes the data and outputs care information and behavioral characteristics. The generative AI learns from historical data and data from other animals to provide insights into the animal's health and behavioral characteristics. For example, the generative AI can suggest optimal care methods for specific health conditions or identify causes of stress based on the animal's behavioral patterns. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term health trends and changes in behavioral patterns. For example, based on past health data, it can predict health risks under specific seasons and environmental conditions and plan preventative care. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0032] The Digitalization Department digitizes care information and behavioral characteristics based on the analysis results obtained by the Analysis Department. The Digitalization Department can, for example, input data into a database or save it to cloud storage. Specifically, it inputs analysis results into a database and creates organized health and behavioral records for each animal. This allows caretakers and managers to grasp the animal's health status and behavioral characteristics at a glance. The Digitalization Department can also digitize analysis results using generative AI. For example, the Digitalization Department inputs analysis results into the generative AI, which then outputs the digitized information. The generative AI can describe the analysis results in natural language and output them in report format. This allows the analysis results to be provided in an easily understandable format. Furthermore, the Digitalization Department can save the digitized information to cloud storage and share it with other systems and departments as needed. For example, digitized health and behavioral records are stored in cloud storage, making them accessible to caretakers and managers at any time. The Digitalization Department can also automatically generate reports on the animal's health status and behavioral characteristics based on the digitized information. This allows caretakers and managers to regularly check the animals' health and behavioral characteristics and take necessary care and measures. Through these functions, the digitalization department can efficiently manage animal health data and behavioral characteristics and support the operation of the entire system.

[0033] The decision-making unit determines the compatibility between an animal and a potential foster parent based on information digitized by the digitalization unit. The decision-making unit can determine compatibility based on criteria such as health status, behavioral characteristics, and the foster parent's qualifications. Specifically, it evaluates the animal's health status and behavioral characteristics and determines compatibility by comparing them with the environment and care capabilities that the foster parent can provide. For example, animals with certain health conditions may require special care, and the decision-making unit prioritizes selecting foster parent candidates who can provide that care. It also evaluates whether the animal is likely to adapt to a new environment based on its behavioral characteristics and selects appropriate foster parent candidates. The decision-making unit can also use generative AI to determine compatibility based on digitized information. For example, the decision-making unit inputs digitized information into the generative AI, which then determines and outputs the compatibility. The generative AI learns from past data and compatibility evaluations of other animals, enabling it to evaluate the compatibility between animals and foster parent candidates with high accuracy. This allows the decision-making unit to quickly and accurately determine the compatibility between animals and foster parent candidates, achieving optimal matching. Furthermore, the decision-making unit can store the compatibility evaluation results in a database for future evaluation and analysis. This allows the decision-making unit to continuously improve the conformity assessment process, thereby enhancing the overall reliability and efficiency of the system.

[0034] The notification unit notifies prospective adopters based on their suitability as determined by the decision-making unit. The notification unit can deliver notifications via methods such as email, app notifications, or phone calls. Specifically, it notifies prospective adopters deemed highly suitable with information about the animal's health, behavioral characteristics, and suitability as an adopter. The notification may include photos and videos of the animal, health records, and detailed behavioral characteristics. This allows prospective adopters to understand detailed information about the animal in advance and make an appropriate decision. The notification unit can also use AI to notify prospective adopters based on their suitability. For example, the notification unit inputs suitability information into the AI, which then generates and outputs notification content. The AI ​​can generate optimal notification content based on the prospective adopter's preferences and conditions, providing individually customized notifications. This allows the notification unit to provide prospective adopters with appropriate information quickly and effectively. Furthermore, the notification unit can store notification history in a database and analyze the effectiveness and responses to notifications. This allows the notification unit to improve notification content and methods. For example, the system analyzes notification open rates and response rates to identify the most effective notification methods and content. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For instance, it can use not only email notifications but also app notifications and phone notifications to ensure important information is delivered reliably. This allows the notification unit to provide information to potential adoptive parents quickly and reliably, efficiently supporting the search for adoptive homes for animals.

[0035] The data collection unit can analyze an animal's past health history and select the optimal data collection method. For example, based on its past health history, the data collection unit can avoid certain tests if the animal is sensitive to them. For example, based on its past health history, the data collection unit can collect data during times when the animal is relaxed. For example, based on its past health history, the data collection unit can avoid certain environments if the animal experiences stress in those environments. This enables efficient data collection by selecting the optimal data collection method based on past health history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animal's past health history data into a generating AI and have the generating AI select the optimal data collection method.

[0036] The data collection unit can filter the collected health data based on the animal's current living environment and behavioral patterns. For example, if the animal is active outdoors, the data collection unit can prioritize collecting outdoor data. For example, if the animal is resting indoors, the data collection unit can prioritize collecting indoor data. For example, if the animal exhibits a specific behavioral pattern, the data collection unit can filter the data based on that behavioral pattern. This allows for the collection of highly relevant data by filtering the data based on the animal's living environment and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal behavioral pattern data into a generating AI and have the generating AI perform data filtering.

[0037] The data collection unit can prioritize the collection of highly relevant data by considering the animal's geographical location when collecting health data. For example, if the animal is in a specific area, the data collection unit can prioritize the collection of environmental data for that area. For example, if the animal is on the move, the data collection unit can prioritize the collection of data along the travel route. For example, if the animal is in a specific facility, the data collection unit can prioritize the collection of environmental data for that facility. This allows for the collection of highly relevant data by considering the animal's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animal's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0038] The data collection unit can analyze the social media activity of animal owners and collect relevant data when collecting health data. For example, if an owner posts about the animal's health status on social media, the data collection unit can collect that information. For example, if an owner posts about the animal's behavioral patterns on social media, the data collection unit can collect that information. For example, if an owner posts about the animal's diet or exercise on social media, the data collection unit can collect that information. This allows for the collection of relevant health data by analyzing the owner's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's social media data into a generating AI and have the generating AI collect the relevant data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis for important health data. For example, the analysis unit can perform a simplified analysis for general health data. For example, the analysis unit can perform a rapid, detailed analysis for urgent health data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the animal species and age during analysis. For example, in the case of young animals, the analysis unit can focus on data related to growth. For example, in the case of older animals, the analysis unit can focus on data related to aging. For example, in the case of a specific species of animal, the analysis unit can focus on health problems specific to that species. By applying analysis algorithms appropriate to the animal species and age, appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal species and age data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0041] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis. For example, the analysis unit can prioritize the analysis of recently collected health data. For example, the analysis unit can analyze current data while referring to past health data. For example, the analysis unit can prioritize the analysis of data with high urgency. This enables efficient analysis by determining the priority of analysis based on the timing of health data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0043] The digitization unit can adjust the level of detail of the digitization based on the importance of the analysis results. For example, the digitization unit can perform detailed digitization for important analysis results. For example, the digitization unit can perform simplified digitization for general analysis results. For example, the digitization unit can perform rapid, detailed digitization for urgent analysis results. This enables efficient digitization by adjusting the level of detail of the digitization based on the importance of the analysis results. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the digitization.

[0044] The digitization unit can apply different digitization algorithms depending on the animal species and age during the digitization process. For example, in the case of young animals, the digitization unit can prioritize data related to growth during digitization. For example, in the case of older animals, the digitization unit can prioritize data related to aging during digitization. For example, in the case of a specific species of animal, the digitization unit can prioritize health problems specific to that species during digitization. This allows for appropriate digitization by applying digitization algorithms appropriate to the animal species and age. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input animal species and age data into a generating AI and have the generating AI execute the application of different digitization algorithms.

[0045] The digitization unit can adjust the order of digitization based on the timing of analysis result collection. For example, the digitization unit can prioritize the digitization of recently collected analysis results. For example, the digitization unit can digitize current analysis results while referring to past analysis results. For example, the digitization unit can prioritize the digitization of analysis results of high urgency. This enables efficient digitization by adjusting the order of digitization based on the timing of analysis result collection. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the timing of analysis result collection into a generating AI and have the generating AI perform the adjustment of the digitization order.

[0046] The digitization unit can adjust the order of digitization based on the relevance of the analysis results during the digitization process. For example, the digitization unit can prioritize the digitization of highly relevant analysis results. For example, the digitization unit can postpone the digitization of less relevant analysis results. For example, the digitization unit can dynamically adjust the order of digitization based on the relevance of the analysis results. This enables efficient digitization by adjusting the order of digitization based on the relevance of the analysis results. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the relevance of the analysis results into a generating AI and have the generating AI perform the adjustment of the digitization order.

[0047] The decision-making unit can adjust the level of detail in its decision based on the importance of the digitized information. For example, the decision-making unit can make a detailed decision in the case of important digitized information. For example, the decision-making unit can make a simplified decision in the case of general digitized information. For example, the decision-making unit can make a quick and detailed decision in the case of urgent digitized information. This allows for efficient decision-making by adjusting the level of detail in the decision based on the importance of the digitized information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the importance of the digitized information into a generating AI and have the generating AI perform the adjustment of the level of detail in the decision.

[0048] The decision unit can apply different decision algorithms depending on the animal species and age when making a decision. For example, in the case of young animals, the decision unit can prioritize data related to growth. For example, in the case of older animals, the decision unit can prioritize data related to aging. For example, in the case of a specific species of animal, the decision unit can prioritize health problems specific to that species. This makes it possible to make an appropriate decision by applying a decision algorithm appropriate to the animal species and age. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input animal species and age data into a generating AI and have the generating AI execute the application of different decision algorithms.

[0049] The decision-making unit can adjust the order of decisions based on when the digitized information was collected. For example, the decision-making unit can prioritize recently collected digitized information. For example, the decision-making unit can make decisions on current information while referring to past digitized information. For example, the decision-making unit can prioritize digitized information of high urgency. This allows for efficient decision-making by adjusting the order of decisions based on when the digitized information was collected. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the collection dates of the digitized information into a generating AI and have the generating AI perform the adjustment of the decision order.

[0050] The decision-making unit can adjust the order of decisions based on the relationships between digitized information when making a decision. For example, the decision-making unit can prioritize decisions based on the relationships between digitized information. For example, the decision-making unit can postpone decisions based on the relationships between digitized information. For example, the decision-making unit can dynamically adjust the order of decisions based on the relationships between digitized information. This enables efficient decision-making by adjusting the order of decisions based on the relationships between digitized information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the relationships between digitized information into a generating AI and have the generating AI perform the adjustment of the order of decisions.

[0051] The notification unit can adjust the level of detail of the notification based on the importance of the conformance. For example, the notification unit can provide a detailed notification for important conformance information. For example, the notification unit can provide a simplified notification for general conformance information. For example, the notification unit can provide a rapid and detailed notification for urgent conformance information. This enables efficient notification by adjusting the level of detail of the notification based on the importance of the conformance. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the importance of the conformance into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0052] The notification unit can apply different notification algorithms depending on the attribute information of the prospective foster parent when sending a notification. For example, the notification unit can send a notification in a casual tone to a young prospective foster parent. For example, the notification unit can send a notification in a polite tone to an elderly prospective foster parent. For example, the notification unit can send a notification appropriate to a prospective foster parent who has a specific attribute. This makes it possible to send appropriate notifications by applying a notification algorithm that matches the prospective foster parent's attribute information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the prospective foster parent's attribute information into a generating AI and have the generating AI execute the application of different notification algorithms.

[0053] The notification unit can adjust the order of notifications based on the timing of the conformity assessment. For example, the notification unit can prioritize notifying recently assessed conformity information. For example, the notification unit can notify current information while referring to past conformity information. For example, the notification unit can prioritize notifying highly urgent conformity information. This allows for efficient notification by adjusting the order of notifications based on the timing of the conformity assessment. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the timing of the conformity assessment into a generating AI and have the generating AI perform the adjustment of the notification order.

[0054] The notification unit can adjust the order of notifications based on the relevance of the relevant information. For example, the notification unit can prioritize notifying highly relevant relevant information. For example, the notification unit can postpone notifying less relevant relevant information. For example, the notification unit can dynamically adjust the order of notifications based on the relevance of the relevant information. This enables efficient notification by adjusting the order of notifications based on the relevance of the relevant information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the relevant information into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The animal shelter management system may also include a section for evaluating the social behavior of animals. This section observes and collects data on how animals interact with other animals and humans. For example, it can assess how often animals play with other animals and whether they enjoy contact with their caretakers. This section can also observe how animals adapt to new environments and assess their adaptability. This allows for the selection of more suitable foster families based on the animals' social behavior.

[0057] The animal shelter management system may also include a section for managing the animals' diets. This section proposes an optimal diet plan based on the animals' health data. For example, if an animal requires a specific nutrient, it can propose a diet containing that nutrient. This section can also manage the animals' weight and suggest appropriate portion sizes. Furthermore, it can record the animals' dietary history and adjust the diet plan based on past data. This enables proper dietary management to maintain the animals' health.

[0058] The animal shelter management system may also include a section for managing animal exercise. This section proposes an optimal exercise plan based on the animals' health data. For example, if an animal is not getting enough exercise, it can suggest an appropriate amount of exercise. Furthermore, this section can record the animals' exercise history and adjust the exercise plan based on past data. It can also assess whether an animal prefers a particular type of exercise and propose an exercise plan based on that preference. This enables appropriate exercise management to maintain the animals' health.

[0059] The animal shelter management system can also include a section that provides preventive care based on animal health data. This section analyzes animal health data and predicts future health risks. For example, if an animal is at high risk of contracting a particular disease, it can propose measures to prevent that disease. Furthermore, this section can regularly monitor the animals' health and take early action if any abnormalities are detected. This allows for the prediction of animal health risks and the provision of preventive care.

[0060] The animal shelter management system may also include a section that provides rehabilitation plans based on animal health data. This section analyzes the animal's health data and proposes the most suitable plan when rehabilitation is needed. For example, if an animal is injured, it can propose a rehabilitation plan to promote recovery from that injury. Furthermore, this section can record the animal's rehabilitation history and adjust the plan based on past data. This allows for the provision of appropriate rehabilitation plans to restore the animal's health.

[0061] The animal shelter management system may also include a department that provides long-term health management plans based on animal health data. This department analyzes animal health data and proposes long-term health management plans. For example, if an animal has a specific health risk, it can propose a long-term plan to manage that risk. This department can also regularly monitor the animals' health status and adjust the plan accordingly. This allows for the provision of an appropriate plan for the long-term management of animal health.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects animal health data and characteristics. The data collection unit can use sensors to collect data such as the animal's weight, body temperature, behavioral patterns, and diet. It can also accept manual data input, allowing the unit to collect health data manually entered by the owner. Furthermore, the data collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the animal is stressed, the data collection timing can be delayed, waiting until the animal relaxes. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. Furthermore, it can also analyze the collected data using generative AI to analyze care information and behavioral characteristics. For example, animal health data can be input into the generative AI, and the generative AI will analyze the data and output care information and behavioral characteristics. Step 3: The digitization unit digitizes care information and behavioral characteristics based on the analysis results obtained by the analysis unit. The digitization unit can input data into a database or save it to cloud storage. Furthermore, it can also digitize the analysis results using a generation AI. For example, the analysis results can be input into the generation AI, and the generation AI can output the digitized information. Step 4: The judgment unit determines the suitability of the foster parent candidate based on the information digitized by the digitization unit. The judgment unit can determine suitability based on criteria such as health status, behavioral characteristics, and the foster parent candidate's qualifications. Furthermore, it can also determine suitability based on the digitized information using a generating AI. For example, the digitized information is input to the generating AI, and the generating AI determines and outputs the suitability. Step 5: The notification unit notifies prospective foster parents based on the suitability determined by the judgment unit. The notification unit can send notifications via email, app, or phone. Furthermore, it can also use AI to notify prospective foster parents based on suitability. For example, suitability information can be input into the AI, and the AI ​​can generate and output the notification content.

[0064] (Example of form 2) An animal shelter management system according to an embodiment of the present invention is a system that digitizes and centrally manages the health status and characteristics of animals. This system uses generative AI to analyze animal health data and analyze care information and behavioral characteristics. Based on the animal's health data, the generative AI quickly and accurately determines the suitability of the animal with potential adoptive families and notifies the candidates. For example, the animal shelter management system analyzes the medical history of a dog named "Buddy" and notifies the candidate of the vaccination and health check schedule. Furthermore, the animal shelter management system evaluates whether Buddy is compatible with other pets and whether he is suitable for a family with children, and provides a list of the most suitable adoptive families. As a result, the animal shelter management system can grasp and update information in real time, significantly reducing the burden of manual work. The automated matching process improves suitability with adoptive families and increases the well-being of the animals. In addition, the animal shelter management system strengthens the support system for the facility by supporting community formation involving adoptive families and volunteers, enabling continuous care and support. As a result, the animal shelter management system can centrally manage the health status and characteristics of animals and achieve efficient operation.

[0065] The animal shelter management system according to this embodiment comprises a collection unit, an analysis unit, a digitization unit, a decision unit, and a notification unit. The collection unit collects animal health data and characteristics. The collection unit can collect data such as animal weight, body temperature, behavioral patterns, and diet using sensors, for example. The collection unit can also accept manual data input. For example, the collection unit can collect health data manually entered by the caretaker. Furthermore, the collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions of the animal. For example, if the collection unit is stressed, it can delay the collection timing and wait until the animal relaxes. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis or machine learning algorithms, for example. The analysis unit can also analyze the collected data using generative AI and analyze care information and behavioral characteristics. For example, the analysis unit inputs animal health data into the generative AI, which analyzes the data and outputs care information and behavioral characteristics. The digitization unit digitizes care information and behavioral characteristics based on the analysis results obtained by the analysis unit. The digitization unit can, for example, input data into a database or save it to cloud storage. The digitization unit can also digitize the analysis results using a generative AI. For example, the digitization unit inputs the analysis results into the generative AI, and the generative AI outputs the digitized information. The judgment unit determines the suitability of the foster parent candidate based on the information digitized by the digitization unit. The judgment unit can determine suitability based on criteria such as health status, behavioral characteristics, and foster parent candidate requirements. The judgment unit can also determine suitability based on the digitized information using a generative AI. For example, the judgment unit inputs the digitized information into the generative AI, and the generative AI determines and outputs the suitability. The notification unit notifies the foster parent candidate based on the suitability determined by the judgment unit. The notification unit can notify by methods such as email, app, or phone. The notification unit can also notify the foster parent candidate based on suitability using AI. For example, the notification unit receives compatibility information as input to the AI, and the AI ​​generates and outputs notification content.As a result, the animal shelter management system according to this embodiment can centrally manage animal health data and characteristics, enabling efficient operation.

[0066] The data collection unit collects animal health data and characteristics. For example, it can collect data such as animal weight, body temperature, behavioral patterns, and diet using sensors. Specifically, sensors attached to the animal's collar or cage periodically measure the animal's weight and body temperature, and transmit this data wirelessly to a central database. Behavioral pattern data is collected using motion sensors and cameras that track the animal's movements, recording how the animal moves around and how much time it spends on specific activities. Dietary data is collected using sensor-equipped food bowls that record the amount and type of food the animal eats. This allows for a detailed understanding of the animal's health status and behavioral characteristics. The data collection unit can also accept manual data input. For example, information obtained when a caretaker performs a health check on an animal can be entered through a dedicated application or web interface. This allows for the incorporation of detailed health information and specific observations that cannot be obtained by sensors into the system. Furthermore, the data collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions of the animal. For example, the data collection unit can use an AI algorithm to estimate stress levels from an animal's facial expressions and behavior, temporarily suspending data collection if the animal is stressed and waiting until the animal relaxes. This minimizes the burden on the animal while ensuring accurate data collection. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For instance, collected data can be stored on a cloud server, making it accessible to the analysis and digitization units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, it can statistically analyze collected weight and body temperature data to detect changes in the animal's health. For example, it can detect rapid weight changes or abnormal fluctuations in body temperature and assess whether these are signs of illness. It can also analyze behavioral pattern data to detect abnormalities in the animal's activity level or behavior. For example, lower-than-normal activity or frequent repetition of specific behaviors can be evaluated as signs of stress or health problems. The analysis unit can also use generative AI to analyze collected data and analyze care information and behavioral characteristics. For example, the analysis unit inputs animal health data into the generative AI, which analyzes the data and outputs care information and behavioral characteristics. The generative AI learns from historical data and data from other animals to provide insights into the animal's health and behavioral characteristics. For example, the generative AI can suggest optimal care methods for specific health conditions or identify causes of stress based on the animal's behavioral patterns. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term health trends and changes in behavioral patterns. For example, based on past health data, it can predict health risks under specific seasons and environmental conditions and plan preventative care. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only monitor the situation in real time but also to handle long-term health management and anomaly detection, improving the overall reliability and safety of the system.

[0068] The Digitalization Department digitizes care information and behavioral characteristics based on the analysis results obtained by the Analysis Department. The Digitalization Department can, for example, input data into a database or save it to cloud storage. Specifically, it inputs analysis results into a database and creates organized health and behavioral records for each animal. This allows caretakers and managers to grasp the animal's health status and behavioral characteristics at a glance. The Digitalization Department can also digitize analysis results using generative AI. For example, the Digitalization Department inputs analysis results into the generative AI, which then outputs the digitized information. The generative AI can describe the analysis results in natural language and output them in report format. This allows the analysis results to be provided in an easily understandable format. Furthermore, the Digitalization Department can save the digitized information to cloud storage and share it with other systems and departments as needed. For example, digitized health and behavioral records are stored in cloud storage, making them accessible to caretakers and managers at any time. The Digitalization Department can also automatically generate reports on the animal's health status and behavioral characteristics based on the digitized information. This allows caretakers and managers to regularly check the animals' health and behavioral characteristics and take necessary care and measures. Through these functions, the digitalization department can efficiently manage animal health data and behavioral characteristics and support the operation of the entire system.

[0069] The decision-making unit determines the compatibility between an animal and a potential foster parent based on information digitized by the digitalization unit. The decision-making unit can determine compatibility based on criteria such as health status, behavioral characteristics, and the foster parent's qualifications. Specifically, it evaluates the animal's health status and behavioral characteristics and determines compatibility by comparing them with the environment and care capabilities that the foster parent can provide. For example, animals with certain health conditions may require special care, and the decision-making unit prioritizes selecting foster parent candidates who can provide that care. It also evaluates whether the animal is likely to adapt to a new environment based on its behavioral characteristics and selects appropriate foster parent candidates. The decision-making unit can also use generative AI to determine compatibility based on digitized information. For example, the decision-making unit inputs digitized information into the generative AI, which then determines and outputs the compatibility. The generative AI learns from past data and compatibility evaluations of other animals, enabling it to evaluate the compatibility between animals and foster parent candidates with high accuracy. This allows the decision-making unit to quickly and accurately determine the compatibility between animals and foster parent candidates, achieving optimal matching. Furthermore, the decision-making unit can store the compatibility evaluation results in a database for future evaluation and analysis. This allows the decision-making unit to continuously improve the conformity assessment process, thereby enhancing the overall reliability and efficiency of the system.

[0070] The notification unit notifies prospective adopters based on their suitability as determined by the decision-making unit. The notification unit can deliver notifications via methods such as email, app notifications, or phone calls. Specifically, it notifies prospective adopters deemed highly suitable with information about the animal's health, behavioral characteristics, and suitability as an adopter. The notification may include photos and videos of the animal, health records, and detailed behavioral characteristics. This allows prospective adopters to understand detailed information about the animal in advance and make an appropriate decision. The notification unit can also use AI to notify prospective adopters based on their suitability. For example, the notification unit inputs suitability information into the AI, which then generates and outputs notification content. The AI ​​can generate optimal notification content based on the prospective adopter's preferences and conditions, providing individually customized notifications. This allows the notification unit to provide prospective adopters with appropriate information quickly and effectively. Furthermore, the notification unit can store notification history in a database and analyze the effectiveness and responses to notifications. This allows the notification unit to improve notification content and methods. For example, the system analyzes notification open rates and response rates to identify the most effective notification methods and content. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For instance, it can use not only email notifications but also app notifications and phone notifications to ensure important information is delivered reliably. This allows the notification unit to provide information to potential adoptive parents quickly and reliably, efficiently supporting the search for adoptive homes for animals.

[0071] The data collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the animal is stressed, the data collection unit can delay the collection timing and wait until the animal relaxes. For example, if the animal is relaxed, the data collection unit can immediately collect health data and obtain accurate data. For example, if the animal is excited, the data collection unit can adjust the collection timing and wait until the animal calms down. This allows for accurate data to be obtained by adjusting the collection timing according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input animal emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The data collection unit can analyze an animal's past health history and select the optimal data collection method. For example, based on its past health history, the data collection unit can avoid certain tests if the animal is sensitive to them. For example, based on its past health history, the data collection unit can collect data during times when the animal is relaxed. For example, based on its past health history, the data collection unit can avoid certain environments if the animal experiences stress in those environments. This enables efficient data collection by selecting the optimal data collection method based on past health history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animal's past health history data into a generating AI and have the generating AI select the optimal data collection method.

[0073] The data collection unit can filter the collected health data based on the animal's current living environment and behavioral patterns. For example, if the animal is active outdoors, the data collection unit can prioritize collecting outdoor data. For example, if the animal is resting indoors, the data collection unit can prioritize collecting indoor data. For example, if the animal exhibits a specific behavioral pattern, the data collection unit can filter the data based on that behavioral pattern. This allows for the collection of highly relevant data by filtering the data based on the animal's living environment and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal behavioral pattern data into a generating AI and have the generating AI perform data filtering.

[0074] The data collection unit can estimate the animal's emotions and determine the priority of health data to collect based on the estimated animal's emotions. For example, if the animal is stressed, the data collection unit can prioritize the collection of stress-related data. For example, if the animal is relaxed, the data collection unit can prioritize the collection of general health data. For example, if the animal is excited, the data collection unit can prioritize the collection of data related to the state of excitement. This allows for the priority collection of important data by prioritizing health data according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input animal emotion data into a generative AI and have the generative AI determine the priority of health data.

[0075] The data collection unit can prioritize the collection of highly relevant data by considering the animal's geographical location when collecting health data. For example, if the animal is in a specific area, the data collection unit can prioritize the collection of environmental data for that area. For example, if the animal is on the move, the data collection unit can prioritize the collection of data along the travel route. For example, if the animal is in a specific facility, the data collection unit can prioritize the collection of environmental data for that facility. This allows for the collection of highly relevant data by considering the animal's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the animal's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0076] The data collection unit can analyze the social media activity of animal owners and collect relevant data when collecting health data. For example, if an owner posts about the animal's health status on social media, the data collection unit can collect that information. For example, if an owner posts about the animal's behavioral patterns on social media, the data collection unit can collect that information. For example, if an owner posts about the animal's diet or exercise on social media, the data collection unit can collect that information. This allows for the collection of relevant health data by analyzing the owner's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the owner's social media data into a generating AI and have the generating AI collect the relevant data.

[0077] The analysis unit can estimate the animal's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the animal is stressed, the analysis unit can highlight stress-related data. For example, if the animal is relaxed, the analysis unit can display general health data in detail. For example, if the animal is excited, the analysis unit can highlight data related to the state of excitement. This allows for the provision of appropriate analysis results by adjusting the presentation of the analysis according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can perform a detailed analysis for important health data. For example, the analysis unit can perform a simplified analysis for general health data. For example, the analysis unit can perform a rapid, detailed analysis for urgent health data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the animal species and age during analysis. For example, in the case of young animals, the analysis unit can focus on data related to growth. For example, in the case of older animals, the analysis unit can focus on data related to aging. For example, in the case of a specific species of animal, the analysis unit can focus on health problems specific to that species. By applying analysis algorithms appropriate to the animal species and age, appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal species and age data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0080] The analysis unit can estimate the animal's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the animal is stressed, the analysis unit can complete the analysis in a short time. For example, if the animal is relaxed, the analysis unit can perform a detailed analysis. For example, if the animal is excited, the analysis unit can perform a rapid analysis. By adjusting the length of the analysis according to the animal's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input animal emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0081] The analysis unit can determine the priority of analysis based on the timing of health data collection during the analysis. For example, the analysis unit can prioritize the analysis of recently collected health data. For example, the analysis unit can analyze current data while referring to past health data. For example, the analysis unit can prioritize the analysis of data with high urgency. This enables efficient analysis by determining the priority of analysis based on the timing of health data collection. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of health data collection into a generating AI and have the generating AI determine the priority of analysis.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the health data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This enables efficient analysis by adjusting the order of analysis based on the relevance of the health data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the health data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0083] The digitization unit can estimate the animal's emotions and adjust the digitization method based on the estimated animal's emotions. For example, if the animal is stressed, the digitization unit can highlight stress-related data during digitization. For example, if the animal is relaxed, the digitization unit can digitize general health data in detail. For example, if the animal is excited, the digitization unit can highlight data related to the state of excitement during digitization. This allows for appropriate digitization by adjusting the digitization method according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input animal emotion data into a generative AI and have the generative AI adjust the digitization method.

[0084] The digitization unit can adjust the level of detail of the digitization based on the importance of the analysis results. For example, the digitization unit can perform detailed digitization for important analysis results. For example, the digitization unit can perform simplified digitization for general analysis results. For example, the digitization unit can perform rapid, detailed digitization for urgent analysis results. This enables efficient digitization by adjusting the level of detail of the digitization based on the importance of the analysis results. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the importance of the analysis results into a generating AI and have the generating AI perform the adjustment of the level of detail of the digitization.

[0085] The digitization unit can apply different digitization algorithms depending on the animal species and age during the digitization process. For example, in the case of young animals, the digitization unit can prioritize data related to growth during digitization. For example, in the case of older animals, the digitization unit can prioritize data related to aging during digitization. For example, in the case of a specific species of animal, the digitization unit can prioritize health problems specific to that species during digitization. This allows for appropriate digitization by applying digitization algorithms appropriate to the animal species and age. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input animal species and age data into a generating AI and have the generating AI execute the application of different digitization algorithms.

[0086] The digitization unit can estimate the animal's emotions and determine the priority of digitization based on the estimated animal's emotions. For example, if the animal is stressed, the digitization unit can prioritize the digitization of stress-related data. For example, if the animal is relaxed, the digitization unit can prioritize the digitization of general health data. For example, if the animal is excited, the digitization unit can prioritize the digitization of data related to the state of excitement. This allows important data to be digitized preferentially by determining the priority of digitization according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input animal emotion data into a generative AI and have the generative AI determine the priority of digitization.

[0087] The digitization unit can adjust the order of digitization based on the timing of analysis result collection. For example, the digitization unit can prioritize the digitization of recently collected analysis results. For example, the digitization unit can digitize current analysis results while referring to past analysis results. For example, the digitization unit can prioritize the digitization of analysis results of high urgency. This enables efficient digitization by adjusting the order of digitization based on the timing of analysis result collection. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the timing of analysis result collection into a generating AI and have the generating AI perform the adjustment of the digitization order.

[0088] The digitization unit can adjust the order of digitization based on the relevance of the analysis results during the digitization process. For example, the digitization unit can prioritize the digitization of highly relevant analysis results. For example, the digitization unit can postpone the digitization of less relevant analysis results. For example, the digitization unit can dynamically adjust the order of digitization based on the relevance of the analysis results. This enables efficient digitization by adjusting the order of digitization based on the relevance of the analysis results. Some or all of the above-described processes in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the relevance of the analysis results into a generating AI and have the generating AI perform the adjustment of the digitization order.

[0089] The decision unit can estimate the animal's emotions and adjust the suitability criteria based on the estimated animal's emotions. For example, if the animal is stressed, the decision unit can prioritize stress-related data when determining suitability. For example, if the animal is relaxed, the decision unit can prioritize general health data when determining suitability. For example, if the animal is excited, the decision unit can prioritize data related to the state of excitement when determining suitability. This allows for appropriate judgment by adjusting the suitability criteria according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input animal emotion data into a generative AI and have the generative AI adjust the suitability criteria.

[0090] The decision-making unit can adjust the level of detail in its decision based on the importance of the digitized information. For example, the decision-making unit can make a detailed decision in the case of important digitized information. For example, the decision-making unit can make a simplified decision in the case of general digitized information. For example, the decision-making unit can make a quick and detailed decision in the case of urgent digitized information. This allows for efficient decision-making by adjusting the level of detail in the decision based on the importance of the digitized information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the importance of the digitized information into a generating AI and have the generating AI perform the adjustment of the level of detail in the decision.

[0091] The decision unit can apply different decision algorithms depending on the animal species and age when making a decision. For example, in the case of young animals, the decision unit can prioritize data related to growth. For example, in the case of older animals, the decision unit can prioritize data related to aging. For example, in the case of a specific species of animal, the decision unit can prioritize health problems specific to that species. This makes it possible to make an appropriate decision by applying a decision algorithm appropriate to the animal species and age. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input animal species and age data into a generating AI and have the generating AI execute the application of different decision algorithms.

[0092] The decision unit can estimate the animal's emotions and determine suitability priorities based on the estimated animal's emotions. For example, if the animal is stressed, the decision unit can prioritize stress-related data. For example, if the animal is relaxed, the decision unit can prioritize general health data. For example, if the animal is excited, the decision unit can prioritize data related to the state of excitement. This allows for prioritization of important suitability by determining suitability priorities according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input animal emotion data into a generative AI and have the generative AI perform the determination of suitability priorities.

[0093] The decision-making unit can adjust the order of decisions based on when the digitized information was collected. For example, the decision-making unit can prioritize recently collected digitized information. For example, the decision-making unit can make decisions on current information while referring to past digitized information. For example, the decision-making unit can prioritize digitized information of high urgency. This allows for efficient decision-making by adjusting the order of decisions based on when the digitized information was collected. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the collection dates of the digitized information into a generating AI and have the generating AI perform the adjustment of the decision order.

[0094] The decision-making unit can adjust the order of decisions based on the relationships between digitized information when making a decision. For example, the decision-making unit can prioritize decisions based on the relationships between digitized information. For example, the decision-making unit can postpone decisions based on the relationships between digitized information. For example, the decision-making unit can dynamically adjust the order of decisions based on the relationships between digitized information. This enables efficient decision-making by adjusting the order of decisions based on the relationships between digitized information. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the relationships between digitized information into a generating AI and have the generating AI perform the adjustment of the order of decisions.

[0095] The notification unit can estimate the animal's emotions and adjust the notification method based on the estimated emotions. For example, if the animal is stressed, the notification unit can send a notification in a calm tone. For example, if the animal is relaxed, the notification unit can send a notification in a bright tone. For example, if the animal is excited, the notification unit can send a quick and concise notification. This allows for appropriate notifications by adjusting the notification method according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI, or not using AI. For example, the notification unit can input animal emotion data into a generative AI and have the generative AI adjust the notification method.

[0096] The notification unit can adjust the level of detail of the notification based on the importance of the conformance. For example, the notification unit can provide a detailed notification for important conformance information. For example, the notification unit can provide a simplified notification for general conformance information. For example, the notification unit can provide a rapid and detailed notification for urgent conformance information. This enables efficient notification by adjusting the level of detail of the notification based on the importance of the conformance. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the importance of the conformance into a generating AI and have the generating AI perform the adjustment of the level of detail of the notification.

[0097] The notification unit can apply different notification algorithms depending on the attribute information of the prospective foster parent when sending a notification. For example, the notification unit can send a notification in a casual tone to a young prospective foster parent. For example, the notification unit can send a notification in a polite tone to an elderly prospective foster parent. For example, the notification unit can send a notification appropriate to a prospective foster parent who has a specific attribute. This makes it possible to send appropriate notifications by applying a notification algorithm that matches the prospective foster parent's attribute information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the prospective foster parent's attribute information into a generating AI and have the generating AI execute the application of different notification algorithms.

[0098] The notification unit can estimate the animal's emotions and determine the priority of notifications based on the estimated emotions. For example, if the animal is stressed, the notification unit can prioritize stress-related notifications. For example, if the animal is relaxed, the notification unit can prioritize general notifications. For example, if the animal is excited, the notification unit can prioritize notifications related to the state of excitement. This allows important notifications to be prioritized by determining the priority of notifications according to the animal's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI. For example, the notification unit can input animal emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0099] The notification unit can adjust the order of notifications based on the timing of the conformity assessment. For example, the notification unit can prioritize notifying recently assessed conformity information. For example, the notification unit can notify current information while referring to past conformity information. For example, the notification unit can prioritize notifying highly urgent conformity information. This allows for efficient notification by adjusting the order of notifications based on the timing of the conformity assessment. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the timing of the conformity assessment into a generating AI and have the generating AI perform the adjustment of the notification order.

[0100] The notification unit can adjust the order of notifications based on the relevance of the relevant information. For example, the notification unit can prioritize notifying highly relevant relevant information. For example, the notification unit can postpone notifying less relevant relevant information. For example, the notification unit can dynamically adjust the order of notifications based on the relevance of the relevant information. This enables efficient notification by adjusting the order of notifications based on the relevance of the relevant information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the relevance of the relevant information into a generating AI and have the generating AI perform the adjustment of the order of notifications.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The animal shelter management system may also include a section for evaluating the social behavior of animals. This section observes and collects data on how animals interact with other animals and humans. For example, it can assess how often animals play with other animals and whether they enjoy contact with their caretakers. This section can also observe how animals adapt to new environments and assess their adaptability. This allows for the selection of more suitable foster families based on the animals' social behavior.

[0103] The animal shelter management system may also include a section that estimates the animals' emotions and manages their stress levels based on those estimates. This section suggests measures to reduce stress if an animal is experiencing it. For example, it could provide a relaxing environment or play specific stress-reducing music. Furthermore, if an animal is relaxed, this section can suggest ways to maintain that state. This allows for the management of the animals' stress levels and the maintenance of their health.

[0104] The animal shelter management system may also include a section for managing the animals' diets. This section proposes an optimal diet plan based on the animals' health data. For example, if an animal requires a specific nutrient, it can propose a diet containing that nutrient. This section can also manage the animals' weight and suggest appropriate portion sizes. Furthermore, it can record the animals' dietary history and adjust the diet plan based on past data. This enables proper dietary management to maintain the animals' health.

[0105] The animal shelter management system may also include a section for managing animal exercise. This section proposes an optimal exercise plan based on the animals' health data. For example, if an animal is not getting enough exercise, it can suggest an appropriate amount of exercise. Furthermore, this section can record the animals' exercise history and adjust the exercise plan based on past data. It can also assess whether an animal prefers a particular type of exercise and propose an exercise plan based on that preference. This enables appropriate exercise management to maintain the animals' health.

[0106] The animal shelter management system may also include a unit that estimates the animals' emotions and predicts their behavior based on those emotions. This unit predicts how animals will behave in specific situations and suggests appropriate countermeasures. For example, if an animal is stressed, it may exhibit aggressive behavior, and the system can suggest ways to avoid that situation. Similarly, if an animal is relaxed, it may exhibit friendly behavior, and the system can suggest ways to take advantage of that situation. This allows for the prediction of animal behavior and the implementation of appropriate countermeasures.

[0107] The animal shelter management system can also include a section that provides preventive care based on animal health data. This section analyzes animal health data and predicts future health risks. For example, if an animal is at high risk of contracting a particular disease, it can propose measures to prevent that disease. Furthermore, this section can regularly monitor the animals' health and take early action if any abnormalities are detected. This allows for the prediction of animal health risks and the provision of preventive care.

[0108] The animal shelter management system may also include a section that estimates the animals' emotions and adjusts their care plans based on those estimates. This section proposes care plans to reduce stress if the animal is experiencing it. For example, if the animal is stressed, it may suggest providing a relaxing environment or specific activities to reduce stress. Furthermore, if the animal is relaxed, this section may propose care plans to maintain that state. This allows for the provision of care plans tailored to the animals' emotions.

[0109] The animal shelter management system may also include a section that provides rehabilitation plans based on animal health data. This section analyzes the animal's health data and proposes the most suitable plan when rehabilitation is needed. For example, if an animal is injured, it can propose a rehabilitation plan to promote recovery from that injury. Furthermore, this section can record the animal's rehabilitation history and adjust the plan based on past data. This allows for the provision of appropriate rehabilitation plans to restore the animal's health.

[0110] The animal shelter management system may further include a section that estimates the emotions of animals and provides socialization programs based on those estimated emotions. This section observes how animals interact with other animals and humans and proposes socialization programs. For example, if an animal is stressed, it can propose a socialization program to reduce that stress. It can also propose a socialization program to maintain a relaxed state if the animal is relaxed. This allows for the provision of socialization programs tailored to the emotions of the animals.

[0111] The animal shelter management system may also include a department that provides long-term health management plans based on animal health data. This department analyzes animal health data and proposes long-term health management plans. For example, if an animal has a specific health risk, it can propose a long-term plan to manage that risk. This department can also regularly monitor the animals' health status and adjust the plan accordingly. This allows for the provision of an appropriate plan for the long-term management of animal health.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The data collection unit collects animal health data and characteristics. The data collection unit can use sensors to collect data such as the animal's weight, body temperature, behavioral patterns, and diet. It can also accept manual data input, allowing the unit to collect health data manually entered by the owner. Furthermore, the data collection unit can estimate the animal's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the animal is stressed, the data collection timing can be delayed, waiting until the animal relaxes. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using statistical analysis and machine learning algorithms. Furthermore, it can also analyze the collected data using generative AI to analyze care information and behavioral characteristics. For example, animal health data can be input into the generative AI, and the generative AI will analyze the data and output care information and behavioral characteristics. Step 3: The digitization unit digitizes care information and behavioral characteristics based on the analysis results obtained by the analysis unit. The digitization unit can input data into a database or save it to cloud storage. Furthermore, it can also digitize the analysis results using a generation AI. For example, the analysis results can be input into the generation AI, and the generation AI can output the digitized information. Step 4: The judgment unit determines the suitability of the foster parent candidate based on the information digitized by the digitization unit. The judgment unit can determine suitability based on criteria such as health status, behavioral characteristics, and the foster parent candidate's qualifications. Furthermore, it can also determine suitability based on the digitized information using a generating AI. For example, the digitized information is input to the generating AI, and the generating AI determines and outputs the suitability. Step 5: The notification unit notifies prospective foster parents based on the suitability determined by the judgment unit. The notification unit can send notifications via email, app, or phone. Furthermore, it can also use AI to notify prospective foster parents based on suitability. For example, suitability information can be input into the AI, and the AI ​​can generate and output the notification content.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the collection unit, analysis unit, digitization unit, decision unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects animal health data using the sensors and manual input device of the smart device 14. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The digitization unit is implemented by the identification processing unit 290 of the data processing unit 12 and digitizes the analysis results. The decision unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines suitability based on the digitized information. The notification unit notifies prospective adoptive parents using the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the collection unit, analysis unit, digitization unit, judgment unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects animal health data using the sensors and manual input device of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The digitization unit is implemented by the identification processing unit 290 of the data processing unit 12 and digitizes the analysis results. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines suitability based on the digitized information. The notification unit notifies prospective adopters using the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, digitization unit, judgment unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects animal health data using the sensors and manual input device of the headset terminal 314. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The digitization unit is implemented by the identification processing unit 290 of the data processing unit 12 and digitizes the analysis results. The judgment unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines suitability based on the digitized information. The notification unit notifies prospective foster parents using the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, digitization unit, decision unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects animal health data using the robot 414's sensors and manual input device. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data. The digitization unit is implemented by the identification processing unit 290 of the data processing unit 12 and digitizes the analysis results. The decision unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines suitability based on the digitized information. The notification unit notifies prospective adoptive parents using the robot 414's communication I / F 44. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] 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.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) A collection unit that collects animal health data and characteristics, An analysis unit analyzes the data collected by the aforementioned collection unit, A digitization unit that digitizes care information and behavioral characteristics based on the analysis results obtained by the aforementioned analysis unit, A judgment unit that determines the suitability of a foster parent candidate based on the information digitized by the aforementioned digitization unit, A notification unit that notifies prospective foster parents based on the suitability determined by the aforementioned judgment unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is We estimate the emotions of animals and adjust the timing of health data collection based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze the animal's past health history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is When collecting health data, filtering is performed based on the animal's current living environment and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is We estimate the emotions of animals and prioritize the health data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is When collecting health data, the collection of highly relevant data is prioritized by considering the animals' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting health data, we analyze the social media activity of pet owners and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the emotions of animals and adjust the representation of the analysis based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the animal species and age. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the animal's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the health data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned digitization unit, It estimates the emotions of animals and adjusts the digitization method based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned digitization unit, During digitization, adjust the level of detail in the digitization based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned digitization unit, When digitizing an animal, different digitization algorithms are applied depending on the animal's species and age. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned digitization unit, Estimate animal emotions and determine digitization priorities based on the estimated animal emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned digitization unit, During digitization, adjust the digitization order based on when the analysis results were collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned digitization unit, During digitization, the order of digitization is adjusted based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The unit that makes the determination said, The system estimates the emotions of animals and adjusts the suitability criteria based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The unit that makes the determination said, When making a decision, adjust the level of detail based on the importance of the digitized information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The unit that makes the determination said, When making a decision, different decision algorithms are applied depending on the type and age of the animal. The system described in Appendix 1, characterized by the features described herein. (Note 23) The unit that makes the determination said, It estimates the emotions of animals and determines suitability priorities based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The unit that makes the determination said, When making decisions, adjust the order of decisions based on when the digitized information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The unit that makes the determination said, When making decisions, the order of decisions is adjusted based on the relevance of the digitized information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the animal's emotions and adjusts the notification method based on the estimated animal's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending a notification, adjust the level of detail based on the importance of the relevance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the attribute information of the prospective foster parent. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the animal's emotions and prioritizes notifications based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When notifying, adjust the order of notifications based on when the conformity assessment was made. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the suitability. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects animal health data and characteristics, An analysis unit analyzes the data collected by the aforementioned collection unit, A digitization unit that digitizes care information and behavioral characteristics based on the analysis results obtained by the aforementioned analysis unit, A judgment unit that determines the suitability of a foster parent candidate based on the information digitized by the aforementioned digitization unit, A notification unit that notifies prospective foster parents based on the suitability determined by the aforementioned judgment unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is We estimate the emotions of animals and adjust the timing of health data collection based on the estimated emotions of the animals. The system according to feature 1.

3. The aforementioned collection unit is Analyze the animal's past health history and select the optimal collection method. The system according to feature 1.

4. The aforementioned collection unit is When collecting health data, filtering is performed based on the animal's current living environment and behavioral patterns. The system according to feature 1.

5. The aforementioned collection unit is The system estimates the emotions of animals and prioritizes the health data to collect based on the estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is When collecting health data, the collection of highly relevant data is prioritized by considering the animals' geographical location. The system according to feature 1.

7. The aforementioned collection unit is When collecting health data, we analyze the social media activity of pet owners and collect relevant data. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the emotions of animals and adjust the representation of the analysis based on the estimated emotions of the animals. The system according to feature 1.

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