system

Abstract

The system according to this embodiment aims to accurately understand animal sounds and behaviors and to facilitate communication. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, and a verbalization unit. The acquisition unit acquires animal sounds and behaviors as audio data and visual data. The analysis unit analyzes the data acquired by the acquisition unit and combines it with various behavioral information about the internet and animals and thinking by LLM. The verbalization unit verbalizes the results analyzed by the analysis 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately understand the cries and behaviors of animals and to communicate with them.

[0005] The system according to the embodiment aims to accurately understand the cries and behaviors of animals and to communicate with them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, and a verbalization unit. The acquisition unit acquires animal sounds and behaviors as audio data and visual data. The analysis unit analyzes the data acquired by the acquisition unit and combines it with various behavioral information about the internet and animals, as well as thinking based on LLM. The verbalization unit verbalizes the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately understand animal sounds and behaviors and communicate with them. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The animal communication system according to an embodiment of the present invention is a system that achieves highly accurate communication with animals by utilizing AI to acquire animal sounds and behaviors as audio and visual data, processing and analyzing the data using an AI agent that combines various behavioral information from the internet and animals with thinking processes based on an LLM (Large-Scale Language Model), and then verbalizing the results. This animal communication system acquires animal sounds and behaviors as audio and visual data. Next, the acquired data is processed by an AI agent, which analyzes the data by combining various behavioral information from the internet and animals with thinking processes based on an LLM. Finally, the analysis results are verbalized, enabling communication with animals. For example, the animal communication system aims to enter the pet market as a highly accurate communication tool with pets. It will enable communication with pets, understanding situations when abnormalities or anomalies occur, and risk avoidance. It also aims to enter the education and zoo markets as a learning tool for zoos and schools to learn more deeply about animal behavior. Furthermore, it aims to be used as an auxiliary tool in animal medical settings and animal research. In the future, it aims to accumulate animal communication data in the generating AI and become a tool that can contribute to supporting biological research. Specifically, we will expand data accumulation and analysis, broaden the range of target animals, and enable its use as support for learning and medical care. Furthermore, we will deepen its accuracy as a support tool for animal research, contributing to the discovery of new laws in the animal kingdom and the conservation of endangered species. Through this, the animal communication system will be able to achieve communication with animals by accurately acquiring, analyzing, and verbalizing animal vocalizations and behaviors.

[0029] The animal communication system according to this embodiment comprises an acquisition unit, an analysis unit, and a language processing unit. The acquisition unit acquires animal sounds and behaviors as audio data and visual data. For example, the acquisition unit collects animal sounds with a microphone and stores them as audio data. The acquisition unit can also photograph animal behaviors with a camera and store them as visual data. For example, the acquisition unit collects animal sounds with a high-sensitivity microphone and acquires clear audio data using noise reduction technology. Furthermore, the acquisition unit can photograph animal behaviors with a high-resolution camera to capture details of their movements. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, when collecting animal sounds, the acquisition unit can use AI to remove noise and acquire clear audio data. The analysis unit analyzes the data acquired by the acquisition unit and combines various behavioral information about animals from the internet with thinking by LLM. For example, the analysis unit analyzes animal sound data using voice analysis technology to estimate the animal's emotions and intentions. Furthermore, the analysis unit can analyze animal behavior data using image analysis technology to identify animal behavior patterns. For example, the analysis unit can analyze the frequency components of animal vocalization data using sound analysis technology to estimate the animal's emotions. In addition, the analysis unit can extract movement features from animal behavior data using image analysis technology to identify behavior patterns. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal vocalization data into AI, which can then estimate the emotions. The language processing unit verbalizes the results analyzed by the analysis unit. For example, the language processing unit can express the animal's emotions and intentions in natural language and provide it to the user. The language processing unit can also explain animal behavior patterns in sentences and provide them in a way that is easy for the user to understand. For example, the language processing unit can express the animal's emotions with words such as "happy" or "sad." Furthermore, the language processing unit can explain animal behavior patterns with sentences such as "this animal frequently performs this action."Some or all of the processing described above in the language processing unit may be performed using AI or not. For example, the language processing unit can input the analysis results into the AI, which can then express them in natural language. As a result, the animal communication system according to this embodiment can achieve communication with animals by acquiring, analyzing, and verbalizing animal sounds and behaviors with high accuracy.

[0030] The acquisition unit acquires animal sounds and behaviors as audio and visual data. For example, the acquisition unit can collect animal sounds with a microphone and save them as audio data. The acquisition unit can also photograph animal behaviors with a camera and save them as visual data. Specifically, the acquisition unit collects animal sounds using a high-sensitivity microphone and acquires clear audio data using noise reduction technology. This eliminates ambient noise and allows for accurate capture of animal sounds. Furthermore, the acquisition unit can photograph animal behaviors using a high-resolution camera to capture details of their movements. For example, it can record subtle movements and changes in facial expressions when an animal performs a specific action in high resolution. This allows for detailed observation of animal behavior and is useful for later analysis. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, when collecting animal sounds, the acquisition unit can use AI to remove noise and acquire clear audio data. The AI ​​analyzes the collected audio data in real time and filters out unnecessary noise, making the animal sounds clearer. Furthermore, the acquisition unit can use AI to extract movement characteristics in real time when filming animal behavior, automatically detecting important actions. This allows the acquisition unit to acquire animal vocalizations and behaviors with high accuracy and efficiently collect data necessary for subsequent analysis. In addition, the acquisition unit can centrally manage the collected data and link with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and language processing units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the acquisition unit can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis unit analyzes the data acquired by the acquisition unit, combining various behavioral information about the internet and animals with thinking based on LLM. Specifically, it analyzes animal vocal data using speech analysis technology to estimate the animal's emotions and intentions. For example, by decomposing animal vocal data into frequency components and analyzing the intensity and patterns in specific frequency bands, it is possible to estimate what kind of emotions the animal is feeling. Furthermore, it is also possible to analyze animal behavior data using image analysis technology to identify animal behavior patterns. For example, by extracting the characteristics of animal movements and identifying specific behavior patterns, it is possible to estimate what kind of intentions the animal is feeling. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input animal vocal data into AI, and the AI ​​can estimate emotions. Based on past data and trained models, the AI ​​analyzes the characteristics of animal vocals and estimates emotions and intentions with high accuracy. It is also possible to input animal behavior data into AI and have the AI ​​identify behavior patterns. The AI ​​analyzes animal movements using image recognition technology and identifies specific behavior patterns to estimate the animal's intentions. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can analyze the behavioral patterns of specific animals based on past behavioral data and predict future behavior. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The language processing unit verbalizes the results analyzed by the analysis unit. Specifically, it expresses the animal's emotions and intentions in natural language and provides them to the user. For example, it can express an animal's emotions using words such as "happy" or "sad." This allows the user to intuitively understand the animal's emotions. The language processing unit can also explain the animal's behavior patterns in sentences and provide them to the user in an easy-to-understand format. For example, by explaining it with sentences such as "This animal frequently performs this action," the user can more easily grasp the animal's behavior patterns. Some or all of the above processing in the language processing unit may be performed using AI or not. For example, the language processing unit can input the analysis results into AI, which can then express them in natural language. Based on the analysis results, the AI ​​expresses the animal's emotions and behavior patterns in natural language and provides them to the user. The language processing unit can also collect user feedback and continuously improve the accuracy and effectiveness of the verbalization. For example, based on user feedback, it can review how to express the animal's emotions and behavior patterns and improve them to be easier to understand. Furthermore, the language processing unit can support multiple languages, accommodating users who speak different languages. This allows the language processing unit to provide users with easily understandable information about animals' emotions and behavioral patterns, thereby supporting communication with animals.

[0033] The animal communication system includes a storage unit that stores animal behavior data. The storage unit, for example, collects animal behavior data over a long period and stores it in a database. The storage unit can also periodically collect animal behavior data and store it in the database. For example, the storage unit records the animal's eating patterns daily and stores them in the database. Furthermore, the storage unit can periodically record the animal's sleep patterns and store them in the database. Some or all of the above processing in the storage unit may be performed using AI, or not. For example, the storage unit can input animal behavior data into the AI, which can then organize and store the data. This allows for long-term data analysis by accumulating animal behavior data.

[0034] The animal communication system includes a delivery unit that provides analysis results to the user. The delivery unit, for example, displays the analysis results on the user's device. The delivery unit can also provide analysis results to the user through a web application or mobile application. For example, the delivery unit can display the analysis results on a smartphone application, allowing the user to view them in real time. Furthermore, the delivery unit can also send the analysis results to the user via email. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the analysis results into an AI, which can then display them to the user in a format optimized for that user. This allows the user to understand the animal's condition by providing them with the analysis results.

[0035] The acquisition unit can estimate the animal's emotions and adjust the timing of acquiring vocalizations and behaviors based on the estimated emotions. For example, if the animal is excited, the acquisition unit can increase the frequency of vocalization acquisition and acquire behavioral changes in real time. If the animal is relaxed, the acquisition unit can also decrease the frequency of vocalization acquisition and acquire longer-term behavioral patterns. For example, if the animal is stressed, the acquisition unit will refrain from acquiring vocalizations and focus on acquiring behavioral changes. By adjusting the acquisition timing according to the animal's emotions, more accurate data can be obtained. 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 acquisition unit may be performed using AI or not. For example, the acquisition unit can input animal vocalizations and behavioral data into a generative AI to estimate the animal's emotions, and the generative AI can estimate the emotions.

[0036] The data acquisition unit can analyze the animal's past behavioral history and select the optimal data acquisition method. For example, the data acquisition unit can focus on acquiring animal vocalizations that occur during specific time periods, based on past behavioral history. The data acquisition unit can also focus on acquiring visual data during time periods when specific behavioral patterns are observed, based on past behavioral history. For example, the data acquisition unit can analyze past behavioral history and prioritize acquiring behavioral data under specific environmental conditions. This enables efficient data acquisition by selecting the optimal data acquisition method based on past behavioral history. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input past animal behavioral history data into a generating AI, which can then select the optimal data acquisition method.

[0037] The acquisition unit can filter the acquired sounds and behaviors based on the animal species and individual differences. For example, the acquisition unit can prioritize the acquisition of sounds specific to a particular animal species. The acquisition unit can also focus on acquiring data from animals with specific behavioral patterns based on individual differences. For example, the acquisition unit can adjust the frequency band of the sounds acquired according to the animal species. This allows for the acquisition of more accurate data by filtering according to the animal species and individual differences. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input sound and behavior data into a generating AI based on the animal species and individual differences, and the generating AI can perform the filtering.

[0038] The data acquisition unit can estimate the animal's emotions and determine the priority of data to acquire based on the estimated animal's emotions. For example, if the animal is excited, the data acquisition unit may prioritize acquiring vocal data. If the animal is relaxed, the data acquisition unit may also prioritize acquiring behavioral data. For example, if the animal is stressed, the data acquisition unit may prioritize acquiring environmental data. This allows for the priority acquisition of important data by determining the data priority 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 may be, 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 acquisition unit may be performed using AI or not. For example, the data acquisition unit can input animal vocal and behavioral data into a generative AI to estimate the animal's emotions, and the generative AI can estimate the emotions.

[0039] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring sounds and behaviors, taking into account information about the animal's habitat. For example, if the animal is outdoors, the acquisition unit prioritizes acquiring sound data, including ambient sounds. If the animal is indoors, the acquisition unit can also prioritize acquiring behavioral data. For example, if the animal is under specific environmental conditions, the acquisition unit prioritizes acquiring data related to that environment. This allows for the priority acquisition of highly relevant data by considering information about the habitat. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without AI. For example, the acquisition unit can input information about the animal's habitat into a generating AI, which can then prioritize the acquisition of highly relevant data.

[0040] The acquisition unit analyzes the animal's health condition when acquiring vocalizations and behavioral data, and can acquire specific data if an abnormality is detected. For example, if the animal's health condition is poor, the acquisition unit may focus on acquiring vocalization data. If the animal's health condition is good, the acquisition unit may focus on acquiring behavioral data. For example, if there is an abnormality in the animal's health condition, the acquisition unit may focus on acquiring environmental data. This allows for the acquisition of specific data when an abnormality is detected by analyzing the health condition. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without AI. For example, the acquisition unit can input animal health condition data into a generating AI, which can detect abnormalities and acquire specific data.

[0041] The analysis unit can estimate the animal's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the animal is excited, the analysis unit can apply an algorithm for rapid analysis. If the animal is relaxed, the analysis unit can also apply an algorithm for detailed analysis. For example, if the animal is stressed, the analysis unit can apply an algorithm to identify the stressors. This allows for a more accurate analysis by adjusting the analysis algorithm according to the animal's emotions. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal emotion data into a generating AI, which can then adjust the analysis algorithm.

[0042] The analysis unit can adjust the level of detail of the analysis based on the animal's behavioral patterns. For example, if the animal's behavioral patterns are complex, the analysis unit will perform a detailed analysis. If the animal's behavioral patterns are simple, the analysis unit can also perform a simplified analysis. For example, if a change is observed in the animal's behavioral patterns, the analysis unit will perform a detailed analysis to identify the factors causing the change. By adjusting the level of detail of the analysis based on the behavioral patterns, efficient analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input animal behavioral pattern data into a generating AI, which can then adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analytical methods depending on the animal species and individual differences during analysis. For example, the analysis unit can apply methods to analyze behavioral patterns specific to a particular animal species. The analysis unit can also apply different analytical methods based on individual differences. For example, the analysis unit can select the optimal analytical method depending on the animal species. This allows for more accurate analysis by applying analytical methods tailored to the animal species and individual differences. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data tailored to the animal species and individual differences into a generating AI, which can then apply the optimal analytical method.

[0044] The analysis unit can estimate the animal's emotions and adjust the display method of the analysis results based on the estimated animal's emotions. For example, if the animal is excited, the analysis unit can provide a concise display method. If the animal is relaxed, the analysis unit can also provide a detailed display method. For example, if the animal is stressed, the analysis unit can provide a display method that highlights the stressors. By adjusting the display method according to the animal's emotions, it becomes possible to provide a display that is easy for the user to understand. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal emotion data into a generating AI, and the generating AI can adjust the display method.

[0045] The analysis unit can improve the accuracy of its analysis by considering information about the animal's habitat. For example, if the animal is outdoors, the analysis unit can add data including ambient sounds to the analysis. If the animal is indoors, the analysis unit can also add indoor environmental data to the analysis. For example, if the animal is under specific environmental conditions, the analysis unit can add data related to that environment to the analysis. This improves the accuracy of the analysis by considering information about the habitat. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the animal's habitat into a generating AI, which can then improve the accuracy of the analysis.

[0046] The analysis unit can correct the analysis results by referring to the animal's health condition during the analysis. For example, if the animal's health condition is poor, the analysis unit can make corrections to identify the cause of abnormal behavior. If the animal's health condition is good, the analysis unit can also correct the analysis results based on normal behavior patterns. For example, if there is an abnormality in the animal's health condition, the analysis unit can make corrections based on the health condition. This improves the accuracy of the analysis results by referring to the health condition. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal health condition data into a generating AI, and the generating AI can correct the analysis results.

[0047] The verbalization unit can estimate the animal's emotions and adjust the verbalization expression based on the estimated animal's emotions. For example, if the animal is excited, the verbalization unit can use a concise expression. If the animal is relaxed, the verbalization unit can also use a detailed expression. For example, if the animal is stressed, the verbalization unit can use an expression that emphasizes the stressor. By adjusting the expression according to the animal's emotions, more appropriate verbalization becomes possible. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input animal emotion data into a generating AI, which can then adjust the expression.

[0048] The verbalization unit can adjust the level of detail in the verbalization based on the importance of the analysis results. For example, the verbalization unit will perform detailed verbalization for important analysis results. The verbalization unit can also perform concise verbalization for analysis results of low importance. For example, the verbalization unit can adjust the level of detail in the verbalization in stages according to importance. This allows for efficient verbalization by adjusting the level of detail in the verbalization according to importance. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input importance data of the analysis results into a generating AI, and the generating AI can adjust the level of detail in the verbalization.

[0049] The language processing unit can apply different language processing algorithms depending on the animal species and individual differences during the language processing process. For example, the language processing unit can apply an algorithm that verbalizes behaviors specific to a particular animal species. The language processing unit can also apply different language processing algorithms based on individual differences. For example, the language processing unit can select the optimal language processing algorithm depending on the animal species. This makes it possible to achieve more accurate language processing by applying a language processing algorithm that is appropriate for the animal species and individual differences. Some or all of the above processing in the language processing unit may be performed using AI or not. For example, the language processing unit can input data appropriate to the animal species and individual differences into a generating AI, which can then apply the optimal language processing algorithm.

[0050] The verbalization unit can estimate the animal's emotions and adjust the length of the verbalization based on the estimated emotions. For example, if the animal is excited, the verbalization unit will produce short, concise verbalization. If the animal is relaxed, the verbalization unit can also produce longer verbalization that includes detailed explanations. For example, if the animal is stressed, the verbalization unit will produce verbalization that emphasizes the stressors. By adjusting the length of the verbalization according to the animal's emotions, more appropriate verbalization becomes possible. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input animal emotion data into a generating AI, which can then adjust the length of the verbalization.

[0051] The verbalization unit can determine the priority of verbalization based on the submission timing of the analysis results. For example, the verbalization unit prioritizes verbalization of analysis results that are urgent. The verbalization unit can also quickly verbalize analysis results that have an approaching submission deadline. For example, the verbalization unit adjusts the verbalization priority in stages according to the submission timing. This enables efficient verbalization by determining the verbalization priority according to the submission timing. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input the submission timing data of the analysis results into a generating AI, and the generating AI can determine the verbalization priority.

[0052] The verbalization unit can adjust the order of verbalization based on the relevance of the analysis results. For example, the verbalization unit can prioritize verbalizing important analysis results. The verbalization unit can also verbalize highly relevant analysis results together. For example, the verbalization unit can adjust the order of verbalization in stages according to relevance. This allows for efficient verbalization by adjusting the order of verbalization according to relevance. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input relevance data of the analysis results into a generating AI, and the generating AI can adjust the order of verbalization.

[0053] The data storage unit can estimate the animal's emotions and select data to store based on the estimated emotions. For example, if the animal is excited, the data storage unit will prioritize storing vocal data. If the animal is relaxed, the data storage unit can also prioritize storing behavioral data. For example, if the animal is stressed, the data storage unit will prioritize storing environmental data. This allows for the priority storage of important data by selecting data according to the animal's emotions. Some or all of the above processing in the data storage unit may be performed using AI or not. For example, the data storage unit can input animal emotion data into a generating AI, which can then select the data to store.

[0054] The storage unit can optimize its storage algorithm by referring to past stored data during storage. For example, the storage unit can select the optimal data storage method based on past stored data. The storage unit can also analyze past stored data and apply algorithms to avoid data duplication. For example, the storage unit can refer to past stored data to select a storage method according to the importance of the data. This allows the optimal storage algorithm to be applied by referring to past stored data. Some or all of the above processes in the storage unit may be performed using AI or not. For example, the storage unit can input past stored data into a generating AI, which can then optimize the storage algorithm.

[0055] The data storage unit can estimate the animal's emotions and adjust the storage frequency based on the estimated emotions. For example, if the animal is excited, the storage unit can increase the storage frequency to collect data in real time. If the animal is relaxed, the storage unit can also decrease the storage frequency to collect data over a longer period. For example, if the animal is stressed, the storage unit can adjust the storage frequency to collect data to identify the stressors. This allows for efficient data storage by adjusting the storage frequency according to the animal's emotions. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input animal emotion data into a generating AI, which can then adjust the storage frequency.

[0056] The storage unit can weight the stored data based on the timing of animal behavioral data submission during storage. For example, the storage unit can assign a higher weight to the most recent behavioral data. It can also assign a lower weight to past behavioral data. For example, the storage unit can adjust the data weighting in stages according to the submission timing. This allows for the priority storage of important data by weighting the data according to the submission timing. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input animal behavioral data submission timing data into a generating AI, which can then weight the stored data.

[0057] The information provider can estimate the animal's emotions and adjust the display method of the information based on the estimated emotions. For example, if the animal is excited, the provider can provide a concise and easily visible display method. If the animal is relaxed, the provider can also provide a display method that includes detailed information. For example, if the animal is stressed, the provider can provide a display method that highlights the stressors. By adjusting the display method according to the animal's emotions, it becomes possible to provide information that is easy for the user to understand. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input animal emotion data into a generating AI, and the generating AI can adjust the display method.

[0058] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also select the most efficient display method from the user's past operation history. For example, the service provider may analyze the user's operation history and provide a customized display method. This allows the service provider to provide the user with the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input the user's operation history data into a generating AI, which can then select the optimal display method.

[0059] The information provider can estimate the animal's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the animal is excited, the information provider will prioritize providing important information. If the animal is relaxed, the information provider may also prioritize providing detailed information. For example, if the animal is stressed, the information provider will prioritize providing information about stressors. In this way, important information can be prioritized by determining the priority of information according to the animal's emotions. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input animal emotion data into a generating AI, and the generating AI can determine the priority of the information.

[0060] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, by taking device information into account, the service provider can provide the user with the most suitable display method. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into a generating AI, which can then select the optimal display method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The acquisition unit can acquire biometric data such as the animal's body temperature and heart rate simultaneously when acquiring animal sounds and behavior. For example, the acquisition unit can acquire body temperature and heart rate in real time using sensors attached to the animal's collar. Furthermore, the acquisition unit can monitor changes in the animal's body temperature and heart rate and issue an alert if an abnormality is detected. This allows for a more detailed understanding of the animal's health condition. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without using AI.

[0063] The analysis unit can analyze not only animal vocalizations and behavioral data, but also data on animal diet and exercise. For example, the analysis unit can record the content and amount of food consumed by animals and analyze their dietary patterns. Furthermore, the analysis unit can record the amount and patterns of exercise by animals and detect signs of insufficient or excessive exercise. This allows for a comprehensive analysis of the animals' overall health status. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0064] The service provider can provide users with appropriate advice and suggestions based on the animal's health condition and behavioral patterns. For example, the service provider can provide advice on the animal's diet and suggest ways to maintain a proper nutritional balance. Furthermore, the service provider can also suggest exercise plans to address the animal's lack of exercise. This allows users to manage their animals' health more effectively. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0065] The data acquisition unit can acquire ambient environmental data simultaneously with the acquisition of animal vocalizations and behavioral data. For example, the acquisition unit can acquire environmental data such as temperature, humidity, and noise level in the area where the animal is located using sensors. Furthermore, the acquisition unit can adjust the frequency of acquiring animal behavioral data in response to changes in the environmental data. This allows for a detailed analysis of how animal behavior is influenced by environmental factors. Some or all of the processing described above in the acquisition unit may be performed using AI or not.

[0066] The analysis unit can detect anomalies when analyzing animal vocalizations and behavioral data by comparing them with past animal data. For example, the analysis unit can compare past and current animal vocalization data to detect any abnormal changes. Furthermore, the analysis unit can compare past and current animal behavioral patterns and issue an alert if abnormal behavior is observed. This allows for the early detection of abnormalities in the animal's health and behavior. Some or all of the above processing in the analysis unit may be performed using AI, or it may not.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The acquisition unit acquires animal sounds and behaviors as audio and visual data. For example, animal sounds are collected with a high-sensitivity microphone, and clear audio data is obtained using noise reduction technology. In addition, animal behavior can be filmed with a high-resolution camera to capture details of movement. These processes may or may not be performed using AI. Step 2: The analysis unit analyzes the data acquired by the acquisition unit, combining various behavioral information about the internet and animals with thinking based on LLM. For example, it can analyze the frequency components of animal vocalization data using speech analysis technology to estimate the animal's emotions. It can also extract movement characteristics from animal behavior data using image analysis technology to identify behavioral patterns. These processes may or may not be performed using AI. Step 3: The language processing unit verbalizes the results analyzed by the analysis unit. For example, it expresses the emotions and intentions of animals in natural language and provides them to the user. It can also explain animal behavior patterns in text and provide them in a way that is easy for the user to understand. These processes may or may not be performed using AI.

[0069] (Example of form 2) The animal communication system according to an embodiment of the present invention is a system that achieves highly accurate communication with animals by utilizing AI to acquire animal sounds and behaviors as audio and visual data, processing and analyzing the data using an AI agent that combines various behavioral information from the internet and animals with thinking processes based on an LLM (Large-Scale Language Model), and then verbalizing the results. This animal communication system acquires animal sounds and behaviors as audio and visual data. Next, the acquired data is processed by an AI agent, which analyzes the data by combining various behavioral information from the internet and animals with thinking processes based on an LLM. Finally, the analysis results are verbalized, enabling communication with animals. For example, the animal communication system aims to enter the pet market as a highly accurate communication tool with pets. It will enable communication with pets, understanding situations when abnormalities or anomalies occur, and risk avoidance. It also aims to enter the education and zoo markets as a learning tool for zoos and schools to learn more deeply about animal behavior. Furthermore, it aims to be used as an auxiliary tool in animal medical settings and animal research. In the future, it aims to accumulate animal communication data in the generating AI and become a tool that can contribute to supporting biological research. Specifically, we will expand data accumulation and analysis, broaden the range of target animals, and enable its use as support for learning and medical care. Furthermore, we will deepen its accuracy as a support tool for animal research, contributing to the discovery of new laws in the animal kingdom and the conservation of endangered species. Through this, the animal communication system will be able to achieve communication with animals by accurately acquiring, analyzing, and verbalizing animal vocalizations and behaviors.

[0070] The animal communication system according to this embodiment comprises an acquisition unit, an analysis unit, and a language processing unit. The acquisition unit acquires animal sounds and behaviors as audio data and visual data. For example, the acquisition unit collects animal sounds with a microphone and stores them as audio data. The acquisition unit can also photograph animal behaviors with a camera and store them as visual data. For example, the acquisition unit collects animal sounds with a high-sensitivity microphone and acquires clear audio data using noise reduction technology. Furthermore, the acquisition unit can photograph animal behaviors with a high-resolution camera to capture details of their movements. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, when collecting animal sounds, the acquisition unit can use AI to remove noise and acquire clear audio data. The analysis unit analyzes the data acquired by the acquisition unit and combines various behavioral information about animals from the internet with thinking by LLM. For example, the analysis unit analyzes animal sound data using voice analysis technology to estimate the animal's emotions and intentions. Furthermore, the analysis unit can analyze animal behavior data using image analysis technology to identify animal behavior patterns. For example, the analysis unit can analyze the frequency components of animal vocalization data using sound analysis technology to estimate the animal's emotions. In addition, the analysis unit can extract movement features from animal behavior data using image analysis technology to identify behavior patterns. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal vocalization data into AI, which can then estimate the emotions. The language processing unit verbalizes the results analyzed by the analysis unit. For example, the language processing unit can express the animal's emotions and intentions in natural language and provide it to the user. The language processing unit can also explain animal behavior patterns in sentences and provide them in a way that is easy for the user to understand. For example, the language processing unit can express the animal's emotions with words such as "happy" or "sad." Furthermore, the language processing unit can explain animal behavior patterns with sentences such as "this animal frequently performs this action."Some or all of the processing described above in the language processing unit may be performed using AI or not. For example, the language processing unit can input the analysis results into the AI, which can then express them in natural language. As a result, the animal communication system according to this embodiment can achieve communication with animals by acquiring, analyzing, and verbalizing animal sounds and behaviors with high accuracy.

[0071] The acquisition unit acquires animal sounds and behaviors as audio and visual data. For example, the acquisition unit can collect animal sounds with a microphone and save them as audio data. The acquisition unit can also photograph animal behaviors with a camera and save them as visual data. Specifically, the acquisition unit collects animal sounds using a high-sensitivity microphone and acquires clear audio data using noise reduction technology. This eliminates ambient noise and allows for accurate capture of animal sounds. Furthermore, the acquisition unit can photograph animal behaviors using a high-resolution camera to capture details of their movements. For example, it can record subtle movements and changes in facial expressions when an animal performs a specific action in high resolution. This allows for detailed observation of animal behavior and is useful for later analysis. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, when collecting animal sounds, the acquisition unit can use AI to remove noise and acquire clear audio data. The AI ​​analyzes the collected audio data in real time and filters out unnecessary noise, making the animal sounds clearer. Furthermore, the acquisition unit can use AI to extract movement characteristics in real time when filming animal behavior, automatically detecting important actions. This allows the acquisition unit to acquire animal vocalizations and behaviors with high accuracy and efficiently collect data necessary for subsequent analysis. In addition, the acquisition unit can centrally manage the collected data and link with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and language processing units. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the acquisition unit can collect data efficiently and effectively, improving the overall performance of the system.

[0072] The analysis unit analyzes the data acquired by the acquisition unit, combining various behavioral information about the internet and animals with thinking based on LLM. Specifically, it analyzes animal vocal data using speech analysis technology to estimate the animal's emotions and intentions. For example, by decomposing animal vocal data into frequency components and analyzing the intensity and patterns in specific frequency bands, it is possible to estimate what kind of emotions the animal is feeling. Furthermore, it is also possible to analyze animal behavior data using image analysis technology to identify animal behavior patterns. For example, by extracting the characteristics of animal movements and identifying specific behavior patterns, it is possible to estimate what kind of intentions the animal is feeling. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input animal vocal data into AI, and the AI ​​can estimate emotions. Based on past data and trained models, the AI ​​analyzes the characteristics of animal vocals and estimates emotions and intentions with high accuracy. It is also possible to input animal behavior data into AI and have the AI ​​identify behavior patterns. The AI ​​analyzes animal movements using image recognition technology and identifies specific behavior patterns to estimate the animal's intentions. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can analyze the behavioral patterns of specific animals based on past behavioral data and predict future behavior. The analysis department can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. As a result, the analysis department can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and safety of the entire system.

[0073] The language processing unit verbalizes the results analyzed by the analysis unit. Specifically, it expresses the animal's emotions and intentions in natural language and provides them to the user. For example, it can express an animal's emotions using words such as "happy" or "sad." This allows the user to intuitively understand the animal's emotions. The language processing unit can also explain the animal's behavior patterns in sentences and provide them to the user in an easy-to-understand format. For example, by explaining it with sentences such as "This animal frequently performs this action," the user can more easily grasp the animal's behavior patterns. Some or all of the above processing in the language processing unit may be performed using AI or not. For example, the language processing unit can input the analysis results into AI, which can then express them in natural language. Based on the analysis results, the AI ​​expresses the animal's emotions and behavior patterns in natural language and provides them to the user. The language processing unit can also collect user feedback and continuously improve the accuracy and effectiveness of the verbalization. For example, based on user feedback, it can review how to express the animal's emotions and behavior patterns and improve them to be easier to understand. Furthermore, the language processing unit can support multiple languages, accommodating users who speak different languages. This allows the language processing unit to provide users with easily understandable information about animals' emotions and behavioral patterns, thereby supporting communication with animals.

[0074] The animal communication system includes a storage unit that stores animal behavior data. The storage unit, for example, collects animal behavior data over a long period and stores it in a database. The storage unit can also periodically collect animal behavior data and store it in the database. For example, the storage unit records the animal's eating patterns daily and stores them in the database. Furthermore, the storage unit can periodically record the animal's sleep patterns and store them in the database. Some or all of the above processing in the storage unit may be performed using AI, or not. For example, the storage unit can input animal behavior data into the AI, which can then organize and store the data. This allows for long-term data analysis by accumulating animal behavior data.

[0075] The animal communication system includes a delivery unit that provides analysis results to the user. The delivery unit, for example, displays the analysis results on the user's device. The delivery unit can also provide analysis results to the user through a web application or mobile application. For example, the delivery unit can display the analysis results on a smartphone application, allowing the user to view them in real time. Furthermore, the delivery unit can also send the analysis results to the user via email. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input the analysis results into an AI, which can then display them to the user in a format optimized for that user. This allows the user to understand the animal's condition by providing them with the analysis results.

[0076] The acquisition unit can estimate the animal's emotions and adjust the timing of acquiring vocalizations and behaviors based on the estimated emotions. For example, if the animal is excited, the acquisition unit can increase the frequency of vocalization acquisition and acquire behavioral changes in real time. If the animal is relaxed, the acquisition unit can also decrease the frequency of vocalization acquisition and acquire longer-term behavioral patterns. For example, if the animal is stressed, the acquisition unit will refrain from acquiring vocalizations and focus on acquiring behavioral changes. By adjusting the acquisition timing according to the animal's emotions, more accurate data can be obtained. 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 acquisition unit may be performed using AI or not. For example, the acquisition unit can input animal vocalizations and behavioral data into a generative AI to estimate the animal's emotions, and the generative AI can estimate the emotions.

[0077] The data acquisition unit can analyze the animal's past behavioral history and select the optimal data acquisition method. For example, the data acquisition unit can focus on acquiring animal vocalizations that occur during specific time periods, based on past behavioral history. The data acquisition unit can also focus on acquiring visual data during time periods when specific behavioral patterns are observed, based on past behavioral history. For example, the data acquisition unit can analyze past behavioral history and prioritize acquiring behavioral data under specific environmental conditions. This enables efficient data acquisition by selecting the optimal data acquisition method based on past behavioral history. Some or all of the above processing in the data acquisition unit may be performed using AI or not. For example, the data acquisition unit can input past animal behavioral history data into a generating AI, which can then select the optimal data acquisition method.

[0078] The acquisition unit can filter the acquired sounds and behaviors based on the animal species and individual differences. For example, the acquisition unit can prioritize the acquisition of sounds specific to a particular animal species. The acquisition unit can also focus on acquiring data from animals with specific behavioral patterns based on individual differences. For example, the acquisition unit can adjust the frequency band of the sounds acquired according to the animal species. This allows for the acquisition of more accurate data by filtering according to the animal species and individual differences. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input sound and behavior data into a generating AI based on the animal species and individual differences, and the generating AI can perform the filtering.

[0079] The data acquisition unit can estimate the animal's emotions and determine the priority of data to acquire based on the estimated animal's emotions. For example, if the animal is excited, the data acquisition unit may prioritize acquiring vocal data. If the animal is relaxed, the data acquisition unit may also prioritize acquiring behavioral data. For example, if the animal is stressed, the data acquisition unit may prioritize acquiring environmental data. This allows for the priority acquisition of important data by determining the data priority 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 may be, 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 acquisition unit may be performed using AI or not. For example, the data acquisition unit can input animal vocal and behavioral data into a generative AI to estimate the animal's emotions, and the generative AI can estimate the emotions.

[0080] The acquisition unit can prioritize the acquisition of highly relevant data when acquiring sounds and behaviors, taking into account information about the animal's habitat. For example, if the animal is outdoors, the acquisition unit prioritizes acquiring sound data, including ambient sounds. If the animal is indoors, the acquisition unit can also prioritize acquiring behavioral data. For example, if the animal is under specific environmental conditions, the acquisition unit prioritizes acquiring data related to that environment. This allows for the priority acquisition of highly relevant data by considering information about the habitat. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without AI. For example, the acquisition unit can input information about the animal's habitat into a generating AI, which can then prioritize the acquisition of highly relevant data.

[0081] The acquisition unit analyzes the animal's health condition when acquiring vocalizations and behavioral data, and can acquire specific data if an abnormality is detected. For example, if the animal's health condition is poor, the acquisition unit may focus on acquiring vocalization data. If the animal's health condition is good, the acquisition unit may focus on acquiring behavioral data. For example, if there is an abnormality in the animal's health condition, the acquisition unit may focus on acquiring environmental data. This allows for the acquisition of specific data when an abnormality is detected by analyzing the health condition. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without AI. For example, the acquisition unit can input animal health condition data into a generating AI, which can detect abnormalities and acquire specific data.

[0082] The analysis unit can estimate the animal's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the animal is excited, the analysis unit can apply an algorithm for rapid analysis. If the animal is relaxed, the analysis unit can also apply an algorithm for detailed analysis. For example, if the animal is stressed, the analysis unit can apply an algorithm to identify the stressors. This allows for a more accurate analysis by adjusting the analysis algorithm according to the animal's emotions. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal emotion data into a generating AI, which can then adjust the analysis algorithm.

[0083] The analysis unit can adjust the level of detail of the analysis based on the animal's behavioral patterns. For example, if the animal's behavioral patterns are complex, the analysis unit will perform a detailed analysis. If the animal's behavioral patterns are simple, the analysis unit can also perform a simplified analysis. For example, if a change is observed in the animal's behavioral patterns, the analysis unit will perform a detailed analysis to identify the factors causing the change. By adjusting the level of detail of the analysis based on the behavioral patterns, efficient analysis becomes possible. Some or all of the above processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input animal behavioral pattern data into a generating AI, which can then adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analytical methods depending on the animal species and individual differences during analysis. For example, the analysis unit can apply methods to analyze behavioral patterns specific to a particular animal species. The analysis unit can also apply different analytical methods based on individual differences. For example, the analysis unit can select the optimal analytical method depending on the animal species. This allows for more accurate analysis by applying analytical methods tailored to the animal species and individual differences. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data tailored to the animal species and individual differences into a generating AI, which can then apply the optimal analytical method.

[0085] The analysis unit can estimate the animal's emotions and adjust the display method of the analysis results based on the estimated animal's emotions. For example, if the animal is excited, the analysis unit can provide a concise display method. If the animal is relaxed, the analysis unit can also provide a detailed display method. For example, if the animal is stressed, the analysis unit can provide a display method that highlights the stressors. By adjusting the display method according to the animal's emotions, it becomes possible to provide a display that is easy for the user to understand. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal emotion data into a generating AI, and the generating AI can adjust the display method.

[0086] The analysis unit can improve the accuracy of its analysis by considering information about the animal's habitat. For example, if the animal is outdoors, the analysis unit can add data including ambient sounds to the analysis. If the animal is indoors, the analysis unit can also add indoor environmental data to the analysis. For example, if the animal is under specific environmental conditions, the analysis unit can add data related to that environment to the analysis. This improves the accuracy of the analysis by considering information about the habitat. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input information about the animal's habitat into a generating AI, which can then improve the accuracy of the analysis.

[0087] The analysis unit can correct the analysis results by referring to the animal's health condition during the analysis. For example, if the animal's health condition is poor, the analysis unit can make corrections to identify the cause of abnormal behavior. If the animal's health condition is good, the analysis unit can also correct the analysis results based on normal behavior patterns. For example, if there is an abnormality in the animal's health condition, the analysis unit can make corrections based on the health condition. This improves the accuracy of the analysis results by referring to the health condition. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input animal health condition data into a generating AI, and the generating AI can correct the analysis results.

[0088] The verbalization unit can estimate the animal's emotions and adjust the verbalization expression based on the estimated animal's emotions. For example, if the animal is excited, the verbalization unit can use a concise expression. If the animal is relaxed, the verbalization unit can also use a detailed expression. For example, if the animal is stressed, the verbalization unit can use an expression that emphasizes the stressor. By adjusting the expression according to the animal's emotions, more appropriate verbalization becomes possible. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input animal emotion data into a generating AI, which can then adjust the expression.

[0089] The verbalization unit can adjust the level of detail in the verbalization based on the importance of the analysis results. For example, the verbalization unit will perform detailed verbalization for important analysis results. The verbalization unit can also perform concise verbalization for analysis results of low importance. For example, the verbalization unit can adjust the level of detail in the verbalization in stages according to importance. This allows for efficient verbalization by adjusting the level of detail in the verbalization according to importance. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input importance data of the analysis results into a generating AI, and the generating AI can adjust the level of detail in the verbalization.

[0090] The language processing unit can apply different language processing algorithms depending on the animal species and individual differences during the language processing process. For example, the language processing unit can apply an algorithm that verbalizes behaviors specific to a particular animal species. The language processing unit can also apply different language processing algorithms based on individual differences. For example, the language processing unit can select the optimal language processing algorithm depending on the animal species. This makes it possible to achieve more accurate language processing by applying a language processing algorithm that is appropriate for the animal species and individual differences. Some or all of the above processing in the language processing unit may be performed using AI or not. For example, the language processing unit can input data appropriate to the animal species and individual differences into a generating AI, which can then apply the optimal language processing algorithm.

[0091] The verbalization unit can estimate the animal's emotions and adjust the length of the verbalization based on the estimated emotions. For example, if the animal is excited, the verbalization unit will produce short, concise verbalization. If the animal is relaxed, the verbalization unit can also produce longer verbalization that includes detailed explanations. For example, if the animal is stressed, the verbalization unit will produce verbalization that emphasizes the stressors. By adjusting the length of the verbalization according to the animal's emotions, more appropriate verbalization becomes possible. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input animal emotion data into a generating AI, which can then adjust the length of the verbalization.

[0092] The verbalization unit can determine the priority of verbalization based on the submission timing of the analysis results. For example, the verbalization unit prioritizes verbalization of analysis results that are urgent. The verbalization unit can also quickly verbalize analysis results that have an approaching submission deadline. For example, the verbalization unit adjusts the verbalization priority in stages according to the submission timing. This enables efficient verbalization by determining the verbalization priority according to the submission timing. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input the submission timing data of the analysis results into a generating AI, and the generating AI can determine the verbalization priority.

[0093] The verbalization unit can adjust the order of verbalization based on the relevance of the analysis results. For example, the verbalization unit can prioritize verbalizing important analysis results. The verbalization unit can also verbalize highly relevant analysis results together. For example, the verbalization unit can adjust the order of verbalization in stages according to relevance. This allows for efficient verbalization by adjusting the order of verbalization according to relevance. Some or all of the above processing in the verbalization unit may be performed using AI or not. For example, the verbalization unit can input relevance data of the analysis results into a generating AI, and the generating AI can adjust the order of verbalization.

[0094] The data storage unit can estimate the animal's emotions and select data to store based on the estimated emotions. For example, if the animal is excited, the data storage unit will prioritize storing vocal data. If the animal is relaxed, the data storage unit can also prioritize storing behavioral data. For example, if the animal is stressed, the data storage unit will prioritize storing environmental data. This allows for the priority storage of important data by selecting data according to the animal's emotions. Some or all of the above processing in the data storage unit may be performed using AI or not. For example, the data storage unit can input animal emotion data into a generating AI, which can then select the data to store.

[0095] The storage unit can optimize its storage algorithm by referring to past stored data during storage. For example, the storage unit can select the optimal data storage method based on past stored data. The storage unit can also analyze past stored data and apply algorithms to avoid data duplication. For example, the storage unit can refer to past stored data to select a storage method according to the importance of the data. This allows the optimal storage algorithm to be applied by referring to past stored data. Some or all of the above processes in the storage unit may be performed using AI or not. For example, the storage unit can input past stored data into a generating AI, which can then optimize the storage algorithm.

[0096] The data storage unit can estimate the animal's emotions and adjust the storage frequency based on the estimated emotions. For example, if the animal is excited, the storage unit can increase the storage frequency to collect data in real time. If the animal is relaxed, the storage unit can also decrease the storage frequency to collect data over a longer period. For example, if the animal is stressed, the storage unit can adjust the storage frequency to collect data to identify the stressors. This allows for efficient data storage by adjusting the storage frequency according to the animal's emotions. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input animal emotion data into a generating AI, which can then adjust the storage frequency.

[0097] The storage unit can weight the stored data based on the timing of animal behavioral data submission during storage. For example, the storage unit can assign a higher weight to the most recent behavioral data. It can also assign a lower weight to past behavioral data. For example, the storage unit can adjust the data weighting in stages according to the submission timing. This allows for the priority storage of important data by weighting the data according to the submission timing. Some or all of the above processing in the storage unit may be performed using AI or not. For example, the storage unit can input animal behavioral data submission timing data into a generating AI, which can then weight the stored data.

[0098] The information provider can estimate the animal's emotions and adjust the display method of the information based on the estimated emotions. For example, if the animal is excited, the provider can provide a concise and easily visible display method. If the animal is relaxed, the provider can also provide a display method that includes detailed information. For example, if the animal is stressed, the provider can provide a display method that highlights the stressors. By adjusting the display method according to the animal's emotions, it becomes possible to provide information that is easy for the user to understand. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input animal emotion data into a generating AI, and the generating AI can adjust the display method.

[0099] The service provider can select the optimal display method by referring to the user's past operation history at the time of delivery. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider can also select the most efficient display method from the user's past operation history. For example, the service provider may analyze the user's operation history and provide a customized display method. This allows the service provider to provide the user with the optimal display method by referring to past operation history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider may input the user's operation history data into a generating AI, which can then select the optimal display method.

[0100] The information provider can estimate the animal's emotions and determine the priority of the information to be provided based on the estimated emotions. For example, if the animal is excited, the information provider will prioritize providing important information. If the animal is relaxed, the information provider may also prioritize providing detailed information. For example, if the animal is stressed, the information provider will prioritize providing information about stressors. In this way, important information can be prioritized by determining the priority of information according to the animal's emotions. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input animal emotion data into a generating AI, and the generating AI can determine the priority of the information.

[0101] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. In this way, by taking device information into account, the service provider can provide the user with the most suitable display method. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into a generating AI, which can then select the optimal display method.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The acquisition unit can acquire biometric data such as the animal's body temperature and heart rate simultaneously when acquiring animal sounds and behavior. For example, the acquisition unit can acquire body temperature and heart rate in real time using sensors attached to the animal's collar. Furthermore, the acquisition unit can monitor changes in the animal's body temperature and heart rate and issue an alert if an abnormality is detected. This allows for a more detailed understanding of the animal's health condition. Some or all of the above processing in the acquisition unit may be performed using AI, or it may be performed without using AI.

[0104] The analysis unit can analyze not only animal vocalizations and behavioral data, but also data on animal diet and exercise. For example, the analysis unit can record the content and amount of food consumed by animals and analyze their dietary patterns. Furthermore, the analysis unit can record the amount and patterns of exercise by animals and detect signs of insufficient or excessive exercise. This allows for a comprehensive analysis of the animals' overall health status. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0105] The service provider can provide users with appropriate advice and suggestions based on the animal's health condition and behavioral patterns. For example, the service provider can provide advice on the animal's diet and suggest ways to maintain a proper nutritional balance. Furthermore, the service provider can also suggest exercise plans to address the animal's lack of exercise. This allows users to manage their animals' health more effectively. Some or all of the above-described processes in the service provider may be performed using AI or not.

[0106] The data acquisition unit can acquire ambient environmental data simultaneously with the acquisition of animal vocalizations and behavioral data. For example, the acquisition unit can acquire environmental data such as temperature, humidity, and noise level in the area where the animal is located using sensors. Furthermore, the acquisition unit can adjust the frequency of acquiring animal behavioral data in response to changes in the environmental data. This allows for a detailed analysis of how animal behavior is influenced by environmental factors. Some or all of the processing described above in the acquisition unit may be performed using AI or not.

[0107] The analysis unit can detect anomalies when analyzing animal vocalizations and behavioral data by comparing them with past animal data. For example, the analysis unit can compare past and current animal vocalization data to detect any abnormal changes. Furthermore, the analysis unit can compare past and current animal behavioral patterns and issue an alert if abnormal behavior is observed. This allows for the early detection of abnormalities in the animal's health and behavior. Some or all of the above processing in the analysis unit may be performed using AI, or it may not.

[0108] The data acquisition unit can estimate the animal's emotions and select the type of data to acquire based on the estimated emotions. For example, if the animal is excited, it can focus on acquiring vocal data. If the animal is relaxed, it can focus on acquiring behavioral data. This allows for the acquisition of appropriate data according to the animal's emotions. Emotion estimation is performed using an emotion engine or generative AI. Some or all of the processing described above in the data acquisition unit may be performed using AI or not.

[0109] The analysis unit can estimate the animal's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the animal is excited, a rapid analysis can be performed. If the animal is relaxed, a detailed analysis can be performed. This allows for appropriate analysis depending on the animal's emotions. Emotion estimation is performed using an emotion engine or generative AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not.

[0110] The information provider can estimate the animal's emotions and adjust the content of the information provided based on the estimated emotions. For example, if the animal is excited, it can provide the user with alert information. If the animal is relaxed, it can also provide the user with reassuring information. This allows for the provision of appropriate information according to the animal's emotions. Emotion estimation is performed using an emotion engine or generative AI. Some or all of the processing described above in the information provider may be performed using AI or not.

[0111] The verbalization unit can estimate the animal's emotions and adjust the tone of verbalization based on the estimated emotions. For example, if the animal is excited, it can verbalize in a warning tone. If the animal is relaxed, it can verbalize in a calm tone. This allows information to be conveyed in an appropriate tone according to the animal's emotions. Emotion estimation is performed using an emotion engine or generative AI. Some or all of the processing described above in the verbalization unit may be performed using AI or not.

[0112] The storage unit can estimate the animal's emotions and adjust the data retention period based on the estimated emotions. For example, if the animal is excited, short-term data can be prioritized for storage. If the animal is relaxed, long-term data can also be stored. This allows for appropriate data storage according to the animal's emotions. Emotion estimation is performed using an emotion engine or generative AI. Some or all of the above-described processing in the storage unit may be performed using AI or not.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The acquisition unit acquires animal sounds and behaviors as audio and visual data. For example, animal sounds are collected with a high-sensitivity microphone, and clear audio data is obtained using noise reduction technology. In addition, animal behavior can be filmed with a high-resolution camera to capture details of movement. These processes may or may not be performed using AI. Step 2: The analysis unit analyzes the data acquired by the acquisition unit, combining various behavioral information about the internet and animals with thinking based on LLM. For example, it can analyze the frequency components of animal vocalization data using speech analysis technology to estimate the animal's emotions. It can also extract movement characteristics from animal behavior data using image analysis technology to identify behavioral patterns. These processes may or may not be performed using AI. Step 3: The language processing unit verbalizes the results analyzed by the analysis unit. For example, it expresses the emotions and intentions of animals in natural language and provides them to the user. It can also explain animal behavior patterns in text and provide them in a way that is easy for the user to understand. These processes may or may not be performed using AI.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the acquisition unit, analysis unit, language processing unit, storage unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires animal sounds and behaviors using the microphone 38B and camera 42 of the smart device 14 and stores them as audio data and visual data by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data by combining various behavioral information about the internet and animals with thinking by LLM. The language processing unit is implemented in the specific processing unit 290 of the data processing unit 12 and expresses the analysis results in natural language. The storage unit is implemented in the specific processing unit 290 of the data processing unit 12 as a processing unit that stores animal behavior data in the database 24. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and displays the analysis results on the user's device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the acquisition unit, analysis unit, language processing unit, storage unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit uses the microphone 238 and camera 42 of the smart glasses 214 to acquire animal sounds and behaviors and stores them as audio data and visual data by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the acquired data by combining various behavioral information about the internet and animals with thinking by LLM. The language processing unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and expresses the analysis results in natural language. The storage unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 as a processing unit that stores animal behavior data in the database 24. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and displays the analysis results on the user's device. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the acquisition unit, analysis unit, language processing unit, storage unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires animal sounds and behaviors using the microphone 238 and camera 42 of the headset terminal 314 and stores them as audio data and visual data by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data by combining various behavioral information about the internet and animals with thinking by LLM. The language processing unit is implemented in the specific processing unit 290 of the data processing unit 12 and expresses the analysis results in natural language. The storage unit is implemented in the specific processing unit 290 of the data processing unit 12 as a processing unit that stores animal behavior data in the database 24. The provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and displays the analysis results on the user's device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the acquisition unit, analysis unit, language processing unit, storage unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires animal sounds and behaviors using the microphone 238 and camera 42 of the robot 414 and stores them as audio data and visual data by the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data by combining various behavioral information about the internet and animals with thinking by LLM. The language processing unit is implemented in the specific processing unit 290 of the data processing unit 12 and expresses the analysis results in natural language. The storage unit is implemented in the specific processing unit 290 of the data processing unit 12 as a processing unit that stores animal behavior data in the database 24. The provision unit is implemented in the control unit 46A of the robot 414 and displays the analysis results on the user's device. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) An acquisition unit that acquires animal sounds and behaviors as audio and visual data, The data acquired by the aforementioned acquisition unit is analyzed by an analysis unit that combines various behavioral information about the internet and animals with thinking based on LLM, and The system comprises a language processing unit that verbalizes the results analyzed by the analysis unit. A system characterized by the following features. (Note 2) It is equipped with a storage unit for accumulating animal behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a unit that provides analysis results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, It estimates the animal's emotions and adjusts the timing of capturing vocalizations and behaviors based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, Analyze the animal's past behavioral history and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, When acquiring vocalizations and behavioral data, filtering is performed based on animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, The system estimates the emotions of animals and prioritizes the data to be collected based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring vocalizations and behavioral data, the system prioritizes the acquisition of highly relevant data, taking into account information about the animals' habitat. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring vocalizations and behavioral data, the system analyzes the animal's health status and retrieves specific data if abnormalities are detected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the emotions of animals and adjusts the analysis algorithm based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is During analysis, adjust the level of detail based on the animal's behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the emotions of animals and adjusts how the analysis results are displayed based on the estimated emotions of the animals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is When analyzing, consider information about the animals' habitat to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, the analysis results are corrected by referring to the animal's health status. The system described in Appendix 1, characterized by the features described herein. (Note 16) The language processing unit, It estimates the emotions of animals and adjusts the way they express themselves verbally based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The language processing unit, When verbalizing the findings, adjust the level of detail based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The language processing unit, When verbalizing information, different verbalization algorithms are applied depending on the animal species and individual differences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The language processing unit, It estimates the animal's emotions and adjusts the length of verbalization based on the estimated animal's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The language processing unit, When verbalizing the findings, prioritize the verbalization based on the timing of submitting the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The language processing unit, When verbalizing the findings, adjust the order of verbalization based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The storage unit is The system estimates the emotions of animals and selects accumulated data based on the estimated emotions of the animals. The system described in Appendix 2, characterized by the features described herein. (Note 23) The storage unit is During data storage, the storage algorithm is optimized by referring to past stored data. The system described in Appendix 2, characterized by the features described herein. (Note 24) The storage unit is It estimates the animal's emotions and adjusts the frequency of accumulation based on the estimated animal's emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The storage unit is During data accumulation, the accumulated data is weighted based on when the animal behavior data was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the emotions of animals and adjusts how the information provided is displayed based on the estimated emotions of the animals. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 3, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the emotions of animals and prioritizes the information to be provided based on the estimated emotions of the animals. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0187] 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. An acquisition unit that acquires animal sounds and behaviors as audio and visual data, The data acquired by the aforementioned acquisition unit is analyzed by an analysis unit that combines various behavioral information about the internet and animals with thinking based on LLM, and The system comprises a language processing unit that verbalizes the results analyzed by the analysis unit. A system characterized by the following features.

2. It is equipped with a storage unit for accumulating animal behavior data. The system according to feature 1.

3. It includes a unit that provides analysis results to the user. The system according to feature 1.

4. The acquisition unit is, It estimates the animal's emotions and adjusts the timing of capturing vocalizations and behaviors based on the estimated emotions. The system according to feature 1.

5. The acquisition unit is, Analyze the animal's past behavioral history and select the optimal acquisition method. The system according to feature 1.

6. The acquisition unit is, When acquiring vocalizations and behavioral data, filtering is performed based on animal species and individual differences. The system according to feature 1.

7. The acquisition unit is, The system estimates the emotions of animals and prioritizes the data to be collected based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, When acquiring vocalizations and behavioral data, the system prioritizes the acquisition of highly relevant data, taking into account information about the animals' habitat. The system according to feature 1.

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