system

Abstract

The system according to this embodiment aims to analyze the sounds and behavior of pets and communicate the results to the user. [Solution] The system according to this embodiment comprises an analysis unit, a transmission unit, and a detection unit. The analysis unit analyzes the pet's barks and behavior. The transmission unit communicates the results analyzed by the analysis unit to the user. The detection unit detects the pet's anxiety and stress based on 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the barks and behaviors of pets have not been sufficiently analyzed and conveyed to users, and there is room for improvement.

[0005] <s The system according to the embodiment aims to analyze the barks and behaviors of pets and convey the results to users.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a transmission unit, and a detection unit. The analysis unit analyzes the barks and behaviors of pets. The transmission unit conveys the results analyzed by the analysis unit to users. The detection unit detects the anxiety and stress of pets based on the results analyzed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can analyze the sounds and behavior of pets and communicate the results to the user. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 pet communication system according to an embodiment of the present invention is a system that analyzes the sounds and behavior of pets and assists in communication with users. The pet communication system analyzes the sounds and behavior of pets, communicates this information to the user, and senses anxiety and stress, thereby facilitating communication between pets and humans. For example, the pet communication system analyzes the sounds and behavior of pets. The pet communication system uses speech recognition technology to analyze the sounds of pets and understand what they are trying to communicate. Next, the pet communication system uses speech synthesis technology to convey a message to the user. Furthermore, the pet communication system senses the anxiety and stress of pets and provides advice to the user. For example, if a pet is barking to ask for food or water, the pet communication system can analyze the sounds and convey a message to the user such as, "It seems the pet is hungry." Also, if the pet communication system senses that the pet is feeling anxious or stressed, it will sense this and advise the user, "Your pet is feeling anxious, so please reassure it." In this way, the pet communication system facilitates smoother communication between pets and humans and helps to understand the needs and emotions of pets. Especially for people experiencing pet loss, the ability to recreate the behavior and sounds of their deceased pet through the pet communication system allows them to relive memories and bring emotional stability and healing. The system analyzes the pet's sounds and behavior, communicates them to the user, and senses anxiety and stress, thereby facilitating smoother communication between pets and humans.

[0029] The pet communication system according to this embodiment comprises an analysis unit, a transmission unit, and a detection unit. The analysis unit analyzes the pet's vocalizations and behavior. The analysis unit, for example, uses speech recognition technology to analyze the pet's vocalizations and understand what it is trying to communicate. The analysis unit can also, for example, analyze the pet's behavior and interpret the meaning of that behavior. The analysis unit can, for example, analyze the frequency and volume of the pet's vocalizations and understand what those vocalizations mean. The transmission unit conveys the results analyzed by the analysis unit to the user. The transmission unit conveys messages to the user, for example, using speech synthesis technology. The transmission unit can also, for example, generate text messages and notify the user. The transmission unit can also, for example, convey messages to the user through app notifications. The detection unit detects the pet's anxiety and stress based on the results analyzed by the analysis unit. The detection unit detects anxiety and stress, for example, by analyzing changes in the pet's vocalizations and behavior. The sensing unit can, for example, analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. The sensing unit can also, for example, analyze the pet's behavioral patterns to measure stress levels. As a result, the pet communication system according to this embodiment can analyze the pet's vocalizations and behavior, communicate this information to the user, and detect anxiety and stress, thereby facilitating smooth communication between pets and humans.

[0030] The analysis unit analyzes the sounds and behaviors of pets. For example, it uses speech recognition technology to analyze pet sounds and understand what the pet is trying to communicate. Specifically, speech recognition technology extracts features such as frequency, volume, rhythm, and tone of the pet's sounds and interprets the meaning of the sounds based on these features. For example, a high-pitched, short bark in a dog may indicate excitement or joy, while a low-pitched, long bark may indicate alertness or anxiety. The analysis unit learns these patterns and can analyze the meaning of sounds with high accuracy. The analysis unit can also analyze pet behavior and interpret the meaning of that behavior. For example, it analyzes actions such as a cat wagging its tail or perking up its ears to understand the emotions and intentions indicated by these actions. Furthermore, the analysis unit analyzes the frequency and volume of pet sounds to understand what those sounds mean. For example, it analyzes the changes in frequency band and volume when a pet makes a specific sound to identify which emotion the sound represents, such as a request, dissatisfaction, or joy. This allows the analysis unit to analyze pet vocalizations and behaviors in detail and accurately understand their meaning. Furthermore, the analysis unit can utilize AI technology to continuously learn patterns of pet vocalizations and behaviors, improving analysis accuracy. For example, it can use deep learning to learn from large amounts of vocalization and behavior data to detect new patterns and anomalies. As a result, the analysis unit can gain a deeper understanding of pet communication and provide users with more accurate information.

[0031] The communication unit conveys the results analyzed by the analysis unit to the user. For example, the communication unit uses speech synthesis technology to deliver messages to the user. Specifically, based on the analysis unit's analysis of the pet's vocalizations and behavior, the communication unit uses speech synthesis technology to convey the pet's intentions and emotions to the user in voice. For example, if the pet barks "I'm hungry," the communication unit can notify the user via voice, "Your pet wants food." The communication unit can also generate and send text messages to the user. For example, it can notify the user of the pet's status and requests via text message through a smartphone app. This allows the user to understand the pet's status in real time. Furthermore, the communication unit can also deliver messages to the user through app notifications. For example, if the pet is feeling anxious, it can notify the user via app notification, "Your pet is feeling anxious. Please reassure them." This allows the communication unit to quickly and effectively convey the results analyzed by the analysis unit to the user. Additionally, the communication unit can customize notification methods according to user preferences. For example, it can notify users who prefer voice notifications via voice, and users who prefer text notifications via text. This allows the communication unit to provide information to the user in the most optimal way, facilitating smoother communication with pets.

[0032] The detection unit detects anxiety and stress in pets based on the results analyzed by the analysis unit. Specifically, it analyzes changes in the pet's vocalizations and behavior to detect anxiety and stress. For example, if a pet makes unusual vocalizations or exhibits abnormal behavior, the detection unit analyzes this and determines that the pet may be experiencing anxiety or stress. The detection unit can also detect anxiety and stress by analyzing changes in the pet's heart rate and body temperature. For example, if a pet's heart rate suddenly increases or its body temperature becomes abnormally high, the detection unit analyzes this and determines that the pet is experiencing stress. Furthermore, the detection unit can analyze the pet's behavioral patterns and measure its stress level. For example, if a pet exhibits unusual behavioral patterns, the detection unit analyzes this and evaluates the pet's stress level. This allows the detection unit to detect anxiety and stress in pets early and prompt the user to take appropriate action. Furthermore, the detection unit utilizes AI technology to learn patterns of anxiety and stress in pets, enabling more accurate analysis. For example, based on past data, it can learn how pets react in specific situations and environments, allowing for early detection of abnormal patterns. This enables the detection unit to more accurately understand the pet's health and emotions, supporting users in taking quick and appropriate action.

[0033] The communication unit can convey messages to the user using speech synthesis technology. For example, the communication unit can use text-to-speech (TTS) technology to convey analysis results to the user as an audio message. The communication unit can also use voice sample-based synthesis technology to reproduce pet sounds and convey them to the user. For example, the communication unit can use speech synthesis technology to analyze pet sounds and convey their meaning to the user as an audio message. In this way, speech synthesis technology can be used to convey pet sounds and behavior to the user in an easy-to-understand manner.

[0034] The detection unit can detect anxiety and stress in pets and provide advice to the user. For example, the detection unit can analyze changes in the pet's vocalizations and behavior to detect anxiety and stress. The detection unit can also analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. The detection unit can also analyze the pet's behavioral patterns to measure stress levels. This allows the detection of anxiety and stress in pets and the provision of appropriate advice to the user, thereby supporting the pet's health and well-being. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet vocalizations and behavioral data into a generating AI and have the generating AI perform the detection of the pet's anxiety and stress.

[0035] The analysis unit can analyze a pet's vocalizations and behavior to understand what it is trying to communicate. For example, the analysis unit can use speech recognition technology to analyze a pet's vocalizations and understand what those vocalizations mean. The analysis unit can also analyze a pet's behavior and interpret its meaning. For example, the analysis unit can analyze the frequency and volume of a pet's vocalizations and understand what those vocalizations mean. In this way, by analyzing a pet's vocalizations and behavior, the pet's intentions can be accurately understood. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input pet vocalization data into a generative AI and have the generative AI perform an analysis of the meaning of the vocalizations.

[0036] The communication unit can convey messages to the user based on the pet's sounds and behavior. For example, the communication unit can analyze the pet's sounds and generate a message to the user explaining their meaning. The communication unit can also analyze the pet's behavior and generate a message to the user explaining its meaning. The communication unit can also send text messages to the user based on the pet's sounds and behavior. This makes it easier for the user to understand the pet's condition by conveying messages based on the pet's sounds and behavior. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet sound and behavior data into a generating AI and have the generating AI generate messages.

[0037] The detection unit can detect anxiety and stress in pets and provide advice to the user, such as, "Your pet is feeling anxious, please reassure it." For example, the detection unit can analyze changes in the pet's vocalizations and behavior to detect anxiety and stress. The detection unit can also analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. For example, the detection unit can analyze the pet's behavioral patterns to measure stress levels. This allows the detection of the pet's anxiety and stress to be recognized, and by providing specific advice to the user, the pet's sense of security can be enhanced. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet vocalizations and behavioral data into a generating AI and have the generating AI perform the detection of the pet's anxiety and stress.

[0038] The analysis unit can estimate the pet's emotions and adjust the analysis method for vocalizations and behaviors based on the estimated emotions. For example, if the pet is excited, the analysis unit may focus on the frequency and volume of vocalizations. If the pet is relaxed, the analysis unit may focus on behavioral patterns. If the pet is anxious, the analysis unit may focus on the tone of vocalizations and changes in behavior. By adjusting the analysis method based on the pet's emotions, the accuracy of the analysis is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI adjust the analysis method for vocalizations and behaviors.

[0039] The analysis unit can improve the accuracy of the analysis by referring to the pet's past behavioral history during the analysis. For example, the analysis unit can record the pet's behavior when it made a specific sound in the past and refer to it when a similar sound occurs. The analysis unit can also refer to the pet's past eating and exercise history to analyze its current behavior. For example, the analysis unit can record situations in which the pet felt anxious in the past and reflect this in the analysis when a similar situation occurs. In this way, the accuracy of the analysis is improved by referring to past behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, in the case of a young dog, the analysis unit can apply an analysis algorithm that emphasizes active behavior patterns. For example, in the case of an elderly cat, the analysis unit can also apply an analysis algorithm that emphasizes quiet behavior patterns. For example, the analysis unit can also apply an analysis algorithm that takes into account behavior patterns specific to a particular dog breed. By applying an analysis algorithm appropriate to the type and age of the pet, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet type and age data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0041] The analysis unit can estimate the pet's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the pet is feeling highly anxious, the analysis unit will convey that result to the user with the highest priority. If the pet is feeling mildly excited, the analysis unit may also give that result the next priority. If the pet is relaxed, the analysis unit may also give that result the last priority. In this way, by determining the priority of the analysis results based on the pet's emotions, important information can be conveyed to the user preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the pet's emotion data into the generative AI and have the generative AI perform the determination of the priority of the analysis results.

[0042] The analysis unit can perform analysis while taking into account the pet's health condition. For example, if the pet is ill, the analysis unit will focus on aspects that differ from its normal behavioral patterns. For example, if the pet is healthy, the analysis unit can also perform analysis based on its normal behavioral patterns. For example, if the pet is injured, the analysis unit can also take the effects of the injury into consideration. This allows for a more accurate analysis by taking the pet's health condition into account. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet health data into a generating AI and have the generating AI perform the analysis.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the pet owner's lifestyle patterns during the analysis. For example, if the pet barks during the time the owner is away at work, the analysis unit can reflect that behavior in the analysis. For example, if the pet gets excited around the time the owner returns home, the analysis unit can also reflect that behavior in the analysis. For example, if the pet relaxes around the time the owner plays with the pet, the analysis unit can also reflect that behavior in the analysis. In this way, the accuracy of the analysis is improved by referring to the owner's lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0044] The communication unit can estimate the pet's emotions and adjust the way the message is delivered based on the estimated emotions. For example, if the pet is feeling anxious, the communication unit will deliver the message in a gentle tone. If the pet is excited, the communication unit can also deliver the message in an energetic tone. If the pet is relaxed, the communication unit can also deliver the message in a calm tone. By adjusting the way the message is delivered based on the pet's emotions, more appropriate information can be conveyed to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input pet emotion data into the generative AI and have the generative AI adjust the way the message is delivered.

[0045] The communication unit can adjust the level of detail in the message based on the importance of the pet's behavior during transmission. For example, if the pet is exhibiting an urgent behavior, the communication unit will deliver a detailed message. For example, if the pet is exhibiting normal behavior, the communication unit can deliver a concise message. For example, if the pet is repeating a particular behavior, the communication unit can deliver details of that behavior. By adjusting the level of detail in the message based on the importance of the pet's behavior, the communication unit can appropriately convey the information the user needs. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet behavior data into a generating AI and have the generating AI perform the adjustment of message detail.

[0046] The transmission unit can apply different speech synthesis algorithms depending on the type of pet during transmission. For example, in the case of a dog, the transmission unit can apply a speech synthesis algorithm that closely resembles a dog's bark. For example, in the case of a cat, the transmission unit can also apply a speech synthesis algorithm that closely resembles a cat's meow. For example, in the case of a bird, the transmission unit can also apply a speech synthesis algorithm that closely resembles a bird's song. By applying a speech synthesis algorithm appropriate to the type of pet, a more natural message can be conveyed to the user. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input pet type data into a generating AI and have the generating AI perform the application of the speech synthesis algorithm.

[0047] The communication unit can estimate the pet's emotions and adjust the message length based on the estimated emotions. For example, if the pet is anxious, the communication unit can convey details in a longer message. If the pet is excited, the communication unit can convey concisely in a shorter message. If the pet is relaxed, the communication unit can convey a message of appropriate length. By adjusting the message length based on the pet's emotions, the system can provide the user with an appropriate amount of information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 communication unit may be performed using AI or not. For example, the communication unit can input pet emotion data into the generative AI and have the generative AI adjust the message length.

[0048] The communication unit can determine the priority of messages based on the timing of the pet's behavior during transmission. For example, if the pet is feeling anxious at night, the communication unit will deliver that message with the highest priority. If the pet is exhibiting normal behavior during the day, the communication unit may also prioritize that message next. If the pet is exhibiting a specific behavior at a specific time, the communication unit may also prioritize that message last. This allows important information to be quickly conveyed to the user by determining the priority of messages based on the timing of the pet's behavior. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet behavior data into a generating AI and have the generating AI determine the priority of messages.

[0049] The communication unit can adjust the order of messages based on the relevance of the pet's behavior during transmission. For example, if the pet exhibits a specific behavior consecutively, the communication unit will deliver messages related to that behavior consecutively. For example, if the pet exhibits different behaviors, the communication unit can also deliver messages related to each behavior in a specific order. For example, if the pet exhibits one behavior followed by another, the communication unit can deliver messages considering the relevance between those behaviors. This allows information to be effectively conveyed to the user by adjusting the order of messages based on the relevance of the pet's behavior. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet behavior data into a generating AI and have the generating AI perform the adjustment of the message order.

[0050] The sensing unit can estimate the pet's emotions and adjust its anxiety and stress detection methods based on the estimated emotions. For example, if the pet is experiencing strong anxiety, the sensing unit will prioritize detecting that emotion. For example, if the pet is experiencing mild stress, the sensing unit may prioritize that emotion next. For example, if the pet is relaxed, the sensing unit may prioritize that emotion last. This improves the accuracy of anxiety and stress detection by adjusting the detection method based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input pet emotion data into a generative AI and have the generative AI adjust its anxiety and stress detection methods.

[0051] The detection unit can improve the accuracy of its detection by referring to the pet's past stress history when detecting stress. For example, if the detection unit has previously experienced stress in a particular situation, it will use that situation as a reference for detection. The detection unit can also analyze the pet's past stress history and reflect that in its detection when a similar situation occurs. For example, the detection unit can record the time of day and place where the pet experienced stress in the past and reflect that in its detection when a similar situation occurs. This improves the accuracy of detection by referring to past stress history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the pet's past stress data into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0052] The detection unit can apply different detection algorithms depending on the type and age of the pet when detection occurs. For example, in the case of a young dog, the detection unit can apply a detection algorithm that emphasizes active behavior patterns. For example, in the case of an elderly cat, the detection unit can also apply a detection algorithm that emphasizes quiet behavior patterns. For example, the detection unit can also apply a detection algorithm that takes into account behavior patterns specific to a particular dog breed. By applying a detection algorithm appropriate to the type and age of the pet, the accuracy of detection is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet type and age data into a generating AI and have the generating AI execute the application of the detection algorithm.

[0053] The sensing unit can estimate the pet's emotions and, based on the estimated emotions, determine the priority of anxiety and stress. For example, if the pet is experiencing strong anxiety, the sensing unit will prioritize that emotion. If the pet is experiencing mild stress, the sensing unit may also prioritize that emotion. If the pet is relaxed, the sensing unit may also prioritize that emotion last. This allows important information to be conveyed to the user preferentially by determining the priority of anxiety and stress based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the sensing unit may be performed using AI or not. For example, the sensing unit can input the pet's emotion data into a generative AI and have the generative AI determine the priority of anxiety and stress.

[0054] The detection unit can perform detection while taking into account the pet's health condition. For example, if the pet is ill, the detection unit will focus on the differences from its normal behavior patterns. For example, if the pet is healthy, the detection unit can also perform detection based on its normal behavior patterns. For example, if the pet is injured, the detection unit can also perform detection while considering the effects of the injury. This allows for more accurate detection by taking the pet's health condition into account. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet health data into a generating AI and have the generating AI perform the detection.

[0055] The detection unit can improve the accuracy of its detection by referring to the pet owner's lifestyle patterns when it detects something. For example, if the pet feels anxious during the time the owner is away at work, the detection unit can reflect that situation in its detection. For example, if the pet is relaxed when the owner returns home, the detection unit can also reflect that situation in its detection. For example, if the pet is excited during the time the owner plays with the pet, the detection unit can also reflect that situation in its detection. In this way, the accuracy of detection is improved by referring to the owner's lifestyle patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also add functions to monitor their health. For example, they can regularly measure the pet's body temperature and heart rate, and notify the user if an abnormality is detected. Furthermore, they can record the pet's eating and exercise history and provide health management advice. This allows owners to constantly monitor their pet's health and detect abnormalities early.

[0058] Pet communication systems can not only analyze pets' barks and behaviors, but also add the ability to learn and predict pet behavior patterns. For example, if a pet tends to perform a specific action at a particular time of day, the system can predict that action and notify the user. Furthermore, if a pet is likely to experience stress in a particular situation, the system can predict that situation and provide advice to the user. This allows users to predict their pet's behavior and take preventative measures.

[0059] Pet communication systems can not only analyze pets' barks and behaviors, but also add features to save pet behavior data to the cloud and share it with other users. For example, uploading pet behavior data to the cloud and comparing it with other users can help detect abnormalities in pet behavior early. Furthermore, by referring to other users' pet behavior data, it's possible to improve pet training methods. This allows for the sharing of pet behavior data, enabling more effective training and health management.

[0060] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also learn their preferences based on behavioral data and add features to suggest them to the user. For example, if a pet likes a particular food, the system can suggest that food to the user. Furthermore, if a pet enjoys a particular game, the system can suggest that game to the user. This allows for appropriate suggestions based on the pet's preferences, thereby improving the pet's satisfaction.

[0061] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also add the functionality to create training plans based on pet behavior data. For example, if a pet repeatedly engages in a particular behavior, the system can create a training plan to improve that behavior. Furthermore, it can also create training plans to help pets acquire specific skills. This allows for the creation of effective training plans based on pet behavior data, thereby improving pet skills.

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

[0063] Step 1: The analysis unit analyzes the pet's vocalizations and behavior. The analysis unit uses speech recognition technology to analyze the pet's vocalizations and understand what it is trying to communicate. It can also analyze the pet's behavior and interpret its meaning. Furthermore, it analyzes the frequency and volume of the pet's vocalizations to understand what those vocalizations signify. Step 2: The transmission unit communicates the results analyzed by the analysis unit to the user. The transmission unit can use speech synthesis technology to deliver messages to the user. It can also generate text messages and notify the user. Furthermore, it can deliver messages to the user through app notifications. Step 3: The detection unit detects the pet's anxiety and stress based on the results analyzed by the analysis unit. The detection unit analyzes changes in the pet's vocalizations and behavior to detect anxiety and stress. It can also analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. Furthermore, it can analyze the pet's behavioral patterns to measure stress levels.

[0064] (Example of form 2) The pet communication system according to an embodiment of the present invention is a system that analyzes the sounds and behavior of pets and assists in communication with users. The pet communication system analyzes the sounds and behavior of pets, communicates this information to the user, and senses anxiety and stress, thereby facilitating communication between pets and humans. For example, the pet communication system analyzes the sounds and behavior of pets. The pet communication system uses speech recognition technology to analyze the sounds of pets and understand what they are trying to communicate. Next, the pet communication system uses speech synthesis technology to convey a message to the user. Furthermore, the pet communication system senses the anxiety and stress of pets and provides advice to the user. For example, if a pet is barking to ask for food or water, the pet communication system can analyze the sounds and convey a message to the user such as, "It seems the pet is hungry." Also, if the pet communication system senses that the pet is feeling anxious or stressed, it will sense this and advise the user, "Your pet is feeling anxious, so please reassure it." In this way, the pet communication system facilitates smoother communication between pets and humans and helps to understand the needs and emotions of pets. Especially for people experiencing pet loss, the ability to recreate the behavior and sounds of their deceased pet through the pet communication system allows them to relive memories and bring emotional stability and healing. The system analyzes the pet's sounds and behavior, communicates them to the user, and senses anxiety and stress, thereby facilitating smoother communication between pets and humans.

[0065] The pet communication system according to this embodiment comprises an analysis unit, a transmission unit, and a detection unit. The analysis unit analyzes the pet's vocalizations and behavior. The analysis unit, for example, uses speech recognition technology to analyze the pet's vocalizations and understand what it is trying to communicate. The analysis unit can also, for example, analyze the pet's behavior and interpret the meaning of that behavior. The analysis unit can, for example, analyze the frequency and volume of the pet's vocalizations and understand what those vocalizations mean. The transmission unit conveys the results analyzed by the analysis unit to the user. The transmission unit conveys messages to the user, for example, using speech synthesis technology. The transmission unit can also, for example, generate text messages and notify the user. The transmission unit can also, for example, convey messages to the user through app notifications. The detection unit detects the pet's anxiety and stress based on the results analyzed by the analysis unit. The detection unit detects anxiety and stress, for example, by analyzing changes in the pet's vocalizations and behavior. The sensing unit can, for example, analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. The sensing unit can also, for example, analyze the pet's behavioral patterns to measure stress levels. As a result, the pet communication system according to this embodiment can analyze the pet's vocalizations and behavior, communicate this information to the user, and detect anxiety and stress, thereby facilitating smooth communication between pets and humans.

[0066] The analysis unit analyzes the sounds and behaviors of pets. For example, it uses speech recognition technology to analyze pet sounds and understand what the pet is trying to communicate. Specifically, speech recognition technology extracts features such as frequency, volume, rhythm, and tone of the pet's sounds and interprets the meaning of the sounds based on these features. For example, a high-pitched, short bark in a dog may indicate excitement or joy, while a low-pitched, long bark may indicate alertness or anxiety. The analysis unit learns these patterns and can analyze the meaning of sounds with high accuracy. The analysis unit can also analyze pet behavior and interpret the meaning of that behavior. For example, it analyzes actions such as a cat wagging its tail or perking up its ears to understand the emotions and intentions indicated by these actions. Furthermore, the analysis unit analyzes the frequency and volume of pet sounds to understand what those sounds mean. For example, it analyzes the changes in frequency band and volume when a pet makes a specific sound to identify which emotion the sound represents, such as a request, dissatisfaction, or joy. This allows the analysis unit to analyze pet vocalizations and behaviors in detail and accurately understand their meaning. Furthermore, the analysis unit can utilize AI technology to continuously learn patterns of pet vocalizations and behaviors, improving analysis accuracy. For example, it can use deep learning to learn from large amounts of vocalization and behavior data to detect new patterns and anomalies. As a result, the analysis unit can gain a deeper understanding of pet communication and provide users with more accurate information.

[0067] The communication unit conveys the results analyzed by the analysis unit to the user. For example, the communication unit uses speech synthesis technology to deliver messages to the user. Specifically, based on the analysis unit's analysis of the pet's vocalizations and behavior, the communication unit uses speech synthesis technology to convey the pet's intentions and emotions to the user in voice. For example, if the pet barks "I'm hungry," the communication unit can notify the user via voice, "Your pet wants food." The communication unit can also generate and send text messages to the user. For example, it can notify the user of the pet's status and requests via text message through a smartphone app. This allows the user to understand the pet's status in real time. Furthermore, the communication unit can also deliver messages to the user through app notifications. For example, if the pet is feeling anxious, it can notify the user via app notification, "Your pet is feeling anxious. Please reassure them." This allows the communication unit to quickly and effectively convey the results analyzed by the analysis unit to the user. Additionally, the communication unit can customize notification methods according to user preferences. For example, it can notify users who prefer voice notifications via voice, and users who prefer text notifications via text. This allows the communication unit to provide information to the user in the most optimal way, facilitating smoother communication with pets.

[0068] The detection unit detects anxiety and stress in pets based on the results analyzed by the analysis unit. Specifically, it analyzes changes in the pet's vocalizations and behavior to detect anxiety and stress. For example, if a pet makes unusual vocalizations or exhibits abnormal behavior, the detection unit analyzes this and determines that the pet may be experiencing anxiety or stress. The detection unit can also detect anxiety and stress by analyzing changes in the pet's heart rate and body temperature. For example, if a pet's heart rate suddenly increases or its body temperature becomes abnormally high, the detection unit analyzes this and determines that the pet is experiencing stress. Furthermore, the detection unit can analyze the pet's behavioral patterns and measure its stress level. For example, if a pet exhibits unusual behavioral patterns, the detection unit analyzes this and evaluates the pet's stress level. This allows the detection unit to detect anxiety and stress in pets early and prompt the user to take appropriate action. Furthermore, the detection unit utilizes AI technology to learn patterns of anxiety and stress in pets, enabling more accurate analysis. For example, based on past data, it can learn how pets react in specific situations and environments, allowing for early detection of abnormal patterns. This enables the detection unit to more accurately understand the pet's health and emotions, supporting users in taking quick and appropriate action.

[0069] The communication unit can convey messages to the user using speech synthesis technology. For example, the communication unit can use text-to-speech (TTS) technology to convey analysis results to the user as an audio message. The communication unit can also use voice sample-based synthesis technology to reproduce pet sounds and convey them to the user. For example, the communication unit can use speech synthesis technology to analyze pet sounds and convey their meaning to the user as an audio message. In this way, speech synthesis technology can be used to convey pet sounds and behavior to the user in an easy-to-understand manner.

[0070] The detection unit can detect anxiety and stress in pets and provide advice to the user. For example, the detection unit can analyze changes in the pet's vocalizations and behavior to detect anxiety and stress. The detection unit can also analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. The detection unit can also analyze the pet's behavioral patterns to measure stress levels. This allows the detection of anxiety and stress in pets and the provision of appropriate advice to the user, thereby supporting the pet's health and well-being. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet vocalizations and behavioral data into a generating AI and have the generating AI perform the detection of the pet's anxiety and stress.

[0071] The analysis unit can analyze a pet's vocalizations and behavior to understand what it is trying to communicate. For example, the analysis unit can use speech recognition technology to analyze a pet's vocalizations and understand what those vocalizations mean. The analysis unit can also analyze a pet's behavior and interpret its meaning. For example, the analysis unit can analyze the frequency and volume of a pet's vocalizations and understand what those vocalizations mean. In this way, by analyzing a pet's vocalizations and behavior, the pet's intentions can be accurately understood. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input pet vocalization data into a generative AI and have the generative AI perform an analysis of the meaning of the vocalizations.

[0072] The communication unit can convey messages to the user based on the pet's sounds and behavior. For example, the communication unit can analyze the pet's sounds and generate a message to the user explaining their meaning. The communication unit can also analyze the pet's behavior and generate a message to the user explaining its meaning. The communication unit can also send text messages to the user based on the pet's sounds and behavior. This makes it easier for the user to understand the pet's condition by conveying messages based on the pet's sounds and behavior. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet sound and behavior data into a generating AI and have the generating AI generate messages.

[0073] The detection unit can detect anxiety and stress in pets and provide advice to the user, such as, "Your pet is feeling anxious, please reassure it." For example, the detection unit can analyze changes in the pet's vocalizations and behavior to detect anxiety and stress. The detection unit can also analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. For example, the detection unit can analyze the pet's behavioral patterns to measure stress levels. This allows the detection of the pet's anxiety and stress to be recognized, and by providing specific advice to the user, the pet's sense of security can be enhanced. Some or all of the above-described processes in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet vocalizations and behavioral data into a generating AI and have the generating AI perform the detection of the pet's anxiety and stress.

[0074] The analysis unit can estimate the pet's emotions and adjust the analysis method for vocalizations and behaviors based on the estimated emotions. For example, if the pet is excited, the analysis unit may focus on the frequency and volume of vocalizations. If the pet is relaxed, the analysis unit may focus on behavioral patterns. If the pet is anxious, the analysis unit may focus on the tone of vocalizations and changes in behavior. By adjusting the analysis method based on the pet's emotions, the accuracy of the analysis is improved. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input pet emotion data into a generative AI and have the generative AI adjust the analysis method for vocalizations and behaviors.

[0075] The analysis unit can improve the accuracy of the analysis by referring to the pet's past behavioral history during the analysis. For example, the analysis unit can record the pet's behavior when it made a specific sound in the past and refer to it when a similar sound occurs. The analysis unit can also refer to the pet's past eating and exercise history to analyze its current behavior. For example, the analysis unit can record situations in which the pet felt anxious in the past and reflect this in the analysis when a similar situation occurs. In this way, the accuracy of the analysis is improved by referring to past behavioral history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the pet's past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the type and age of the pet during analysis. For example, in the case of a young dog, the analysis unit can apply an analysis algorithm that emphasizes active behavior patterns. For example, in the case of an elderly cat, the analysis unit can also apply an analysis algorithm that emphasizes quiet behavior patterns. For example, the analysis unit can also apply an analysis algorithm that takes into account behavior patterns specific to a particular dog breed. By applying an analysis algorithm appropriate to the type and age of the pet, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet type and age data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0077] The analysis unit can estimate the pet's emotions and determine the priority of the analysis results based on the estimated emotions. For example, if the pet is feeling highly anxious, the analysis unit will convey that result to the user with the highest priority. If the pet is feeling mildly excited, the analysis unit may also give that result the next priority. If the pet is relaxed, the analysis unit may also give that result the last priority. In this way, by determining the priority of the analysis results based on the pet's emotions, important information can be conveyed to the user preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the pet's emotion data into the generative AI and have the generative AI perform the determination of the priority of the analysis results.

[0078] The analysis unit can perform analysis while taking into account the pet's health condition. For example, if the pet is ill, the analysis unit will focus on aspects that differ from its normal behavioral patterns. For example, if the pet is healthy, the analysis unit can also perform analysis based on its normal behavioral patterns. For example, if the pet is injured, the analysis unit can also take the effects of the injury into consideration. This allows for a more accurate analysis by taking the pet's health condition into account. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input pet health data into a generating AI and have the generating AI perform the analysis.

[0079] The analysis unit can improve the accuracy of its analysis by referring to the pet owner's lifestyle patterns during the analysis. For example, if the pet barks during the time the owner is away at work, the analysis unit can reflect that behavior in the analysis. For example, if the pet gets excited around the time the owner returns home, the analysis unit can also reflect that behavior in the analysis. For example, if the pet relaxes around the time the owner plays with the pet, the analysis unit can also reflect that behavior in the analysis. In this way, the accuracy of the analysis is improved by referring to the owner's lifestyle patterns. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0080] The communication unit can estimate the pet's emotions and adjust the way the message is delivered based on the estimated emotions. For example, if the pet is feeling anxious, the communication unit will deliver the message in a gentle tone. If the pet is excited, the communication unit can also deliver the message in an energetic tone. If the pet is relaxed, the communication unit can also deliver the message in a calm tone. By adjusting the way the message is delivered based on the pet's emotions, more appropriate information can be conveyed to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The 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 communication unit may be performed using AI, for example, or not using AI. For example, the communication unit can input pet emotion data into the generative AI and have the generative AI adjust the way the message is delivered.

[0081] The communication unit can adjust the level of detail in the message based on the importance of the pet's behavior during transmission. For example, if the pet is exhibiting an urgent behavior, the communication unit will deliver a detailed message. For example, if the pet is exhibiting normal behavior, the communication unit can deliver a concise message. For example, if the pet is repeating a particular behavior, the communication unit can deliver details of that behavior. By adjusting the level of detail in the message based on the importance of the pet's behavior, the communication unit can appropriately convey the information the user needs. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet behavior data into a generating AI and have the generating AI perform the adjustment of message detail.

[0082] The transmission unit can apply different speech synthesis algorithms depending on the type of pet during transmission. For example, in the case of a dog, the transmission unit can apply a speech synthesis algorithm that closely resembles a dog's bark. For example, in the case of a cat, the transmission unit can also apply a speech synthesis algorithm that closely resembles a cat's meow. For example, in the case of a bird, the transmission unit can also apply a speech synthesis algorithm that closely resembles a bird's song. By applying a speech synthesis algorithm appropriate to the type of pet, a more natural message can be conveyed to the user. Some or all of the above processing in the transmission unit may be performed using AI, for example, or without AI. For example, the transmission unit can input pet type data into a generating AI and have the generating AI perform the application of the speech synthesis algorithm.

[0083] The communication unit can estimate the pet's emotions and adjust the message length based on the estimated emotions. For example, if the pet is anxious, the communication unit can convey details in a longer message. If the pet is excited, the communication unit can convey concisely in a shorter message. If the pet is relaxed, the communication unit can convey a message of appropriate length. By adjusting the message length based on the pet's emotions, the system can provide the user with an appropriate amount of information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 communication unit may be performed using AI or not. For example, the communication unit can input pet emotion data into the generative AI and have the generative AI adjust the message length.

[0084] The communication unit can determine the priority of messages based on the timing of the pet's behavior during transmission. For example, if the pet is feeling anxious at night, the communication unit will deliver that message with the highest priority. If the pet is exhibiting normal behavior during the day, the communication unit may also prioritize that message next. If the pet is exhibiting a specific behavior at a specific time, the communication unit may also prioritize that message last. This allows important information to be quickly conveyed to the user by determining the priority of messages based on the timing of the pet's behavior. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet behavior data into a generating AI and have the generating AI determine the priority of messages.

[0085] The communication unit can adjust the order of messages based on the relevance of the pet's behavior during transmission. For example, if the pet exhibits a specific behavior consecutively, the communication unit will deliver messages related to that behavior consecutively. For example, if the pet exhibits different behaviors, the communication unit can also deliver messages related to each behavior in a specific order. For example, if the pet exhibits one behavior followed by another, the communication unit can deliver messages considering the relevance between those behaviors. This allows information to be effectively conveyed to the user by adjusting the order of messages based on the relevance of the pet's behavior. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input pet behavior data into a generating AI and have the generating AI perform the adjustment of the message order.

[0086] The sensing unit can estimate the pet's emotions and adjust its anxiety and stress detection methods based on the estimated emotions. For example, if the pet is experiencing strong anxiety, the sensing unit will prioritize detecting that emotion. For example, if the pet is experiencing mild stress, the sensing unit may prioritize that emotion next. For example, if the pet is relaxed, the sensing unit may prioritize that emotion last. This improves the accuracy of anxiety and stress detection by adjusting the detection method based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input pet emotion data into a generative AI and have the generative AI adjust its anxiety and stress detection methods.

[0087] The detection unit can improve the accuracy of its detection by referring to the pet's past stress history when detecting stress. For example, if the detection unit has previously experienced stress in a particular situation, it will use that situation as a reference for detection. The detection unit can also analyze the pet's past stress history and reflect that in its detection when a similar situation occurs. For example, the detection unit can record the time of day and place where the pet experienced stress in the past and reflect that in its detection when a similar situation occurs. This improves the accuracy of detection by referring to past stress history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the pet's past stress data into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0088] The detection unit can apply different detection algorithms depending on the type and age of the pet when detection occurs. For example, in the case of a young dog, the detection unit can apply a detection algorithm that emphasizes active behavior patterns. For example, in the case of an elderly cat, the detection unit can also apply a detection algorithm that emphasizes quiet behavior patterns. For example, the detection unit can also apply a detection algorithm that takes into account behavior patterns specific to a particular dog breed. By applying a detection algorithm appropriate to the type and age of the pet, the accuracy of detection is improved. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet type and age data into a generating AI and have the generating AI execute the application of the detection algorithm.

[0089] The sensing unit can estimate the pet's emotions and, based on the estimated emotions, determine the priority of anxiety and stress. For example, if the pet is experiencing strong anxiety, the sensing unit will prioritize that emotion. If the pet is experiencing mild stress, the sensing unit may also prioritize that emotion. If the pet is relaxed, the sensing unit may also prioritize that emotion last. This allows important information to be conveyed to the user preferentially by determining the priority of anxiety and stress based on the pet's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the sensing unit may be performed using AI or not. For example, the sensing unit can input the pet's emotion data into a generative AI and have the generative AI determine the priority of anxiety and stress.

[0090] The detection unit can perform detection while taking into account the pet's health condition. For example, if the pet is ill, the detection unit will focus on the differences from its normal behavior patterns. For example, if the pet is healthy, the detection unit can also perform detection based on its normal behavior patterns. For example, if the pet is injured, the detection unit can also perform detection while considering the effects of the injury. This allows for more accurate detection by taking the pet's health condition into account. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input pet health data into a generating AI and have the generating AI perform the detection.

[0091] The detection unit can improve the accuracy of its detection by referring to the pet owner's lifestyle patterns when it detects something. For example, if the pet feels anxious during the time the owner is away at work, the detection unit can reflect that situation in its detection. For example, if the pet is relaxed when the owner returns home, the detection unit can also reflect that situation in its detection. For example, if the pet is excited during the time the owner plays with the pet, the detection unit can also reflect that situation in its detection. In this way, the accuracy of detection is improved by referring to the owner's lifestyle patterns. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input the owner's lifestyle pattern data into a generating AI and have the generating AI perform the task of improving the accuracy of detection.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also add functions to monitor their health. For example, they can regularly measure the pet's body temperature and heart rate, and notify the user if an abnormality is detected. Furthermore, they can record the pet's eating and exercise history and provide health management advice. This allows owners to constantly monitor their pet's health and detect abnormalities early.

[0094] The pet communication system not only analyzes the pet's vocalizations and behavior, but can also estimate the pet's emotions and provide advice to the user based on those emotions. For example, if the pet is feeling anxious, it will advise the user, "Your pet is feeling anxious, so please reassure it." Furthermore, if the pet is excited, it can advise the user, "Your pet is excited, so please calm it down." This allows the system to provide appropriate advice based on the pet's emotions.

[0095] Pet communication systems can not only analyze pets' barks and behaviors, but also add the ability to learn and predict pet behavior patterns. For example, if a pet tends to perform a specific action at a particular time of day, the system can predict that action and notify the user. Furthermore, if a pet is likely to experience stress in a particular situation, the system can predict that situation and provide advice to the user. This allows users to predict their pet's behavior and take preventative measures.

[0096] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also estimate their emotions and guide their behavior based on those emotions. For example, if a pet is feeling anxious, it can play relaxing music. Furthermore, if a pet is excited, it can play a calming voice message. This allows for the guidance of appropriate behavior based on the pet's emotions.

[0097] Pet communication systems can not only analyze pets' barks and behaviors, but also add features to save pet behavior data to the cloud and share it with other users. For example, uploading pet behavior data to the cloud and comparing it with other users can help detect abnormalities in pet behavior early. Furthermore, by referring to other users' pet behavior data, it's possible to improve pet training methods. This allows for the sharing of pet behavior data, enabling more effective training and health management.

[0098] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also estimate their emotions and monitor their health based on those emotions. For example, if a pet is feeling anxious, the system can monitor changes in heart rate and body temperature, and notify the user if any abnormalities are detected. Furthermore, if a pet is relaxed, the system can inform the user that their health is good. This allows for monitoring a pet's health based on their emotions, enabling early detection of abnormalities.

[0099] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also learn their preferences based on behavioral data and add features to suggest them to the user. For example, if a pet likes a particular food, the system can suggest that food to the user. Furthermore, if a pet enjoys a particular game, the system can suggest that game to the user. This allows for appropriate suggestions based on the pet's preferences, thereby improving the pet's satisfaction.

[0100] The pet communication system can not only analyze the pet's vocalizations and behavior, but also add a function to estimate the pet's emotions and suggest ways to reduce the pet's stress based on those emotions. For example, if the pet is feeling anxious, it can suggest massage techniques to help the user relax. Furthermore, if the pet is stressed, it can suggest games to help reduce stress. This allows the system to suggest appropriate stress reduction methods based on the pet's emotions.

[0101] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also add the functionality to create training plans based on pet behavior data. For example, if a pet repeatedly engages in a particular behavior, the system can create a training plan to improve that behavior. Furthermore, it can also create training plans to help pets acquire specific skills. This allows for the creation of effective training plans based on pet behavior data, thereby improving pet skills.

[0102] Pet communication systems can not only analyze pets' vocalizations and behaviors, but also add features to estimate pets' emotions and record their behavior based on those emotions. For example, if a pet is feeling anxious, its behavior can be recorded for later review by the user. Furthermore, if a pet is excited, its behavior can be recorded and the user notified. This allows for recording behavior based on the pet's emotions, making it easier for users to understand their pet's state.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The analysis unit analyzes the pet's vocalizations and behavior. The analysis unit uses speech recognition technology to analyze the pet's vocalizations and understand what it is trying to communicate. It can also analyze the pet's behavior and interpret its meaning. Furthermore, it analyzes the frequency and volume of the pet's vocalizations to understand what those vocalizations signify. Step 2: The transmission unit communicates the results analyzed by the analysis unit to the user. The transmission unit can use speech synthesis technology to deliver messages to the user. It can also generate text messages and notify the user. Furthermore, it can deliver messages to the user through app notifications. Step 3: The detection unit detects the pet's anxiety and stress based on the results analyzed by the analysis unit. The detection unit analyzes changes in the pet's vocalizations and behavior to detect anxiety and stress. It can also analyze changes in the pet's heart rate and body temperature to detect anxiety and stress. Furthermore, it can analyze the pet's behavioral patterns to measure stress levels.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the analysis unit, transmission unit, and detection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 38B of the smart device 14 to detect the pet's barks and behavior, and the control unit 46A analyzes them. The transmission unit, for example, uses the specific processing unit 290 of the data processing unit 12 to communicate the analysis results to the user using speech synthesis technology. The detection unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the pet's anxiety and stress and provides advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the analysis unit, transmission unit, and detection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to detect the pet's barks and behavior, and the control unit 46A analyzes them. The transmission unit, for example, uses the specific processing unit 290 of the data processing unit 12 to convey the analysis results to the user using speech synthesis technology. The detection unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the pet's anxiety and stress and provides advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements, including the analysis unit, transmission unit, and detection unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the headset terminal 314 to detect the pet's barks and behavior, and the control unit 46A analyzes them. The transmission unit, for example, uses the specific processing unit 290 of the data processing unit 12 to convey the analysis results to the user using speech synthesis technology. The detection unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the pet's anxiety and stress and provides advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the analysis unit, transmission unit, and detection unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the robot 414 to detect the pet's barks and behavior, and the control unit 46A analyzes them. The transmission unit, for example, uses the specific processing unit 290 of the data processing unit 12 to convey the analysis results to the user using speech synthesis technology. The detection unit, for example, uses the specific processing unit 290 of the data processing unit 12 to analyze the pet's anxiety and stress and provides advice to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) An analysis unit that analyzes the sounds and behavior of pets, A transmission unit that conveys the results of the analysis performed by the aforementioned analysis unit to the user, The system includes a detection unit that detects the anxiety and stress of a pet based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned transmission unit is Using speech synthesis technology to deliver messages to users The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned detection unit, It detects anxiety and stress in pets and provides advice to users. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze your pet's vocalizations and behavior to understand what they are trying to communicate. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmission unit is Messages are conveyed to the user based on the sounds and behaviors of pets. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned detection unit, It detects anxiety and stress in pets and advises users, "Your pet is feeling anxious, so please try to reassure them." The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, We estimate the pet's emotions and adjust the analysis methods for their vocalizations and behaviors based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the pet's past behavioral history is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the pet's emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During the analysis, the pet's health condition will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referencing the lifestyle patterns of pet owners. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned transmission unit is It estimates the pet's emotions and adjusts the way messages are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned transmission unit is When communicating, adjust the level of detail in the message based on the importance of the pet's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned transmission unit is When communicating, different speech synthesis algorithms are applied depending on the type of pet. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned transmission unit is It estimates the pet's emotions and adjusts the message length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned transmission unit is When communicating, prioritize messages based on when the pet's behavior occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned transmission unit is When communicating, the order of messages is adjusted based on the relevance of the pet's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned detection unit, It estimates the pet's emotions and adjusts the methods for detecting anxiety and stress based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned detection unit, When detecting stress, the system improves detection accuracy by referring to the pet's past stress history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned detection unit, When detection occurs, different detection algorithms are applied depending on the type and age of the pet. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned detection unit, It estimates the pet's emotions and prioritizes anxiety and stress based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned detection unit, When detecting a problem, the detection process takes the pet's health condition into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned detection unit, When detecting a pet, the system improves detection accuracy by referencing the pet owner's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0177] 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 analysis unit that analyzes the sounds and behavior of pets, A transmission unit that conveys the results of the analysis performed by the aforementioned analysis unit to the user, The system includes a detection unit that detects the anxiety and stress of a pet based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned transmission unit is Using speech synthesis technology to deliver messages to users The system according to feature 1.

3. The aforementioned detection unit, It detects anxiety and stress in pets and provides advice to users. The system according to feature 1.

4. The aforementioned analysis unit, Analyze your pet's vocalizations and behavior to understand what they are trying to communicate. The system according to feature 1.

5. The aforementioned transmission unit is Messages are conveyed to the user based on the sounds and behaviors of pets. The system according to feature 1.

6. The aforementioned analysis unit, We estimate the pet's emotions and adjust the analysis methods for their vocalizations and behaviors based on those estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, During analysis, the pet's past behavioral history is referenced to improve the accuracy of the analysis. The system according to feature 1.

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