system

Abstract

The system according to the embodiment aims to analyze the cries of pets, verbalize their intentions, and provide the verbalization to the pet owner. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the cries of a pet. The analysis unit analyzes the cries collected by the collection unit and extracts characteristics of the pitch of the voice or the way the voice is extended. The generation unit verbalizes the pet's intentions based on the characteristics extracted by the analysis unit. The provision unit provides the owner with the message generated by the generation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to accurately understand the intention of a pet from its cries, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the cries of pets, verbalize their intentions, and provide the verbalization to the pet owner. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects sounds made by the pet. The analysis unit analyzes the sounds collected by the collection unit and extracts characteristics of the pitch of the voice or the way the voice is extended. The generation unit verbalizes the pet's intentions based on the characteristics extracted by the analysis unit. The provision unit provides the owner with the message generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the cries of pets, verbalize their intentions, and provide the verbalized information to the pet owner. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A pet voice analysis system according to an embodiment of the present invention collects pet voices and uses a generation AI to analyze and verbalize them. The pet voice analysis system collects and analyzes pet voices, and the generation AI verbalizes the pet's intentions to provide a message to the owner. For example, the pet voice analysis system may be configured to install a high-precision microphone to collect pet voices. Next, an analysis unit operates to analyze the collected pet voices and extract features such as the pitch of the voice and how the voice is extended. Based on the analyzed features, the generation AI verbalizes the pet's intentions. For example, a message such as "I'm hungry" or "Take me for a walk" is generated. Finally, a provision unit operates to provide the generated message to the owner. This allows the owner to more easily understand the pet's feelings and improves communication with the pet. The pet voice analysis system analyzes the pet's voices and verbalizes them using the generation AI, allowing the owner to better understand the pet's feelings. For example, if the pet says "I'm hungry," the owner can immediately provide food. Also, if a pet cries "take me for a walk," the owner can take the pet for a walk, thus improving communication with the pet and improving the quality of life of the pet.

[0029] A pet voice analysis system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects pet voices. Pet voices include, but are not limited to, the meows of dogs and cats. The collection unit collects pet voices using, for example, a high-precision microphone. The collection unit can also collect pet voices in real time. The collection unit can also record pet voices and save them for later analysis. For example, the collection unit collects pet voices using a high-sensitivity microphone while filtering out ambient noise. The analysis unit analyzes the pet voices collected by the collection unit and extracts features such as voice pitch and voice extension. The analysis unit extracts features of the collected voices using audio signal processing techniques such as Fourier transform and Mel Frequency Cepstrum Coefficients (MFCC). The analysis unit can also use AI to analyze the features of the voices with high precision. For example, the analysis unit analyzes the frequency components of the pet's bark using a Fourier transform. The analysis unit can also extract acoustic features of the bark using Mel frequency cepstrum coefficients. Furthermore, the analysis unit can use AI to learn the features of the bark and improve the analysis accuracy. The generation unit verbalizes the pet's intentions based on the features extracted by the analysis unit. The generation unit, for example, uses a generation AI to generate messages corresponding to the features of the pet's bark. The generation AI learns from a large amount of pet bark data collected in advance. This data is labeled by experts and is highly reliable. The generation unit generates messages corresponding to the features of the pet's bark based on this data. For example, the generation unit generates messages such as "I'm hungry" or "Take me for a walk." The provision unit provides the messages generated by the generation unit to the owner. For example, the provision unit displays the generated messages on the owner's smartphone or tablet. The provision unit can also play the generated messages aloud. Furthermore, the provision unit has a function to notify the owner of the generated messages. For example, the providing unit notifies the owner of the generated message via a smartphone app.As a result, the pet cry analysis system according to the embodiment analyzes the pet's cry and verbalizes it using the generation AI, making it easier for the owner to understand the pet's feelings. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit inputs the features extracted by the analysis unit into the generation AI and causes the generation AI to execute a message verbalizing the pet's intentions.

[0030] The collection unit includes a high-precision microphone for collecting pet cries with high accuracy. The collection unit includes, for example, a high-precision microphone for collecting pet cries with high accuracy. The high-precision microphone is, for example, a microphone with high sensitivity and a wide frequency response. The high-precision microphone may also have a noise-canceling function. For example, the collection unit uses a microphone with high sensitivity and a wide frequency response to accurately collect pet cries. The collection unit can also use a microphone with a noise-canceling function to collect pet cries while removing ambient noise. Furthermore, the collection unit includes a high-precision microphone for collecting pet cries in real time. In this way, the use of the high-precision microphone allows for accurate collection of pet cries. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the cries data collected by the high-precision microphone into AI and have the AI ​​perform noise removal and sound quality improvement processing.

[0031] The analysis unit can extract features of the collected calls using audio signal processing techniques such as Fourier transform or Mel Frequency Cepstrum Coefficients (MFCC). The analysis unit analyzes the frequency components of the collected calls using, for example, Fourier transform. For example, the analysis unit analyzes the frequency components of the calls using a Discrete Fourier Transform (DFT). The analysis unit can also quickly analyze the frequency components of the calls using a Fast Fourier Transform (FFT). Furthermore, the analysis unit can extract acoustic features of the collected calls using Mel Frequency Cepstrum Coefficients (MFCC). For example, the analysis unit sets a filter bank and adjusts the number of cepstrum dimensions to extract acoustic features of the calls with high accuracy. This enables the use of audio signal processing techniques to extract features of the calls with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input collected bird call data into AI and have the AI ​​perform Fourier transforms and MFCC calculations.

[0032] The generation unit can learn from data of multiple pet sounds collected in advance and generate messages corresponding to the characteristics of the pet's sounds. The generation unit, for example, learns from data of multiple pet sounds collected in advance and generates messages corresponding to the characteristics of the pet's sounds. For example, the generation unit collects data of sounds such as dogs, cats, and birds and learns the characteristics of each sound. The generation unit generates messages corresponding to the characteristics of the pet's sounds based on the learned data. For example, the generation unit generates messages such as "I'm hungry" or "Take me for a walk." By generating messages based on the data learned in advance, the pet's intentions can be accurately expressed in words. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input learning data into the generation AI and cause the generation AI to generate messages corresponding to the characteristics of the pet's sounds.

[0033] The providing unit can display the generated message on the owner's smartphone or tablet. For example, the providing unit displays the generated message on the owner's smartphone or tablet. For example, the providing unit notifies the owner of the generated message through an app compatible with an OS such as iOS or Android. The providing unit can also play the generated message audibly. For example, the providing unit plays the generated message audibly through a smartphone speaker. The providing unit can also send the generated message to the owner by email. For example, the providing unit can send the generated message to the owner's email address, allowing the owner to receive the message by email. This makes it easier for the owner to understand their pet's feelings by displaying the generated message on the owner's device. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the generated message into AI and have the AI ​​select the optimal display method.

[0034] The collection unit can estimate the pet's emotions and adjust the timing of collecting the pet's cries based on the estimated emotions. For example, if the pet is excited, the collection unit shortens the collection timing and collects the pet's cries more frequently. For example, the collection unit analyzes the pet's behavior and facial expressions to detect an excited state. Furthermore, if the pet is relaxed, the collection unit can lengthen the collection timing and collect the pet's cries at intervals. For example, the collection unit monitors the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is feeling anxious, the collection unit can adjust the collection timing and collect changes in the pet's cries in detail. For example, the collection unit analyzes the pet's behavioral patterns and changes in its voice to detect an anxious state. By adjusting the collection timing according to the pet's emotions, the pet's cries can be collected at more appropriate times. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotional data into AI and have the AI ​​adjust the collection timing.

[0035] The collection unit can analyze past pet bark data and select a collection method. For example, if past pet bark data indicates that there are many barks during a specific time period, the collection unit can concentrate collection on that time period. For example, the collection unit can analyze past pet bark data and compile the frequency of barks by time period. Furthermore, if past data indicates that there are many barks under specific environmental conditions, the collection unit can strengthen collection under those conditions. For example, the collection unit can combine and analyze past pet bark data and environmental data to identify the conditions under which the barks occur. Furthermore, the collection unit can analyze patterns of pet barks based on the past data and select an optimal collection method. For example, the collection unit can cluster past pet bark data and classify the bark patterns. By selecting an optimal collection method based on the past data, it is possible to efficiently collect pet barks. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past pet bark data into AI and have the AI ​​select an optimal collection method.

[0036] When collecting the pet's barks, the collection unit can filter them based on the pet's current activity status and environment. For example, when the pet is playing, the collection unit filters out the sounds of the play and collects only the barks. For example, the collection unit detects the pet's movements with a sensor and removes the sounds of the play. Furthermore, when the pet is eating, the collection unit can filter out the sounds of the pet's dishes and collect only the barks. For example, the collection unit identifies and filters out the sounds of the pet eating. Furthermore, when the pet is sleeping, the collection unit can filter out environmental sounds and collect only the barks. For example, the collection unit analyzes environmental sounds and distinguishes them from barks. By filtering based on the activity status and environment, noise can be removed and accurate barks can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the pet's activity status and environmental data into AI and have the AI ​​perform the filtering process.

[0037] When collecting meows, the collection unit can select a collection method according to the type and individual differences of the pet. For example, in the case of a dog, the collection unit collects meows using a high-precision microphone dedicated to dogs. For example, the collection unit uses a microphone specialized for dog meows. In the case of a cat, the collection unit can also collect meows using a high-precision microphone dedicated to cat meows. For example, the collection unit uses a microphone specialized for cat meows. Furthermore, the collection unit can customize the collection method according to specific individual differences and perform optimal collection. For example, the collection unit adjusts the collection method according to the age and gender of the pet. In this way, by selecting a collection method according to the type and individual differences of the pet, more accurate meows can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the type and individual differences of the pet into AI and have the AI ​​select the optimal collection method.

[0038] The collection unit can estimate the pet's emotions and determine the priority of the cries to be collected based on the estimated pet emotions. For example, if the pet is excited, the collection unit prioritizes collecting cries made when the pet is excited. For example, the collection unit analyzes the pet's behavior and facial expressions to detect the excited state. Furthermore, if the pet is relaxed, the collection unit can prioritize collecting cries made when the pet is relaxed. For example, the collection unit monitors the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, if the pet is feeling anxious, the collection unit can prioritize collecting cries made when the pet is anxious. For example, the collection unit analyzes the pet's behavioral patterns and vocal changes to detect the anxious state. In this way, by determining the priority based on the pet's emotions, important cries can be preferentially collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotional data into AI and have the AI ​​determine the priority of the cries to be collected.

[0039] When collecting pet cries, the collection unit can prioritize collecting highly relevant pet cries based on the geographical location information of the pet. For example, if the pet is in a specific location, the collection unit prioritizes collecting pet cries from that location. For example, the collection unit acquires the pet's location information using GPS and collects pet cries from that location. Furthermore, if the pet is moving, the collection unit can prioritize collecting pet cries from the destination. For example, the collection unit tracks the pet's movement route and collects pet cries from the destination. Furthermore, if the pet is in a specific area, the collection unit can prioritize collecting pet cries from that area. For example, the collection unit collects pet cries from a specific area based on the pet's location information. This allows for efficient collection of highly relevant pet cries by taking the geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's geographical location information to AI and cause the AI ​​to collect highly relevant pet cries.

[0040] The collection unit can analyze the pet's health condition when collecting the pet's cries and detect abnormal cries. For example, if the pet is healthy, the collection unit collects normal cries. For example, the collection unit monitors the pet's health condition and collects normal cries. The collection unit can also detect and collect abnormal cries if the pet is not feeling well. For example, the collection unit measures the pet's body temperature and heart rate and detects abnormal cries. Furthermore, if the pet is feeling stressed, the collection unit can collect cries made during stress. For example, the collection unit analyzes the pet's behavioral patterns and changes in voice to detect a stressed state. This allows for early detection of abnormal cries by analyzing the pet's health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's health data into AI and have the AI ​​detect abnormal cries.

[0041] When collecting barks, the collection unit can customize the collection method based on the pet's past feedback. For example, the collection unit prioritizes collection of specific barks from past feedback. For example, the collection unit prioritizes collection of specific barks based on the owner's ratings and comments. The collection unit can also adjust the collection method based on past feedback to perform optimal collection. For example, the collection unit analyzes past feedback and adjusts the collection timing and means. The collection unit can also adjust the collection timing by referring to past feedback. For example, the collection unit optimizes the collection timing based on past feedback. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into AI and have the AI ​​customize the collection method.

[0042] The analysis unit can estimate the pet's emotions and adjust the method of expression of the analysis based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit emphasizes the pet's excited cries during analysis. For example, the analysis unit analyzes the pet's behavior and facial expressions to detect the excited state. Furthermore, if the pet is relaxed, the analysis unit can analyze the pet's relaxed cries in detail. For example, the analysis unit monitors the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, if the pet is feeling anxious, the analysis unit can focus on analyzing the pet's anxious cries. For example, the analysis unit analyzes the pet's behavioral patterns and vocal changes to detect the anxious state. This allows for more appropriate analysis results to be obtained by adjusting the method of expression of the analysis according to the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the pet's emotional data into AI and have the AI ​​adjust the method of expression of the analysis.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's bark. For example, the analysis unit performs a detailed analysis in the case of an important bark. For example, the analysis unit analyzes the pet's behavior and facial expressions to identify important barks. The analysis unit can also perform a simplified analysis in the case of a general bark. For example, the analysis unit analyzes the frequency and pattern of the pet's barks to identify general barks. Furthermore, in the case of a bark made under a specific situation, the analysis unit can perform an analysis tailored to that situation. For example, the analysis unit analyzes the pet's environmental data to identify the bark made under the specific situation. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the bark. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input bark importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0044] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the type of sound. For example, in the case of a dog's bark, the analysis unit applies an analysis algorithm specifically for dogs. For example, the analysis unit uses a voice recognition algorithm specialized for dog barks. Furthermore, in the case of a cat's meow, the analysis unit can also apply an analysis algorithm specifically for cats. For example, the analysis unit uses a voice recognition algorithm specialized for cat meows. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the type of specific sound. For example, the analysis unit clusters pet sound data and selects an optimal analysis algorithm. This improves analysis accuracy by applying an analysis algorithm depending on the type of sound. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the type of sound meow into AI and have the AI ​​apply an appropriate analysis algorithm.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis based on past analysis results of the pet. For example, the analysis unit adjusts the analysis algorithm based on past analysis results to improve accuracy. For example, the analysis unit analyzes past analysis logs and optimizes algorithm parameters. The analysis unit can also improve the analysis accuracy of specific cries by referring to past analysis results. For example, the analysis unit learns the characteristics of specific cries based on past analysis results and improves analysis accuracy. Furthermore, the analysis unit can improve the efficiency of analysis by utilizing past analysis results. For example, the analysis unit determines analysis priorities based on past analysis results and performs analysis efficiently. This improves the accuracy of analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0046] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit can perform a short analysis. For example, the analysis unit can analyze the pet's behavior and facial expressions to detect an excited state. Furthermore, if the pet is relaxed, the analysis unit can perform a long, detailed analysis. For example, the analysis unit can monitor the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is feeling anxious, the analysis unit can perform a quick analysis. For example, the analysis unit can analyze the pet's behavioral patterns and vocal changes to detect an anxious state. This allows for efficient analysis by adjusting the length of the analysis according to the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the pet's emotional data into AI and have the AI ​​adjust the length of the analysis.

[0047] During analysis, the analysis unit can determine the analysis priority based on the time when the cries occurred. The analysis unit, for example, prioritizes analyzing recent cries. For example, the analysis unit analyzes the timestamps of the cries and prioritizes analyzing recent cries. The analysis unit can also prioritize analyzing cries that occurred during a specific time period. For example, the analysis unit analyzes the time when the cries occurred and prioritizes analyzing cries that occurred during a specific time period. Furthermore, the analysis unit can determine the analysis priority by referring to past cries. For example, the analysis unit analyzes past cries data and determines the analysis priority. This enables efficient analysis by determining the priority based on the time when the cries occurred. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the cries occurred into AI and have the AI ​​determine the analysis priority.

[0048] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the calls. The analysis unit, for example, prioritizes analysis of highly related calls. For example, the analysis unit analyzes the co-occurrence frequency and correlation of calls and prioritizes analysis of highly related calls. The analysis unit can also postpone analysis of less related calls. For example, the analysis unit evaluates the relevance of calls and postpones analysis of less related calls. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of calls. For example, the analysis unit analyzes the relevance of calls and optimizes the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of calls. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input relevance data of calls into AI and have the AI ​​adjust the order of analysis.

[0049] During the analysis, the analysis unit can adjust the level of detail of the analysis according to the pet's health condition. For example, if the pet is healthy, the analysis unit performs the analysis at a normal level of detail. For example, the analysis unit monitors the pet's health data and analyzes normal cries. The analysis unit can also perform a more detailed analysis if the pet is in poor health. For example, the analysis unit measures the pet's body temperature and heart rate and analyzes abnormal cries in detail. Furthermore, if the pet is feeling stressed, the analysis unit can analyze the cries made during stress in detail. For example, the analysis unit analyzes the pet's behavioral patterns and vocal changes to analyze the stress state in detail. By adjusting the level of detail of the analysis according to the pet's health condition, abnormal cries can be detected early. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the pet's health data into AI and have the AI ​​adjust the level of detail of the analysis.

[0050] The generation unit can estimate the pet's emotions and adjust the expression method of the generated message based on the estimated pet's emotions. For example, if the pet is excited, the generation unit generates a message expressing excitement. For example, the generation unit analyzes the pet's behavior and facial expressions to detect the excited state. The generation unit can also generate a message expressing relaxation if the pet is relaxed. For example, the generation unit monitors the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, the generation unit can generate a message expressing anxiety if the pet is feeling anxious. For example, the generation unit analyzes the pet's behavior patterns and changes in voice to detect the anxious state. This allows for the generation of more appropriate messages by adjusting the expression method of the message based on the pet's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the expression method of the message.

[0051] The generation unit can adjust the level of detail of the generated message based on the importance of the pet's bark during generation. For example, the generation unit generates a detailed message when the pet's bark is important. For example, the generation unit analyzes the pet's behavior and facial expressions to identify important barks. The generation unit can also generate a simplified message when the pet's bark is common. For example, the generation unit analyzes the frequency and pattern of the pet's barks to identify common barks. Furthermore, the generation unit can generate a message appropriate for a bark made under a specific situation. For example, the generation unit analyzes the pet's environmental data to identify the bark made under a specific situation. This enables efficient message generation by adjusting the level of detail of the message based on the importance of the bark. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input bark importance data to the generation AI and cause the generation AI to adjust the level of detail of the message.

[0052] The generation unit can apply an appropriate generation algorithm depending on the type of meow during generation. For example, in the case of a dog's bark, the generation unit applies a generation algorithm specifically for dogs. For example, the generation unit uses a natural language generation algorithm specialized for dog barks. Furthermore, in the case of a cat's meow, the generation unit can also apply a generation algorithm specifically for cats. For example, the generation unit uses a natural language generation algorithm specialized for cat meows. Furthermore, the generation unit can select an optimal generation algorithm depending on the type of specific meow. For example, the generation unit clusters pet meow data and selects an optimal generation algorithm. This improves generation accuracy by applying a generation algorithm depending on the type of meow. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input meow type data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0053] During generation, the generation unit can improve the accuracy of generation based on past generation results of the pet. For example, the generation unit adjusts the generation algorithm based on past generation results to improve accuracy. For example, the generation unit analyzes past generation logs and optimizes algorithm parameters. The generation unit can also improve the accuracy of generating a specific cry by referring to past generation results. For example, the generation unit learns the characteristics of a specific cry based on past generation results and improves generation accuracy. Furthermore, the generation unit can improve the efficiency of generation by utilizing past generation results. For example, the generation unit determines generation priorities based on past generation results and performs generation efficiently. This improves the accuracy of generation by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0054] The generation unit can estimate the pet's emotions and adjust the length of the generated message based on the estimated pet's emotions. For example, if the pet is excited, the generation unit generates a short, to-the-point message. For example, the generation unit analyzes the pet's behavior and facial expressions to detect an excited state. Furthermore, if the pet is relaxed, the generation unit can generate a longer message with detailed explanations. For example, the generation unit monitors the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is anxious, the generation unit can generate a message to provide reassurance. For example, the generation unit analyzes the pet's behavioral patterns and vocal changes to detect an anxious state. This allows for the generation of a more appropriate message by adjusting the length of the message based on the pet's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the pet's emotional data into the generation AI and have the generation AI adjust the length of the message.

[0055] The generation unit can determine the priority of messages to be generated based on the time of occurrence of the bird's cry during generation. The generation unit, for example, prioritizes generating messages based on the most recent bird's cry. For example, the generation unit analyzes the timestamp of the bird's cry and prioritizes reflecting the most recent bird's cry in the message. The generation unit can also prioritize generating messages based on bird's cry that occurred during a specific time period. For example, the generation unit analyzes the time of occurrence of the bird's cry and prioritizes reflecting the bird's cry that occurred during a specific time period in the message. Furthermore, the generation unit can determine the priority of messages by referring to past bird's cry. For example, the generation unit analyzes past bird's cry data and determines the priority of messages. This enables efficient message generation by determining the priority based on the time of occurrence of the bird's cry. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time of occurrence of the bird's cry into the generation AI and have the generation AI determine the priority of messages.

[0056] The generation unit can adjust the order of messages to be generated based on the relevance of the calls during generation. The generation unit, for example, prioritizes generating messages based on highly related calls. For example, the generation unit analyzes the co-occurrence frequency and correlation of calls and prioritizes reflecting highly related calls in the message. The generation unit can also generate messages later based on less related calls. For example, the generation unit evaluates the relevance of calls and reflects less related calls in the message later. The generation unit can also adjust the order of messages based on the relevance of calls. For example, the generation unit analyzes the relevance of calls and optimizes the order of messages. This enables efficient message generation by adjusting the order of messages based on the relevance of calls. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input call relevance data into the generation AI and cause the generation AI to adjust the order of messages.

[0057] The generation unit can adjust the level of detail of the generated message depending on the pet's health condition at the time of generation. For example, if the pet is healthy, the generation unit generates a message with a normal level of detail. For example, the generation unit monitors the pet's health data and generates a message based on normal vocalizations. The generation unit can also generate a detailed message if the pet is in poor health. For example, the generation unit measures the pet's body temperature and heart rate and generates a detailed message based on abnormal vocalizations. Furthermore, if the pet is feeling stressed, the generation unit can generate a message based on the vocalizations made during stress. For example, the generation unit analyzes the pet's behavioral patterns and vocalization changes and generates a message based on the pet's stress state. This allows for early detection of abnormal vocalizations by adjusting the level of detail of the message depending on the pet's health condition. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the pet's health data into the generation AI and have the generation AI adjust the level of detail of the message.

[0058] The providing unit can estimate the pet's emotions and adjust the display method of the message to be provided based on the estimated pet's emotions. For example, if the pet is excited, the providing unit can provide a visually emphasized display method. For example, the providing unit can analyze the pet's behavior and facial expressions to detect an excited state. The providing unit can also provide a calm display method if the pet is relaxed. For example, the providing unit can monitor the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is feeling anxious, the providing unit can provide a display method that gives a sense of security. For example, the providing unit can analyze the pet's behavioral patterns and changes in voice to detect an anxious state. This makes it easier for the owner to understand their pet's feelings by adjusting the display method based on the pet's emotions. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the pet's emotional data into AI and have the AI ​​adjust the display method.

[0059] The providing unit can select the optimal display method based on the owner's past operation history when providing the display data. For example, the providing unit preferentially provides a display method that the owner has previously preferred. For example, the providing unit analyzes the owner's past operation history and identifies the preferred display method. The providing unit can also suggest the optimal display method based on the owner's past operation history. For example, the providing unit analyzes the owner's operation log and suggests the optimal display method. Furthermore, the providing unit can customize the display method by referring to the owner's past operation history. For example, the providing unit customizes the display method based on the owner's operation history. In this way, the optimal display method can be provided by referring to the owner's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's operation history data into AI and cause the AI ​​to select the optimal display method.

[0060] The providing unit can customize the display content according to the owner's current work at the time of providing the display content. For example, if the owner is busy, the providing unit provides concise display content. For example, the providing unit analyzes the owner's current work status and provides concise display content. The providing unit can also provide detailed display content if the owner is relaxed. For example, the providing unit analyzes the owner's work status and provides detailed display content. Furthermore, if the owner is performing a specific task, the providing unit can also provide display content related to that task. For example, the providing unit analyzes the owner's work status and provides related display content. This makes it easier for the owner to understand their pet's feelings by customizing the display content according to the owner's current work. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's work status data into AI and have the AI ​​customize the display content.

[0061] The providing unit can improve the providing method based on the owner's feedback when providing the information. The providing unit, for example, improves the display method based on the owner's feedback. For example, the providing unit analyzes the owner's ratings and comments and improves the display method. The providing unit can also customize the provided content by referring to the owner's feedback. For example, the providing unit adjusts the provided content based on the owner's feedback. Furthermore, the providing unit can improve the efficiency of the providing method by utilizing the owner's feedback. For example, the providing unit analyzes the owner's feedback and improves the efficiency of the providing method. In this way, the providing method can be optimized by reflecting the owner's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's feedback data into AI and have the AI ​​improve the providing method.

[0062] The providing unit can estimate the pet's emotions and determine the priority of messages to provide based on the estimated pet's emotions. For example, if the pet is excited, the providing unit can prioritize providing messages expressing excitement. For example, the providing unit can analyze the pet's behavior and facial expressions to detect the excited state. Furthermore, if the pet is relaxed, the providing unit can prioritize providing messages expressing relaxation. For example, the providing unit can monitor the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, if the pet is feeling anxious, the providing unit can prioritize providing messages expressing anxiety. For example, the providing unit can analyze the pet's behavioral patterns and changes in voice to detect the anxious state. This allows important messages to be prioritized by determining the priority based on the pet's emotions. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the pet's emotional data into AI and have the AI ​​determine the priority of messages.

[0063] The providing unit can select the optimal display method based on the owner's device information at the time of providing the display information. For example, if the owner is using a smartphone, the providing unit provides a display method that matches the screen size. For example, the providing unit analyzes the smartphone's screen size and provides the optimal display method. Furthermore, if the owner is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit analyzes the tablet's screen size and provides the optimal display method. Furthermore, if the owner is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit analyzes the smartwatch's screen size and provides the optimal display method. This makes it possible to provide the optimal display method by taking the owner's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's device information into AI and have the AI ​​select the optimal display method.

[0064] The providing unit can provide the display content in multiple languages ​​according to the owner's language setting when providing the content. The providing unit, for example, automatically sets the display content based on the language setting of the owner's device. For example, the providing unit analyzes the device's language setting and automatically sets the display content. The providing unit can also provide a language switching function if the owner uses multiple languages. For example, the providing unit analyzes the language used by the owner and provides the language switching function. Furthermore, if the owner selects a specific language, the providing unit can provide the display content in that language. For example, the providing unit analyzes the language selected by the owner and provides the display content in that language. This makes it easier for the owner to understand their pet's feelings by making the display content multilingual according to the owner's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the owner's language setting data into AI and cause the AI ​​to provide the multilingual display content.

[0065] The providing unit can customize the provision method based on the owner's past feedback when providing the information. The providing unit, for example, customizes the display method based on the owner's past feedback. For example, the providing unit analyzes the owner's ratings and comments and customizes the display method. The providing unit can also adjust the provision content by referring to the owner's past feedback. For example, the providing unit adjusts the provision content based on the owner's feedback. Furthermore, the providing unit can improve the efficiency of the provision method by utilizing the owner's past feedback. For example, the providing unit analyzes the owner's feedback and improves the efficiency of the provision method. In this way, the provision method can be optimized by reflecting the owner's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's feedback data into AI and have the AI ​​customize the provision method.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The analysis unit can learn the patterns of pet cries in real time and improve the accuracy of the analysis. For example, the analysis unit collects real-time data on pet cries and updates the analysis model using a sequential learning algorithm. The analysis unit can also cluster the patterns of pet cries and detect abnormal cries. Furthermore, the analysis unit can analyze the patterns of pet cries and predict the signs of when a particular cries will occur. This allows the analysis unit to learn the patterns of pet cries in real time and improve the accuracy of the analysis.

[0068] When collecting pet cries, the collection unit can analyze environmental sounds and distinguish between pet cries and environmental sounds. For example, the collection unit can analyze the characteristics of the environmental sounds and separate the pet cries from the environmental sounds. The collection unit can also learn environmental sound patterns and improve the accuracy of collecting pet cries. Furthermore, the collection unit can detect changes in the environmental sounds and adjust the timing of collecting pet cries. In this way, the collection unit can analyze environmental sounds and distinguish between pet cries and environmental sounds, thereby improving the accuracy of collecting pet cries.

[0069] The providing unit can link the generated message to the owner's smart home devices. For example, the providing unit can send the generated message to a smart speaker and play it aloud. The providing unit can also display the generated message on a smart display. Furthermore, the providing unit can express the generated message by the color or brightness of a smart light. In this way, by linking the generated message to the smart home devices, the providing unit can make it easier for the owner to understand their pet's feelings.

[0070] The analysis unit can take the pet's health condition into consideration when analyzing the characteristics of the pet's cries. For example, the analysis unit can monitor the pet's body temperature and heart rate and reflect the health condition in the analysis. The analysis unit can also classify the characteristics of the pet's cries based on the pet's health condition. Furthermore, the analysis unit can detect changes in the pet's health condition and detect abnormal cries early on. In this way, the analysis unit can improve the accuracy of the analysis of the pet's cries by taking the pet's health condition into consideration.

[0071] The providing unit can link the generated message with a calendar app of the pet owner. For example, the providing unit can register the generated message in the calendar app and notify the pet owner as a reminder. The providing unit can also add the generated message to an appointment in the calendar app. Furthermore, the providing unit can also display the generated message as an event in the calendar app. In this way, the providing unit can link the generated message with the calendar app, making it easier for the pet owner to manage their pet's appointments.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The collection unit collects pet sounds. Pet sounds include, but are not limited to, sounds from dogs and cats. The collection unit collects pet sounds using, for example, a high-precision microphone. The collection unit can also collect pet sounds in real time. Furthermore, the collection unit can record pet sounds and save them for later analysis. For example, the collection unit collects pet sounds using a high-sensitivity microphone while filtering out ambient noise. Step 2: The analysis unit analyzes the calls collected by the collection unit and extracts features such as vocal pitch and vocal extension. The analysis unit extracts the features of the collected calls using audio signal processing techniques such as Fourier transform and Mel frequency cepstral coefficients (MFCC). The analysis unit can also use AI to analyze the features of the calls with high accuracy. For example, the analysis unit uses Fourier transform to analyze the frequency components of the calls. The analysis unit can also use Mel frequency cepstral coefficients to extract the acoustic features of the calls. Furthermore, the analysis unit can use AI to learn the features of the calls and improve the accuracy of the analysis. Step 3: The generation unit verbalizes the pet's intentions based on the features extracted by the analysis unit. The generation unit uses, for example, a generation AI to generate messages that correspond to the features of the pet's cries. The generation AI is trained based on a large amount of pet cries data collected in advance. This data is labeled by experts and is highly reliable. The generation unit uses this data to generate messages that correspond to the features of the pet's cries. For example, the generation unit generates messages such as "I'm hungry" or "Take me for a walk." Step 4: The providing unit provides the message generated by the generating unit to the owner. For example, the providing unit displays the generated message on the owner's smartphone or tablet. The providing unit can also play the generated message by voice. Furthermore, the providing unit has a function to notify the owner of the generated message. For example, the providing unit notifies the owner of the generated message through a smartphone app.

[0074] (Example 2) A pet voice analysis system according to an embodiment of the present invention collects pet voices and uses a generation AI to analyze and verbalize them. The pet voice analysis system collects and analyzes pet voices, and the generation AI verbalizes the pet's intentions to provide a message to the owner. For example, the pet voice analysis system may be configured to install a high-precision microphone to collect pet voices. Next, an analysis unit operates to analyze the collected pet voices and extract features such as the pitch of the voice and how the voice is extended. Based on the analyzed features, the generation AI verbalizes the pet's intentions. For example, a message such as "I'm hungry" or "Take me for a walk" is generated. Finally, a provision unit operates to provide the generated message to the owner. This allows the owner to more easily understand the pet's feelings and improves communication with the pet. The pet voice analysis system analyzes the pet's voices and verbalizes them using the generation AI, allowing the owner to better understand the pet's feelings. For example, if the pet says "I'm hungry," the owner can immediately provide food. Also, if a pet cries "take me for a walk," the owner can take the pet for a walk, thus improving communication with the pet and improving the quality of life of the pet.

[0075] A pet voice analysis system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects pet voices. Pet voices include, but are not limited to, the meows of dogs and cats. The collection unit collects pet voices using, for example, a high-precision microphone. The collection unit can also collect pet voices in real time. The collection unit can also record pet voices and save them for later analysis. For example, the collection unit collects pet voices using a high-sensitivity microphone while filtering out ambient noise. The analysis unit analyzes the pet voices collected by the collection unit and extracts features such as voice pitch and voice extension. The analysis unit extracts features of the collected voices using audio signal processing techniques such as Fourier transform and Mel Frequency Cepstrum Coefficients (MFCC). The analysis unit can also use AI to analyze the features of the voices with high precision. For example, the analysis unit analyzes the frequency components of the pet's bark using a Fourier transform. The analysis unit can also extract acoustic features of the bark using Mel frequency cepstrum coefficients. Furthermore, the analysis unit can use AI to learn the features of the bark and improve the analysis accuracy. The generation unit verbalizes the pet's intentions based on the features extracted by the analysis unit. The generation unit, for example, uses a generation AI to generate messages corresponding to the features of the pet's bark. The generation AI learns from a large amount of pet bark data collected in advance. This data is labeled by experts and is highly reliable. The generation unit generates messages corresponding to the features of the pet's bark based on this data. For example, the generation unit generates messages such as "I'm hungry" or "Take me for a walk." The provision unit provides the messages generated by the generation unit to the owner. For example, the provision unit displays the generated messages on the owner's smartphone or tablet. The provision unit can also play the generated messages aloud. Furthermore, the provision unit has a function to notify the owner of the generated messages. For example, the providing unit notifies the owner of the generated message via a smartphone app.As a result, the pet cry analysis system according to the embodiment analyzes the pet's cry and verbalizes it using the generation AI, making it easier for the owner to understand the pet's feelings. Some or all of the above-described processing in the generation unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the generation unit inputs the features extracted by the analysis unit into the generation AI and causes the generation AI to execute a message verbalizing the pet's intentions.

[0076] The collection unit includes a high-precision microphone for collecting pet cries with high accuracy. The collection unit includes, for example, a high-precision microphone for collecting pet cries with high accuracy. The high-precision microphone is, for example, a microphone with high sensitivity and a wide frequency response. The high-precision microphone may also have a noise-canceling function. For example, the collection unit uses a microphone with high sensitivity and a wide frequency response to accurately collect pet cries. The collection unit can also use a microphone with a noise-canceling function to collect pet cries while removing ambient noise. Furthermore, the collection unit includes a high-precision microphone for collecting pet cries in real time. In this way, the use of the high-precision microphone allows for accurate collection of pet cries. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the cries data collected by the high-precision microphone into AI and have the AI ​​perform noise removal and sound quality improvement processing.

[0077] The analysis unit can extract features of the collected calls using audio signal processing techniques such as Fourier transform or Mel Frequency Cepstrum Coefficients (MFCC). The analysis unit analyzes the frequency components of the collected calls using, for example, Fourier transform. For example, the analysis unit analyzes the frequency components of the calls using a Discrete Fourier Transform (DFT). The analysis unit can also quickly analyze the frequency components of the calls using a Fast Fourier Transform (FFT). Furthermore, the analysis unit can extract acoustic features of the collected calls using Mel Frequency Cepstrum Coefficients (MFCC). For example, the analysis unit sets a filter bank and adjusts the number of cepstrum dimensions to extract acoustic features of the calls with high accuracy. This enables the use of audio signal processing techniques to extract features of the calls with high accuracy. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input collected bird call data into AI and have the AI ​​perform Fourier transforms and MFCC calculations.

[0078] The generation unit can learn from data of multiple pet sounds collected in advance and generate messages corresponding to the characteristics of the pet's sounds. The generation unit, for example, learns from data of multiple pet sounds collected in advance and generates messages corresponding to the characteristics of the pet's sounds. For example, the generation unit collects data of sounds such as dogs, cats, and birds and learns the characteristics of each sound. The generation unit generates messages corresponding to the characteristics of the pet's sounds based on the learned data. For example, the generation unit generates messages such as "I'm hungry" or "Take me for a walk." By generating messages based on the data learned in advance, the pet's intentions can be accurately expressed in words. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input learning data into the generation AI and cause the generation AI to generate messages corresponding to the characteristics of the pet's sounds.

[0079] The providing unit can display the generated message on the owner's smartphone or tablet. For example, the providing unit displays the generated message on the owner's smartphone or tablet. For example, the providing unit notifies the owner of the generated message through an app compatible with an OS such as iOS or Android. The providing unit can also play the generated message audibly. For example, the providing unit plays the generated message audibly through a smartphone speaker. The providing unit can also send the generated message to the owner by email. For example, the providing unit can send the generated message to the owner's email address, allowing the owner to receive the message by email. This makes it easier for the owner to understand their pet's feelings by displaying the generated message on the owner's device. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the generated message into AI and have the AI ​​select the optimal display method.

[0080] The collection unit can estimate the pet's emotions and adjust the timing of collecting the pet's cries based on the estimated emotions. For example, if the pet is excited, the collection unit shortens the collection timing and collects the pet's cries more frequently. For example, the collection unit analyzes the pet's behavior and facial expressions to detect an excited state. Furthermore, if the pet is relaxed, the collection unit can lengthen the collection timing and collect the pet's cries at intervals. For example, the collection unit monitors the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is feeling anxious, the collection unit can adjust the collection timing and collect changes in the pet's cries in detail. For example, the collection unit analyzes the pet's behavioral patterns and changes in its voice to detect an anxious state. By adjusting the collection timing according to the pet's emotions, the pet's cries can be collected at more appropriate times. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotional data into AI and have the AI ​​adjust the collection timing.

[0081] The collection unit can analyze past pet bark data and select a collection method. For example, if past pet bark data indicates that there are many barks during a specific time period, the collection unit can concentrate collection on that time period. For example, the collection unit can analyze past pet bark data and compile the frequency of barks by time period. Furthermore, if past data indicates that there are many barks under specific environmental conditions, the collection unit can strengthen collection under those conditions. For example, the collection unit can combine and analyze past pet bark data and environmental data to identify the conditions under which the barks occur. Furthermore, the collection unit can analyze patterns of pet barks based on the past data and select an optimal collection method. For example, the collection unit can cluster past pet bark data and classify the bark patterns. By selecting an optimal collection method based on the past data, it is possible to efficiently collect pet barks. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past pet bark data into AI and have the AI ​​select an optimal collection method.

[0082] When collecting the pet's barks, the collection unit can filter them based on the pet's current activity status and environment. For example, when the pet is playing, the collection unit filters out the sounds of the play and collects only the barks. For example, the collection unit detects the pet's movements with a sensor and removes the sounds of the play. Furthermore, when the pet is eating, the collection unit can filter out the sounds of the pet's dishes and collect only the barks. For example, the collection unit identifies and filters out the sounds of the pet eating. Furthermore, when the pet is sleeping, the collection unit can filter out environmental sounds and collect only the barks. For example, the collection unit analyzes environmental sounds and distinguishes them from barks. By filtering based on the activity status and environment, noise can be removed and accurate barks can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the pet's activity status and environmental data into AI and have the AI ​​perform the filtering process.

[0083] When collecting meows, the collection unit can select a collection method according to the type and individual differences of the pet. For example, in the case of a dog, the collection unit collects meows using a high-precision microphone dedicated to dogs. For example, the collection unit uses a microphone specialized for dog meows. In the case of a cat, the collection unit can also collect meows using a high-precision microphone dedicated to cat meows. For example, the collection unit uses a microphone specialized for cat meows. Furthermore, the collection unit can customize the collection method according to specific individual differences and perform optimal collection. For example, the collection unit adjusts the collection method according to the age and gender of the pet. In this way, by selecting a collection method according to the type and individual differences of the pet, more accurate meows can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the type and individual differences of the pet into AI and have the AI ​​select the optimal collection method.

[0084] The collection unit can estimate the pet's emotions and determine the priority of the cries to be collected based on the estimated pet emotions. For example, if the pet is excited, the collection unit prioritizes collecting cries made when the pet is excited. For example, the collection unit analyzes the pet's behavior and facial expressions to detect the excited state. Furthermore, if the pet is relaxed, the collection unit can prioritize collecting cries made when the pet is relaxed. For example, the collection unit monitors the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, if the pet is feeling anxious, the collection unit can prioritize collecting cries made when the pet is anxious. For example, the collection unit analyzes the pet's behavioral patterns and vocal changes to detect the anxious state. In this way, by determining the priority based on the pet's emotions, important cries can be preferentially collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's emotional data into AI and have the AI ​​determine the priority of the cries to be collected.

[0085] When collecting pet cries, the collection unit can prioritize collecting highly relevant pet cries based on the geographical location information of the pet. For example, if the pet is in a specific location, the collection unit prioritizes collecting pet cries from that location. For example, the collection unit acquires the pet's location information using GPS and collects pet cries from that location. Furthermore, if the pet is moving, the collection unit can prioritize collecting pet cries from the destination. For example, the collection unit tracks the pet's movement route and collects pet cries from the destination. Furthermore, if the pet is in a specific area, the collection unit can prioritize collecting pet cries from that area. For example, the collection unit collects pet cries from a specific area based on the pet's location information. This allows for efficient collection of highly relevant pet cries by taking the geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the pet's geographical location information to AI and cause the AI ​​to collect highly relevant pet cries.

[0086] The collection unit can analyze the pet's health condition when collecting the pet's cries and detect abnormal cries. For example, if the pet is healthy, the collection unit collects normal cries. For example, the collection unit monitors the pet's health condition and collects normal cries. The collection unit can also detect and collect abnormal cries if the pet is not feeling well. For example, the collection unit measures the pet's body temperature and heart rate and detects abnormal cries. Furthermore, if the pet is feeling stressed, the collection unit can collect cries made during stress. For example, the collection unit analyzes the pet's behavioral patterns and changes in voice to detect a stressed state. This allows for early detection of abnormal cries by analyzing the pet's health condition. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input the pet's health data into AI and have the AI ​​detect abnormal cries.

[0087] When collecting barks, the collection unit can customize the collection method based on the pet's past feedback. For example, the collection unit prioritizes collection of specific barks from past feedback. For example, the collection unit prioritizes collection of specific barks based on the owner's ratings and comments. The collection unit can also adjust the collection method based on past feedback to perform optimal collection. For example, the collection unit analyzes past feedback and adjusts the collection timing and means. The collection unit can also adjust the collection timing by referring to past feedback. For example, the collection unit optimizes the collection timing based on past feedback. In this way, the collection method can be optimized by reflecting past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past feedback data into AI and have the AI ​​customize the collection method.

[0088] The analysis unit can estimate the pet's emotions and adjust the method of expression of the analysis based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit emphasizes the pet's excited cries during analysis. For example, the analysis unit analyzes the pet's behavior and facial expressions to detect the excited state. Furthermore, if the pet is relaxed, the analysis unit can analyze the pet's relaxed cries in detail. For example, the analysis unit monitors the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, if the pet is feeling anxious, the analysis unit can focus on analyzing the pet's anxious cries. For example, the analysis unit analyzes the pet's behavioral patterns and vocal changes to detect the anxious state. This allows for more appropriate analysis results to be obtained by adjusting the method of expression of the analysis according to the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the pet's emotional data into AI and have the AI ​​adjust the method of expression of the analysis.

[0089] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the pet's bark. For example, the analysis unit performs a detailed analysis in the case of an important bark. For example, the analysis unit analyzes the pet's behavior and facial expressions to identify important barks. The analysis unit can also perform a simplified analysis in the case of a general bark. For example, the analysis unit analyzes the frequency and pattern of the pet's barks to identify general barks. Furthermore, in the case of a bark made under a specific situation, the analysis unit can perform an analysis tailored to that situation. For example, the analysis unit analyzes the pet's environmental data to identify the bark made under the specific situation. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the bark. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input bark importance data into AI and have the AI ​​adjust the level of detail of the analysis.

[0090] During analysis, the analysis unit can apply an appropriate analysis algorithm depending on the type of sound. For example, in the case of a dog's bark, the analysis unit applies an analysis algorithm specifically for dogs. For example, the analysis unit uses a voice recognition algorithm specialized for dog barks. Furthermore, in the case of a cat's meow, the analysis unit can also apply an analysis algorithm specifically for cats. For example, the analysis unit uses a voice recognition algorithm specialized for cat meows. Furthermore, the analysis unit can select an optimal analysis algorithm depending on the type of specific sound. For example, the analysis unit clusters pet sound data and selects an optimal analysis algorithm. This improves analysis accuracy by applying an analysis algorithm depending on the type of sound. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the type of sound meow into AI and have the AI ​​apply an appropriate analysis algorithm.

[0091] During analysis, the analysis unit can improve the accuracy of the analysis based on past analysis results of the pet. For example, the analysis unit adjusts the analysis algorithm based on past analysis results to improve accuracy. For example, the analysis unit analyzes past analysis logs and optimizes algorithm parameters. The analysis unit can also improve the analysis accuracy of specific cries by referring to past analysis results. For example, the analysis unit learns the characteristics of specific cries based on past analysis results and improves analysis accuracy. Furthermore, the analysis unit can improve the efficiency of analysis by utilizing past analysis results. For example, the analysis unit determines analysis priorities based on past analysis results and performs analysis efficiently. This improves the accuracy of analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input past analysis result data into AI and have the AI ​​improve the accuracy of the analysis.

[0092] The analysis unit can estimate the pet's emotions and adjust the length of the analysis based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit can perform a short analysis. For example, the analysis unit can analyze the pet's behavior and facial expressions to detect an excited state. Furthermore, if the pet is relaxed, the analysis unit can perform a long, detailed analysis. For example, the analysis unit can monitor the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is feeling anxious, the analysis unit can perform a quick analysis. For example, the analysis unit can analyze the pet's behavioral patterns and vocal changes to detect an anxious state. This allows for efficient analysis by adjusting the length of the analysis according to the pet's emotions. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the pet's emotional data into AI and have the AI ​​adjust the length of the analysis.

[0093] During analysis, the analysis unit can determine the analysis priority based on the time when the cries occurred. The analysis unit, for example, prioritizes analyzing recent cries. For example, the analysis unit analyzes the timestamps of the cries and prioritizes analyzing recent cries. The analysis unit can also prioritize analyzing cries that occurred during a specific time period. For example, the analysis unit analyzes the time when the cries occurred and prioritizes analyzing cries that occurred during a specific time period. Furthermore, the analysis unit can determine the analysis priority by referring to past cries. For example, the analysis unit analyzes past cries data and determines the analysis priority. This enables efficient analysis by determining the priority based on the time when the cries occurred. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time when the cries occurred into AI and have the AI ​​determine the analysis priority.

[0094] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the calls. The analysis unit, for example, prioritizes analysis of highly related calls. For example, the analysis unit analyzes the co-occurrence frequency and correlation of calls and prioritizes analysis of highly related calls. The analysis unit can also postpone analysis of less related calls. For example, the analysis unit evaluates the relevance of calls and postpones analysis of less related calls. Furthermore, the analysis unit can adjust the order of analysis based on the relevance of calls. For example, the analysis unit analyzes the relevance of calls and optimizes the order of analysis. This enables efficient analysis by adjusting the order of analysis based on the relevance of calls. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input relevance data of calls into AI and have the AI ​​adjust the order of analysis.

[0095] During the analysis, the analysis unit can adjust the level of detail of the analysis according to the pet's health condition. For example, if the pet is healthy, the analysis unit performs the analysis at a normal level of detail. For example, the analysis unit monitors the pet's health data and analyzes normal cries. The analysis unit can also perform a more detailed analysis if the pet is in poor health. For example, the analysis unit measures the pet's body temperature and heart rate and analyzes abnormal cries in detail. Furthermore, if the pet is feeling stressed, the analysis unit can analyze the cries made during stress in detail. For example, the analysis unit analyzes the pet's behavioral patterns and vocal changes to analyze the stress state in detail. By adjusting the level of detail of the analysis according to the pet's health condition, abnormal cries can be detected early. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the pet's health data into AI and have the AI ​​adjust the level of detail of the analysis.

[0096] The generation unit can estimate the pet's emotions and adjust the expression method of the generated message based on the estimated pet's emotions. For example, if the pet is excited, the generation unit generates a message expressing excitement. For example, the generation unit analyzes the pet's behavior and facial expressions to detect the excited state. The generation unit can also generate a message expressing relaxation if the pet is relaxed. For example, the generation unit monitors the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, the generation unit can generate a message expressing anxiety if the pet is feeling anxious. For example, the generation unit analyzes the pet's behavior patterns and changes in voice to detect the anxious state. This allows for the generation of more appropriate messages by adjusting the expression method of the message based on the pet's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the pet's emotion data into the generation AI and cause the generation AI to adjust the expression method of the message.

[0097] The generation unit can adjust the level of detail of the generated message based on the importance of the pet's bark during generation. For example, the generation unit generates a detailed message when the pet's bark is important. For example, the generation unit analyzes the pet's behavior and facial expressions to identify important barks. The generation unit can also generate a simplified message when the pet's bark is common. For example, the generation unit analyzes the frequency and pattern of the pet's barks to identify common barks. Furthermore, the generation unit can generate a message appropriate for a bark made under a specific situation. For example, the generation unit analyzes the pet's environmental data to identify the bark made under a specific situation. This enables efficient message generation by adjusting the level of detail of the message based on the importance of the bark. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input bark importance data to the generation AI and cause the generation AI to adjust the level of detail of the message.

[0098] The generation unit can apply an appropriate generation algorithm depending on the type of meow during generation. For example, in the case of a dog's bark, the generation unit applies a generation algorithm specifically for dogs. For example, the generation unit uses a natural language generation algorithm specialized for dog barks. Furthermore, in the case of a cat's meow, the generation unit can also apply a generation algorithm specifically for cats. For example, the generation unit uses a natural language generation algorithm specialized for cat meows. Furthermore, the generation unit can select an optimal generation algorithm depending on the type of specific meow. For example, the generation unit clusters pet meow data and selects an optimal generation algorithm. This improves generation accuracy by applying a generation algorithm depending on the type of meow. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input meow type data into the generation AI and cause the generation AI to apply an appropriate generation algorithm.

[0099] During generation, the generation unit can improve the accuracy of generation based on past generation results of the pet. For example, the generation unit adjusts the generation algorithm based on past generation results to improve accuracy. For example, the generation unit analyzes past generation logs and optimizes algorithm parameters. The generation unit can also improve the accuracy of generating a specific cry by referring to past generation results. For example, the generation unit learns the characteristics of a specific cry based on past generation results and improves generation accuracy. Furthermore, the generation unit can improve the efficiency of generation by utilizing past generation results. For example, the generation unit determines generation priorities based on past generation results and performs generation efficiently. This improves the accuracy of generation by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0100] The generation unit can estimate the pet's emotions and adjust the length of the generated message based on the estimated pet's emotions. For example, if the pet is excited, the generation unit generates a short, to-the-point message. For example, the generation unit analyzes the pet's behavior and facial expressions to detect an excited state. Furthermore, if the pet is relaxed, the generation unit can generate a longer message with detailed explanations. For example, the generation unit monitors the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is anxious, the generation unit can generate a message to provide reassurance. For example, the generation unit analyzes the pet's behavioral patterns and vocal changes to detect an anxious state. This allows for the generation of a more appropriate message by adjusting the length of the message based on the pet's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the pet's emotional data into the generation AI and have the generation AI adjust the length of the message.

[0101] The generation unit can determine the priority of messages to be generated based on the time of occurrence of the bird's cry during generation. The generation unit, for example, prioritizes generating messages based on the most recent bird's cry. For example, the generation unit analyzes the timestamp of the bird's cry and prioritizes reflecting the most recent bird's cry in the message. The generation unit can also prioritize generating messages based on bird's cry that occurred during a specific time period. For example, the generation unit analyzes the time of occurrence of the bird's cry and prioritizes reflecting the bird's cry that occurred during a specific time period in the message. Furthermore, the generation unit can determine the priority of messages by referring to past bird's cry. For example, the generation unit analyzes past bird's cry data and determines the priority of messages. This enables efficient message generation by determining the priority based on the time of occurrence of the bird's cry. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the time of occurrence of the bird's cry into the generation AI and have the generation AI determine the priority of messages.

[0102] The generation unit can adjust the order of messages to be generated based on the relevance of the calls during generation. The generation unit, for example, prioritizes generating messages based on highly related calls. For example, the generation unit analyzes the co-occurrence frequency and correlation of calls and prioritizes reflecting highly related calls in the message. The generation unit can also generate messages later based on less related calls. For example, the generation unit evaluates the relevance of calls and reflects less related calls in the message later. The generation unit can also adjust the order of messages based on the relevance of calls. For example, the generation unit analyzes the relevance of calls and optimizes the order of messages. This enables efficient message generation by adjusting the order of messages based on the relevance of calls. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input call relevance data into the generation AI and cause the generation AI to adjust the order of messages.

[0103] The generation unit can adjust the level of detail of the generated message depending on the pet's health condition at the time of generation. For example, if the pet is healthy, the generation unit generates a message with a normal level of detail. For example, the generation unit monitors the pet's health data and generates a message based on normal vocalizations. The generation unit can also generate a detailed message if the pet is in poor health. For example, the generation unit measures the pet's body temperature and heart rate and generates a detailed message based on abnormal vocalizations. Furthermore, if the pet is feeling stressed, the generation unit can generate a message based on the vocalizations made during stress. For example, the generation unit analyzes the pet's behavioral patterns and vocalization changes and generates a message based on the pet's stress state. This allows for early detection of abnormal vocalizations by adjusting the level of detail of the message depending on the pet's health condition. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input the pet's health data into the generation AI and have the generation AI adjust the level of detail of the message.

[0104] The providing unit can estimate the pet's emotions and adjust the display method of the message to be provided based on the estimated pet's emotions. For example, if the pet is excited, the providing unit can provide a visually emphasized display method. For example, the providing unit can analyze the pet's behavior and facial expressions to detect an excited state. The providing unit can also provide a calm display method if the pet is relaxed. For example, the providing unit can monitor the pet's heart rate and breathing pattern to detect a relaxed state. Furthermore, if the pet is feeling anxious, the providing unit can provide a display method that gives a sense of security. For example, the providing unit can analyze the pet's behavioral patterns and changes in voice to detect an anxious state. This makes it easier for the owner to understand their pet's feelings by adjusting the display method based on the pet's emotions. Some or all of the above-mentioned processing by the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the pet's emotional data into AI and have the AI ​​adjust the display method.

[0105] The providing unit can select the optimal display method based on the owner's past operation history when providing the display data. For example, the providing unit preferentially provides a display method that the owner has previously preferred. For example, the providing unit analyzes the owner's past operation history and identifies the preferred display method. The providing unit can also suggest the optimal display method based on the owner's past operation history. For example, the providing unit analyzes the owner's operation log and suggests the optimal display method. Furthermore, the providing unit can customize the display method by referring to the owner's past operation history. For example, the providing unit customizes the display method based on the owner's operation history. In this way, the optimal display method can be provided by referring to the owner's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's operation history data into AI and cause the AI ​​to select the optimal display method.

[0106] The providing unit can customize the display content according to the owner's current work at the time of providing the display content. For example, if the owner is busy, the providing unit provides concise display content. For example, the providing unit analyzes the owner's current work status and provides concise display content. The providing unit can also provide detailed display content if the owner is relaxed. For example, the providing unit analyzes the owner's work status and provides detailed display content. Furthermore, if the owner is performing a specific task, the providing unit can also provide display content related to that task. For example, the providing unit analyzes the owner's work status and provides related display content. This makes it easier for the owner to understand their pet's feelings by customizing the display content according to the owner's current work. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's work status data into AI and have the AI ​​customize the display content.

[0107] The providing unit can improve the providing method based on the owner's feedback when providing the information. The providing unit, for example, improves the display method based on the owner's feedback. For example, the providing unit analyzes the owner's ratings and comments and improves the display method. The providing unit can also customize the provided content by referring to the owner's feedback. For example, the providing unit adjusts the provided content based on the owner's feedback. Furthermore, the providing unit can improve the efficiency of the providing method by utilizing the owner's feedback. For example, the providing unit analyzes the owner's feedback and improves the efficiency of the providing method. In this way, the providing method can be optimized by reflecting the owner's feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's feedback data into AI and have the AI ​​improve the providing method.

[0108] The providing unit can estimate the pet's emotions and determine the priority of messages to provide based on the estimated pet's emotions. For example, if the pet is excited, the providing unit can prioritize providing messages expressing excitement. For example, the providing unit can analyze the pet's behavior and facial expressions to detect the excited state. Furthermore, if the pet is relaxed, the providing unit can prioritize providing messages expressing relaxation. For example, the providing unit can monitor the pet's heart rate and breathing pattern to detect the relaxed state. Furthermore, if the pet is feeling anxious, the providing unit can prioritize providing messages expressing anxiety. For example, the providing unit can analyze the pet's behavioral patterns and changes in voice to detect the anxious state. This allows important messages to be prioritized by determining the priority based on the pet's emotions. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the pet's emotional data into AI and have the AI ​​determine the priority of messages.

[0109] The providing unit can select the optimal display method based on the owner's device information at the time of providing the display information. For example, if the owner is using a smartphone, the providing unit provides a display method that matches the screen size. For example, the providing unit analyzes the smartphone's screen size and provides the optimal display method. Furthermore, if the owner is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit analyzes the tablet's screen size and provides the optimal display method. Furthermore, if the owner is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit analyzes the smartwatch's screen size and provides the optimal display method. This makes it possible to provide the optimal display method by taking the owner's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's device information into AI and have the AI ​​select the optimal display method.

[0110] The providing unit can provide the display content in multiple languages ​​according to the owner's language setting when providing the content. The providing unit, for example, automatically sets the display content based on the language setting of the owner's device. For example, the providing unit analyzes the device's language setting and automatically sets the display content. The providing unit can also provide a language switching function if the owner uses multiple languages. For example, the providing unit analyzes the language used by the owner and provides the language switching function. Furthermore, if the owner selects a specific language, the providing unit can provide the display content in that language. For example, the providing unit analyzes the language selected by the owner and provides the display content in that language. This makes it easier for the owner to understand their pet's feelings by making the display content multilingual according to the owner's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can input the owner's language setting data into AI and cause the AI ​​to provide the multilingual display content.

[0111] The providing unit can customize the provision method based on the owner's past feedback when providing the information. The providing unit, for example, customizes the display method based on the owner's past feedback. For example, the providing unit analyzes the owner's ratings and comments and customizes the display method. The providing unit can also adjust the provision content by referring to the owner's past feedback. For example, the providing unit adjusts the provision content based on the owner's feedback. Furthermore, the providing unit can improve the efficiency of the provision method by utilizing the owner's past feedback. For example, the providing unit analyzes the owner's feedback and improves the efficiency of the provision method. In this way, the provision method can be optimized by reflecting the owner's past feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the owner's feedback data into AI and have the AI ​​customize the provision method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the pet's cries using a high-precision microphone of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the collected cries. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and verbalizes the pet's intentions using a generation AI. The provision unit is realized by the control unit 46A of the smart device 14 and provides the generated message to the owner. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the pet's cries using a high-precision microphone of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the collected cries. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and verbalizes the pet's intentions using a generation AI. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides the generated message to the owner. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the pet's cries using a high-precision microphone in the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 in the data processing device 12 and extracts features of the collected cries. The generation unit is realized by the specific processing unit 290 in the data processing device 12 and verbalizes the pet's intentions using a generation AI. The provision unit is realized by the control unit 46A of the headset-type terminal 314 and provides the generated message to the owner. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the pet's cries using a high-precision microphone of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and extracts features of the collected cries. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and verbalizes the pet's intentions using a generation AI. The provision unit is realized by the control unit 46A of the robot 414 and provides the generated message to the owner.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The analysis unit can learn the patterns of pet cries in real time and improve the accuracy of the analysis. For example, the analysis unit collects real-time data on pet cries and updates the analysis model using a sequential learning algorithm. The analysis unit can also cluster the patterns of pet cries and detect abnormal cries. Furthermore, the analysis unit can analyze the patterns of pet cries and predict the signs of when a particular cries will occur. This allows the analysis unit to learn the patterns of pet cries in real time and improve the accuracy of the analysis.

[0114] When collecting pet cries, the collection unit can analyze environmental sounds and distinguish between pet cries and environmental sounds. For example, the collection unit can analyze the characteristics of the environmental sounds and separate the pet cries from the environmental sounds. The collection unit can also learn environmental sound patterns and improve the accuracy of collecting pet cries. Furthermore, the collection unit can detect changes in the environmental sounds and adjust the timing of collecting pet cries. In this way, the collection unit can analyze environmental sounds and distinguish between pet cries and environmental sounds, thereby improving the accuracy of collecting pet cries.

[0115] The providing unit can link the generated message to the owner's smart home devices. For example, the providing unit can send the generated message to a smart speaker and play it aloud. The providing unit can also display the generated message on a smart display. Furthermore, the providing unit can express the generated message by the color or brightness of a smart light. In this way, by linking the generated message to the smart home devices, the providing unit can make it easier for the owner to understand their pet's feelings.

[0116] The analysis unit can take the pet's health condition into consideration when analyzing the characteristics of the pet's cries. For example, the analysis unit can monitor the pet's body temperature and heart rate and reflect the health condition in the analysis. The analysis unit can also classify the characteristics of the pet's cries based on the pet's health condition. Furthermore, the analysis unit can detect changes in the pet's health condition and detect abnormal cries early on. In this way, the analysis unit can improve the accuracy of the analysis of the pet's cries by taking the pet's health condition into consideration.

[0117] The providing unit can link the generated message with a calendar app of the pet owner. For example, the providing unit can register the generated message in the calendar app and notify the pet owner as a reminder. The providing unit can also add the generated message to an appointment in the calendar app. Furthermore, the providing unit can also display the generated message as an event in the calendar app. In this way, the providing unit can link the generated message with the calendar app, making it easier for the pet owner to manage their pet's appointments.

[0118] The analysis unit can estimate the pet's emotions and determine the priority of analysis based on the estimated pet's emotions. For example, if the pet is excited, the analysis unit can prioritize analyzing the excited cries. Also, if the pet is relaxed, the analysis unit can prioritize analyzing the relaxed cries. Furthermore, if the pet is feeling anxious, the analysis unit can prioritize analyzing the anxious cries. In this way, the analysis unit can prioritize analyzing important cries by determining the priority of analysis based on the pet's emotions.

[0119] The providing unit can adjust the display method of the generated message based on the owner's emotions. For example, if the owner is stressed, the providing unit can provide a simple and easy-to-understand display method. If the owner is relaxed, the providing unit can also provide a detailed display method. Furthermore, if the owner is excited, the providing unit can also provide a visually emphasized display method. In this way, the providing unit can adjust the display method based on the owner's emotions, making it easier for the owner to understand their pet's feelings.

[0120] The generation unit can estimate the pet's emotion and adjust the tone of the generated message based on the estimated pet's emotion. For example, if the pet is excited, the generation unit can generate a message in a tone that expresses excitement. If the pet is relaxed, the generation unit can also generate a message in a tone that expresses relaxation. Furthermore, if the pet is feeling anxious, the generation unit can also generate a message in a tone that expresses anxiety. In this way, the generation unit can generate a more appropriate message by adjusting the tone of the message based on the pet's emotion.

[0121] The providing unit can adjust the notification method of the generated message based on the owner's emotions. For example, if the owner is busy, the providing unit can provide a concise notification method. If the owner is relaxed, the providing unit can also provide a detailed notification method. Furthermore, if the owner is stressed, the providing unit can also provide a notification method that provides a sense of security. In this way, the providing unit can adjust the notification method based on the owner's emotions, making it easier for the owner to understand their pet's feelings.

[0122] The analysis unit can estimate the pet's emotions and adjust the level of detail of the analysis based on the estimated pet's emotions. For example, the analysis unit can perform a detailed analysis when the pet is excited. The analysis unit can also perform a simplified analysis when the pet is relaxed. Furthermore, the analysis unit can also perform a focused analysis when the pet is feeling anxious. In this way, the analysis unit can obtain more appropriate analysis results by adjusting the level of detail of the analysis based on the pet's emotions.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The collection unit collects pet sounds. Pet sounds include, but are not limited to, sounds from dogs and cats. The collection unit collects pet sounds using, for example, a high-precision microphone. The collection unit can also collect pet sounds in real time. Furthermore, the collection unit can record pet sounds and save them for later analysis. For example, the collection unit collects pet sounds using a high-sensitivity microphone while filtering out ambient noise. Step 2: The analysis unit analyzes the calls collected by the collection unit and extracts features such as vocal pitch and vocal extension. The analysis unit extracts the features of the collected calls using audio signal processing techniques such as Fourier transform and Mel frequency cepstral coefficients (MFCC). The analysis unit can also use AI to analyze the features of the calls with high accuracy. For example, the analysis unit uses Fourier transform to analyze the frequency components of the calls. The analysis unit can also use Mel frequency cepstral coefficients to extract the acoustic features of the calls. Furthermore, the analysis unit can use AI to learn the features of the calls and improve the accuracy of the analysis. Step 3: The generation unit verbalizes the pet's intentions based on the features extracted by the analysis unit. The generation unit uses, for example, a generation AI to generate messages that correspond to the features of the pet's cries. The generation AI is trained based on a large amount of pet cries data collected in advance. This data is labeled by experts and is highly reliable. The generation unit uses this data to generate messages that correspond to the features of the pet's cries. For example, the generation unit generates messages such as "I'm hungry" or "Take me for a walk." Step 4: The providing unit provides the message generated by the generating unit to the owner. For example, the providing unit displays the generated message on the owner's smartphone or tablet. The providing unit can also play the generated message by voice. Furthermore, the providing unit has a function to notify the owner of the generated message. For example, the providing unit notifies the owner of the generated message through a smartphone app.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0176] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects pet sounds; an analysis unit that analyzes the bird calls collected by the collection unit and extracts characteristics of the pitch of the voice or the way the voice is extended; a generation unit that verbalizes the intention of the pet based on the features extracted by the analysis unit; a providing unit that provides the message generated by the generating unit to the owner; Equipped with A system characterized by:

2. The collecting unit Equipped with a highly accurate microphone that captures pet cries 2. The system of claim 1.

3. The analysis unit Extract the features of the collected calls using audio signal processing techniques such as Fourier transform or Mel-frequency cepstrum coefficients.

2. The system of claim 1.

4. The generation unit It learns from pre-collected data on the sounds of multiple pets and generates messages that correspond to the characteristics of pet sounds.

2. The system of claim 1.

5. The providing unit The generated message is displayed on the owner's smartphone or tablet.

2. The system of claim 1.

6. The collecting unit Estimate the pet's emotions and adjust the timing of collecting sounds based on the estimated emotions.

2. The system of claim 1.

Images