System

Abstract

The system according to the embodiment aims to quickly identify the location of a pet when the pet has gone.SOLUTION: A system according to an embodiment includes a registration unit, a collection unit, a prediction unit, a provision unit, and an authentication unit. The registration unit registers face information of the pet. The collection unit collects a behavior pattern and past data of the pet based on the face information registered by the registration unit. The prediction unit predicts a place that may be hidden based on the data collected by the collection unit. The providing unit provides the location predicted by the predicting unit to the user. The authentication unit searches for the place provided by the providing unit and performs face authentication using the camera of the smartphone.SELECTED DRAWING: Figure 1

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] Conventional technology has had the problem of making it difficult to locate a pet when it goes missing.

[0005] The system according to the embodiment aims to quickly locate a pet when it goes missing. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, a collection unit, a prediction unit, a provision unit, and an authentication unit. The registration unit registers facial information of the pet. The collection unit collects behavioral patterns and past data of the pet based on the facial information registered by the registration unit. The prediction unit predicts possible hiding locations based on the data collected by the collection unit. The provision unit provides the user with the locations predicted by the prediction unit. The authentication unit searches the locations provided by the provision unit and performs facial authentication using the smartphone camera. [Effects of the Invention]

[0007] The system according to the embodiment can quickly locate a pet when it goes missing. [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 search system according to an embodiment of the present invention registers facial information of a pet, and when the pet goes missing, a generation AI predicts where the pet is likely hiding and performs facial recognition using a smartphone camera to identify the lost pet. In the pet search system, a user takes a photo of the pet and registers it in the system. Next, when the pet goes missing, the user requests the system to search for the pet. The generation AI predicts a likely hiding place based on the pet's behavioral patterns and past data. The user then uses the smartphone camera to search the predicted location and performs facial recognition. Facial recognition can identify the lost pet. For example, the pet search system records the pet's facial features in detail and collects the data necessary for facial recognition. For example, the system records the pet's facial contours, eye position, nose shape, and other details. Next, when the pet goes missing, the user requests the system to search for the pet. The generation AI predicts a likely hiding place based on the pet's behavioral patterns and past data. For example, it identifies places the pet has previously hidden or places that are predicted based on the pet's behavioral patterns. The user uses the smartphone camera to search the predicted location and performs facial recognition. The video captured by the smartphone camera is analyzed in real time to recognize the pet's face. For example, the pet's facial features are analyzed from the video captured by the smartphone camera and compared with registered data. This allows the pet search system to efficiently search for the pet and enable early detection of lost pets. This allows the pet search system to efficiently search for the pet and enable early detection of lost pets. The user can easily search for the pet using their smartphone, ensuring the pet's safety. For example, by predicting where the pet is likely hiding and performing facial recognition using the smartphone camera, the lost pet can be quickly identified.

[0029] A pet search system according to an embodiment includes a registration unit, a collection unit, a prediction unit, a provision unit, and an authentication unit. The registration unit registers facial information of a pet. The facial information of a pet includes, but is not limited to, photos, videos, and 3D models. The registration unit records, for example, detailed information such as the pet's facial contours, eye position, and nose shape. The registration unit can also register multiple photos of the pet's facial information taken under different lighting conditions and angles. For example, a photo of the pet's face taken in bright sunlight can be registered. The collection unit collects the pet's behavioral patterns and past data. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and play frequency. The collection unit, for example, records the pet's movement history in detail and links it to the behavioral patterns. The collection unit can also collect data taking into account the pet's health condition and stress level. The prediction unit uses a generation AI to predict likely hiding locations based on the data collected by the collection unit. Potential hiding places are identified based on, for example, data on past hiding places and analysis results of behavioral patterns. The prediction unit, for example, prioritizes data on places where the pet has previously hidden. The prediction unit can also predict hiding places taking into account the pet's current health condition and stress level. The providing unit provides the location predicted by the prediction unit to the user. Methods of providing the information include, but are not limited to, a smartphone app, notifications, and map display. The providing unit can, for example, propose an optimal search route taking into account the user's current location and travel route. The providing unit can also estimate the user's emotions and adjust the providing method based on the estimated emotions. The authentication unit analyzes video captured by the smartphone camera in real time and recognizes the pet's face. For example, the authentication unit analyzes the pet's facial features from the video captured by the smartphone camera and compares them with registered data. The authentication unit can also improve authentication accuracy by taking into account changes in the pet's movements and facial expressions. As a result, the pet search system according to the embodiment can register the face information of the pet, predict where the pet is likely hiding, and identify the lost pet by performing face authentication.

[0030] The registration unit can specifically record the facial features of the pet. Examples of facial features include, but are not limited to, the position of the eyes, the shape of the nose, and the color of the fur. The registration unit can record, for example, the pet's facial contour, the position of the eyes, the shape of the nose, and other details. The registration unit can also register multiple photos of the pet's facial features taken under different lighting conditions and angles. For example, photos of the pet's face taken in bright sunlight can be registered. This allows for detailed recording of the pet's facial features, thereby improving the accuracy of facial recognition. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input the pet's facial features into a generation AI and cause the generation AI to record the facial features.

[0031] The collection unit can collect the pet's behavioral patterns and past data. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and play frequency. For example, the collection unit can record the pet's movement history in detail and link it to the behavioral patterns. The collection unit can also collect data taking into account the pet's health condition and stress level. For example, the pet's health check results can be registered along with facial information. By collecting the pet's behavioral patterns and past data, the accuracy of predicting likely hiding places can be improved. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the pet's behavioral patterns into the generation AI and cause the generation AI to collect the behavioral patterns.

[0032] The prediction unit can predict possible hiding places based on the pet's behavioral patterns and past data. Possible hiding places are identified, for example, based on data on past hiding places and the results of analyzing the behavioral patterns. The prediction unit, for example, prioritizes data on places where the pet has previously hidden. The prediction unit can also predict hiding places taking into account the pet's current health condition and stress level. For example, if the pet's health condition is good, the prediction unit predicts a normal hiding place. This makes it easier to find a lost pet by predicting a likely hiding place based on the pet's behavioral patterns and past data. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input the pet's behavioral patterns and past data into the generation AI and have the generation AI predict a likely hiding place.

[0033] The providing unit can provide the predicted location to the user. Methods of providing the predicted location include, but are not limited to, a smartphone app, a notification, and a map display. For example, the providing unit can propose an optimal search route taking into account the user's current location and travel route. The providing unit can also estimate the user's emotions and adjust the providing method based on the estimated emotions. For example, if the user is relaxed, detailed information is provided. By providing the predicted location to the user, the user can efficiently search for the pet. 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 information about the predicted location to a generation AI and cause the generation AI to provide the information.

[0034] The authentication unit can analyze video captured by the smartphone camera in real time and recognize the pet's face. Real-time analysis includes, but is not limited to, the algorithm used and the speed of analysis. For example, the authentication unit can analyze the pet's facial features from the video captured by the smartphone camera and compare them with registered data. The authentication unit can also improve authentication accuracy by taking into account the pet's movements and changes in facial expression. For example, the authentication unit can analyze video of the pet moving and perform facial recognition. This allows for the video captured by the smartphone camera to be analyzed in real time and the pet's face recognized, allowing for the rapid identification of lost pets. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without AI. For example, the authentication unit can input video captured by the smartphone camera into a generation AI and have the generation AI perform facial recognition.

[0035] When registering facial information of a pet, the registration unit can register multiple photos taken under different lighting conditions and angles. Different lighting conditions and angles include, but are not limited to, daytime and nighttime, front and side views, and the like. The registration unit, for example, registers a facial photo of the pet taken under bright sunlight. The registration unit can also register a facial photo of the pet taken under indoor lighting conditions. This improves the accuracy of facial recognition by registering multiple photos taken under different lighting conditions and angles. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input facial photos of the pet taken under different lighting conditions and angles into the generation AI and cause the generation AI to register the facial information.

[0036] When registering the facial information of a pet, the registration unit can periodically update the information, taking into account changes in the pet's face as it grows. Examples of changes in the face as the pet grows include, but are not limited to, taking regular photographs and recording the pet's growth stages. For example, the registration unit periodically registers new facial photos as the pet grows. The registration unit can also re-record the pet's facial features according to the pet's growth stage. This maintains the accuracy of facial recognition by periodically updating the information, taking into account changes in the pet's face as it grows. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input changes in the pet's face as it grows into the generation AI and have the generation AI update the facial information.

[0037] When registering the facial information of a pet, the registration unit can also record the pet's characteristic movements and expressions. Characteristic movements and expressions include, but are not limited to, specific gestures and changes in facial expression. For example, the registration unit registers a facial photograph of the pet performing a specific movement. The registration unit can also record multiple photographs of the pet's characteristic expressions. This improves the accuracy of facial recognition by also recording the pet's characteristic movements and expressions. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the pet's characteristic movements and expressions into a generation AI and cause the generation AI to record the movements and expressions.

[0038] When registering the facial information of a pet, the registration unit can also record the pet's overall body characteristics. Examples of overall body characteristics include, but are not limited to, body size, hair color, and body shape. For example, the registration unit takes a full-body photo of the pet and registers it together with the facial information. The registration unit can also record the pet's body characteristics (hair color, pattern, etc.) in detail. This improves the accuracy of facial recognition by also recording the pet's overall body characteristics. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input the pet's overall body characteristics into the generation AI and cause the generation AI to record the overall body characteristics.

[0039] When registering the facial information of a pet, the registration unit can also record the pet's cries and voice data. The cries and voice data include, but are not limited to, for example, the frequency of the cries and voice patterns. For example, the registration unit records the pet's cries and registers them together with the facial information. The registration unit can also analyze specific voice patterns of the pet and store them in a database. By recording the pet's cries and voice data, the accuracy of facial recognition is improved. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input the pet's cries and voice data into a generation AI and cause the generation AI to record the voice data.

[0040] When registering the pet's facial information, the registration unit can also record the pet's health condition and behavioral patterns. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and frequency of play. The registration unit, for example, registers the pet's health check results along with the facial information. The registration unit can also record the pet's behavioral patterns (eating, sleeping, exercise, etc.) in detail. This improves the accuracy of facial recognition by also recording the pet's health condition and behavioral patterns. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the pet's health condition and behavioral patterns into a generation AI and have the generation AI record the data.

[0041] When collecting the pet's behavioral patterns, the collection unit may also collect GPS data and movement history. Examples of GPS data include, but are not limited to, the accuracy of location information and collection frequency. Examples of movement history include, but are not limited to, past movement routes and stay times. The collection unit may, for example, collect location information from a GPS device attached to the pet's collar. The collection unit may also record the pet's movement history in detail and link it to the behavioral patterns. By collecting GPS data and movement history, the accuracy of behavioral pattern analysis is improved. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the pet's GPS data and movement history into the generation AI and cause the generation AI to collect data.

[0042] When collecting the behavioral patterns of a pet, the collection unit can also collect data on the pet's diet and sleep. The diet and sleep data includes, but is not limited to, for example, meal times and sleep quality. The collection unit, for example, records the pet's meal times and food amounts and links them to the behavioral patterns. The collection unit can also record the pet's sleep times and sleep quality in detail. By collecting the pet's diet and sleep data as well, the accuracy of analyzing the behavioral patterns can be improved. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the pet's diet and sleep data into the generation AI and have the generation AI collect the data.

[0043] When collecting the behavioral patterns of a pet, the collection unit can also collect data on the pet's play and exercise. The play and exercise data includes, for example, the frequency of play and the amount of exercise, but is not limited to these examples. The collection unit, for example, records the amount of time the pet plays and the type of play. The collection unit can also record the amount of exercise and the type of exercise in detail. By collecting data on the pet's play and exercise, the accuracy of analyzing the behavioral patterns can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without using AI. For example, the collection unit can input data on the pet's play and exercise into the generation AI and have the generation AI collect the data.

[0044] When collecting the behavioral patterns of a pet, the collection unit can also collect the pet's relationships with friends and other pets. Relationships with friends and other pets include, but are not limited to, for example, frequency of play and records of joint activities. The collection unit, for example, records the time and place where the pet plays with other pets. The collection unit can also record the pet's relationships with friends in detail. By collecting the pet's relationships with friends and other pets, the accuracy of behavioral pattern analysis can be improved. 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 relationships with friends and other pets into the generation AI and cause the generation AI to collect data.

[0045] When collecting the behavioral patterns of a pet, the collection unit can also collect the behavioral patterns of the pet owner. The behavioral patterns of the owner include, but are not limited to, for example, walking times and meal times. The collection unit, for example, records the time the owner spends with the pet. The collection unit can also link the behavioral patterns of the owner with the behavioral patterns of the pet. By collecting the behavioral patterns of the pet owner as well, the accuracy of analyzing the behavioral patterns is improved. 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 the behavioral patterns of the owner into the generation AI and cause the generation AI to collect data.

[0046] When collecting the behavioral patterns of the pet, the collection unit can also collect environmental data (temperature, humidity, noise, etc.) around the pet. Examples of environmental data include, but are not limited to, temperature, humidity, and noise levels. For example, the collection unit records the temperature and humidity of the place where the pet is spending time. The collection unit can also record the noise level around the pet in detail. By collecting environmental data around the pet as well, the accuracy of analyzing the behavioral patterns can be improved. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input environmental data around the pet into the generation AI and cause the generation AI to collect data.

[0047] When predicting a likely hiding place, the prediction unit can improve prediction accuracy by weighting data on the pet's past hiding places. Data on past hiding places includes, but is not limited to, for example, frequency and duration of stay. For example, the prediction unit preferentially uses data on places where the pet has hidden in the past. The prediction unit can also make predictions taking into account the frequency of the pet's past hiding places. This makes it easier to find a lost pet by weighting the data on the pet's past hiding places to improve prediction accuracy. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input data on the pet's past hiding places into the generation AI and have the generation AI weight the data.

[0048] The prediction unit may take into account the pet's current health condition and stress level when predicting a likely hiding place. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of stress levels include, but are not limited to, behavioral changes and hormone levels. For example, the prediction unit may predict a typical hiding place if the pet is in good health. Furthermore, the prediction unit may preferentially predict a quiet place if the pet is stressed. Taking the pet's current health condition and stress level into consideration improves the accuracy of predictions. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit may input the pet's health condition and stress level into the generation AI and have the generation AI analyze the data.

[0049] The prediction unit can take into account environmental changes around the pet (such as moving house or rearrangement of furniture) when predicting likely hiding places. Environmental changes include, but are not limited to, moving house or rearrangement of furniture. For example, the prediction unit predicts hiding places taking into account the new environment after moving house. The prediction unit can also predict new hiding places after rearrangement of furniture. This improves the accuracy of predictions by taking into account changes in the environment around the pet. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input changes in the environment around the pet into the generation AI and have the generation AI analyze the data.

[0050] The prediction unit can also take into account the behavioral patterns of the pet's friends and other pets when predicting a likely hiding place. The behavioral patterns of the friends and other pets include, but are not limited to, for example, the frequency of play and records of joint activities. The prediction unit makes a prediction, for example, by referring to the hiding places of the pet's friends. The prediction unit can also predict hiding places by taking into account the behavioral patterns of other pets. This improves the accuracy of predictions by taking into account the behavioral patterns of the pet's friends and other pets. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the behavioral patterns of the pet's friends and other pets into the generation AI and have the generation AI analyze the data.

[0051] The prediction unit can also take into account the behavioral patterns of the pet owner when predicting a likely hiding place. The behavioral patterns of the pet owner include, but are not limited to, for example, walk times and meal times. The prediction unit predicts a hiding place, for example, by referring to places the pet owner frequently visits. The prediction unit can also predict a hiding place by taking into account the behavioral patterns of the pet owner. This improves the accuracy of predictions by taking into account the behavioral patterns of the pet owner. Some or all of the above-described processing in the prediction unit can be performed, for example, using a generation AI or without using a generation AI. For example, the prediction unit can input the behavioral patterns of the pet owner into the generation AI and have the generation AI analyze the data.

[0052] The prediction unit can also take into account environmental data (temperature, humidity, noise, etc.) around the pet when predicting a likely hiding place. Environmental data includes, but is not limited to, temperature, humidity, noise level, etc. For example, the prediction unit predicts a hiding place by taking into account the temperature and humidity of the place where the pet is spending time. The prediction unit can also predict a hiding place by taking into account the noise level around the pet. This improves the accuracy of prediction by taking into account the environmental data around the pet. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input environmental data around the pet into the generation AI and have the generation AI analyze the data.

[0053] When providing the predicted location, the providing unit can propose an optimal search route taking into account the user's current location and travel route. Examples of the current location and travel route include, but are not limited to, GPS data and travel history. The providing unit, for example, proposes the shortest route from the user's current location. The providing unit can also propose an efficient search route taking into account the user's travel route. This improves search efficiency by proposing an optimal search route taking into account the user's current location and travel route. 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 user's current location and travel route into the generating AI and cause the generating AI to propose an optimal route.

[0054] When providing a predicted location, the providing unit can customize the provided content by referring to the user's past search history. Past search history includes, but is not limited to, for example, search routes and search times. The providing unit provides information by referring to places the user has previously searched for. The providing unit can also provide customized information by taking the user's past search history into consideration. This improves search efficiency by customizing the provided content by referring to the user's past search history. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's past search history into a generation AI and cause the generation AI to customize the information.

[0055] When providing the predicted location, the providing unit can select the optimal display method according to the user's device information. Device information includes, but is not limited to, for example, a smartphone, tablet, or PC. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. This improves the visibility of the information by selecting the optimal display method according to the user's device information. Some or all of the above-described processing by 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 user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0056] When providing the predicted location, the providing unit may also provide information for obtaining cooperation from the user's friends and family. Cooperation from friends and family may include, but is not limited to, contact methods and details of cooperation. For example, the providing unit may send a message to the user's friends and family requesting their cooperation. The providing unit may also suggest locations and times when the user's friends and family can cooperate. By providing information for obtaining cooperation from the user's friends and family, the efficiency of the search is improved. Some or all of the above-described processing by the providing unit may be performed using, or without using, AI. For example, the providing unit may input information for obtaining cooperation from friends and family into the generation AI and cause the generation AI to provide the information.

[0057] When providing a predicted location, the providing unit may provide advice taking into account the pet's current health condition and stress level. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of stress levels include, but are not limited to, behavioral changes and hormone levels. For example, if the pet's health condition is good, the providing unit may suggest a normal search method. Furthermore, if the pet is feeling stressed, the providing unit may suggest a method of preferentially searching quiet places. This improves search efficiency by providing advice that takes into account the pet's current health condition and stress level. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the pet's health condition and stress level into the generating AI and cause the generating AI to provide advice.

[0058] When providing a predicted location, the providing unit can analyze the user's social media activity to provide related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts to provide information about related tourist spots and stores. This improves search efficiency by analyzing the user's social media activity and providing related information. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0059] When performing facial recognition using a smartphone camera, the authentication unit can analyze images captured under different lighting conditions and angles. Examples of different lighting conditions and angles include, but are not limited to, daytime and nighttime, front and side views, etc. The authentication unit analyzes, for example, images captured under bright sunlight. The authentication unit can also analyze images captured under indoor lighting conditions. By analyzing images captured under different lighting conditions and angles, the accuracy of facial recognition is improved. Some or all of the above-described processing in the authentication unit may be performed using, or without, AI. For example, the authentication unit can input images captured under different lighting conditions and angles into a generation AI and have the generation AI analyze the images.

[0060] When performing facial recognition using a smartphone camera, the authentication unit can improve authentication accuracy by taking into account changes in the pet's movements and facial expressions. Changes in movements and facial expressions include, but are not limited to, specific gestures and changes in facial expressions. For example, the authentication unit analyzes video of the pet while it is moving and performs facial recognition. The authentication unit can also perform facial recognition by taking into account changes in the pet's facial expressions. This improves authentication accuracy by taking into account changes in the pet's movements and facial expressions. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input changes in the pet's movements and facial expressions into a generation AI and have the generation AI analyze the data.

[0061] When performing facial recognition using the smartphone camera, the authentication unit can also analyze the pet's cries and voice data. The cries and voice data include, but are not limited to, for example, the frequency of the cries and voice patterns. The authentication unit, for example, analyzes the pet's cries in real time to perform facial recognition. The authentication unit can also analyze the pet's voice data to improve the accuracy of facial recognition. By analyzing the pet's cries and voice data, the accuracy of authentication is improved. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the pet's cries and voice data into a generation AI and have the generation AI analyze the data.

[0062] When performing facial recognition using the smartphone camera, the authentication unit can also analyze the pet's overall body characteristics. Examples of overall body characteristics include, but are not limited to, body size, hair color, and body shape. The authentication unit, for example, analyzes a full-body video of the pet to perform facial recognition. The authentication unit can also analyze the pet's body characteristics (hair color, pattern, etc.) in detail. This analysis of the pet's overall body characteristics improves the accuracy of authentication. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the pet's overall body characteristics into a generation AI and have the generation AI analyze the data.

[0063] When performing facial recognition using the smartphone camera, the authentication unit can also analyze the pet's health condition and behavioral patterns. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and play frequency. The authentication unit, for example, analyzes the pet's health condition to improve the accuracy of facial recognition. The authentication unit can also analyze the pet's behavioral patterns to improve the accuracy of facial recognition. By analyzing the pet's health condition and behavioral patterns, the accuracy of authentication is improved. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the pet's health condition and behavioral patterns into the generation AI and have the generation AI analyze the data.

[0064] When performing facial recognition using the smartphone camera, the authentication unit can also analyze facial information of the pet's friends and other pets. Facial information of friends and other pets includes, but is not limited to, facial features and behavioral patterns. For example, the authentication unit analyzes the facial information of the pet's friends to improve the accuracy of facial recognition. The authentication unit can also analyze the facial information of other pets to improve the accuracy of facial recognition. By analyzing the facial information of the pet's friends and other pets together, the accuracy of authentication is improved. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input facial information of the pet's friends and other pets into a generation AI and have the generation AI analyze the data.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The pet search system may further include a health management unit that monitors the health condition of the pet. The health management unit collects data such as the pet's body temperature, heart rate, and activity level, and monitors the pet's health condition in real time. For example, the health management unit may collect data from a sensor attached to the pet's collar to monitor the pet's health condition. The health management unit may also send a notification to the user if there is an abnormality in the pet's health condition. This allows the pet's safety to be ensured by constantly keeping track of the pet's health condition. Furthermore, the health management unit may also use data to predict where the pet is likely to be hiding, based on the pet's health condition. For example, if the pet is feeling unwell, quiet places may be predicted as a priority.

[0067] The pet search system may further include a behavior monitoring unit that monitors the behavior of the pet. The behavior monitoring unit monitors the pet's movements in real time and detects abnormal behavior. For example, if the pet is behaving differently than normal, a notification can be sent to the user. The behavior monitoring unit can also collect pet behavior data and analyze the pet's behavior patterns. This allows the pet's behavior to be constantly monitored, ensuring the pet's safety. Furthermore, the behavior monitoring unit can also use the pet behavior data as data for predicting where the pet is likely to be hiding. For example, if the pet tends to frequently hide in a specific place, that place can be predicted with priority.

[0068] The pet search system may further include a location tracking unit that tracks the location information of the pet. The location tracking unit collects location information from a GPS device attached to the pet's collar and tracks the pet's current location in real time. For example, if the pet leaves a specific area, a notification can be sent to the user. The location tracking unit can also record the pet's movement history and analyze the pet's behavioral patterns. This allows the pet's location information to be constantly known, ensuring the pet's safety. Furthermore, the location tracking unit can also use the pet's location information as data for predicting where the pet is likely to be hiding. For example, if the pet tends to frequently hide in a specific area, that area can be given priority in predictions.

[0069] The pet search system can further include a health advice unit that suggests a search method that takes the pet's health condition into consideration. The health advice unit analyzes the pet's health condition and suggests a search method that corresponds to the pet's health condition. For example, if the pet's body temperature and heart rate are normal, it suggests a normal search method. Also, if the pet is not feeling well, it can suggest a method that prioritizes searching in quiet places. This makes it easier to find the pet by suggesting a search method that corresponds to the pet's health condition. Furthermore, the health advice unit can also use data to predict where the pet is likely to be hiding, based on the pet's health condition. For example, if the pet is not feeling well, it can predict that quiet places will be prioritized.

[0070] The pet search system may further include a behavior analysis unit that analyzes the behavioral patterns of a pet. The behavior analysis unit collects behavioral data of the pet and analyzes the behavioral patterns of the pet. For example, if a pet tends to hide in a specific place at a specific time of day, it can predict that place with priority. The behavior analysis unit can also use the data to predict where the pet is likely to hide based on the pet's behavioral patterns. This makes it easier to find the pet by analyzing the pet's behavioral patterns. Furthermore, the behavior analysis unit can also estimate the pet's health condition and emotions based on the pet's behavioral data. For example, if a pet is performing a specific behavior, it can estimate the pet's health condition and emotions from that behavior.

[0071] The pet search system may further include an anomaly detection unit that monitors the pet's behavior and detects abnormalities. The anomaly detection unit monitors the pet's behavior data in real time and detects abnormal behavior. For example, if the pet is behaving differently than usual, a notification can be sent to the user. The anomaly detection unit can also use the pet's behavior data as data to predict where the pet is likely to be hiding. This allows the pet's safety to be ensured by constantly monitoring the pet's behavior. Furthermore, the anomaly detection unit can also estimate the pet's health condition and emotions based on the pet's behavior data. For example, if the pet is behaving in a specific manner, the pet's health condition and emotions can be estimated from that behavior.

[0072] The pet search system may further include an action suggestion unit that analyzes the behavioral patterns of the pet and suggests a search method based on the behavioral patterns of the pet. The action suggestion unit collects pet behavior data and analyzes the pet's behavioral patterns. For example, if the pet tends to hide in a specific place at a specific time of day, it can predict that place with priority. The action suggestion unit can also use the data to predict where the pet is likely to be hiding based on the pet's behavioral patterns. This makes it easier to find the pet by analyzing the pet's behavioral patterns. Furthermore, the action suggestion unit can also estimate the pet's health condition and emotions based on the pet's behavioral data. For example, if the pet is performing a specific behavior, it can estimate the pet's health condition and emotions from that behavior.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The registration unit registers the pet's facial information. This information includes photos, videos, and 3D models. The registration unit records the pet's facial contours, eye position, nose shape, and other details, and can register multiple photos taken under different lighting conditions and angles. Step 2: The collection unit collects the pet's behavioral patterns and past data. The behavioral patterns include movement history, eating patterns, and frequency of play. The collection unit records the pet's movement history in detail and links it to the behavioral patterns. It can also collect data taking into account the pet's health condition and stress level. Step 3: The prediction unit uses the generation AI to predict likely hiding places based on the data collected by the collection unit. Likely hiding places are identified based on data on past hiding places and the results of analyzing behavioral patterns. The prediction unit prioritizes data on places where the pet has previously hidden, and can also predict hiding places taking into account the pet's current health condition and stress level. Step 4: The providing unit provides the location predicted by the prediction unit to the user. Methods of providing the location include a smartphone app, notification, map display, etc. The providing unit proposes the optimal search route taking into account the user's current location and travel route, and can also adjust the method of providing the location by estimating the user's emotions. Step 5: The authentication unit analyzes the video captured by the smartphone camera in real time and recognizes the pet's face. The authentication unit analyzes the pet's facial features from the video captured by the smartphone camera and compares them with registered data. It is also possible to improve authentication accuracy by taking into account changes in the pet's movements and facial expressions.

[0075] (Example 2) A pet search system according to an embodiment of the present invention registers facial information of a pet, and when the pet goes missing, a generation AI predicts where the pet is likely hiding and performs facial recognition using a smartphone camera to identify the lost pet. In the pet search system, a user takes a photo of the pet and registers it in the system. Next, when the pet goes missing, the user requests the system to search for the pet. The generation AI predicts a likely hiding place based on the pet's behavioral patterns and past data. The user then uses the smartphone camera to search the predicted location and performs facial recognition. Facial recognition can identify the lost pet. For example, the pet search system records the pet's facial features in detail and collects the data necessary for facial recognition. For example, the system records the pet's facial contours, eye position, nose shape, and other details. Next, when the pet goes missing, the user requests the system to search for the pet. The generation AI predicts a likely hiding place based on the pet's behavioral patterns and past data. For example, it identifies places the pet has previously hidden or places that are predicted based on the pet's behavioral patterns. The user uses the smartphone camera to search the predicted location and performs facial recognition. The video captured by the smartphone camera is analyzed in real time to recognize the pet's face. For example, the pet's facial features are analyzed from the video captured by the smartphone camera and compared with registered data. This allows the pet search system to efficiently search for the pet and enable early detection of lost pets. This allows the pet search system to efficiently search for the pet and enable early detection of lost pets. The user can easily search for the pet using their smartphone, ensuring the pet's safety. For example, by predicting where the pet is likely hiding and performing facial recognition using the smartphone camera, the lost pet can be quickly identified.

[0076] A pet search system according to an embodiment includes a registration unit, a collection unit, a prediction unit, a provision unit, and an authentication unit. The registration unit registers facial information of a pet. The facial information of a pet includes, but is not limited to, photos, videos, and 3D models. The registration unit records, for example, detailed information such as the pet's facial contours, eye position, and nose shape. The registration unit can also register multiple photos of the pet's facial information taken under different lighting conditions and angles. For example, a photo of the pet's face taken in bright sunlight can be registered. The collection unit collects the pet's behavioral patterns and past data. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and play frequency. The collection unit, for example, records the pet's movement history in detail and links it to the behavioral patterns. The collection unit can also collect data taking into account the pet's health condition and stress level. The prediction unit uses a generation AI to predict likely hiding locations based on the data collected by the collection unit. Potential hiding places are identified based on, for example, data on past hiding places and analysis results of behavioral patterns. The prediction unit, for example, prioritizes data on places where the pet has previously hidden. The prediction unit can also predict hiding places taking into account the pet's current health condition and stress level. The providing unit provides the location predicted by the prediction unit to the user. Methods of providing the information include, but are not limited to, a smartphone app, notifications, and map display. The providing unit can, for example, propose an optimal search route taking into account the user's current location and travel route. The providing unit can also estimate the user's emotions and adjust the providing method based on the estimated emotions. The authentication unit analyzes video captured by the smartphone camera in real time and recognizes the pet's face. For example, the authentication unit analyzes the pet's facial features from the video captured by the smartphone camera and compares them with registered data. The authentication unit can also improve authentication accuracy by taking into account changes in the pet's movements and facial expressions. As a result, the pet search system according to the embodiment can register the face information of the pet, predict where the pet is likely hiding, and identify the lost pet by performing face authentication.

[0077] The registration unit can specifically record the facial features of the pet. Examples of facial features include, but are not limited to, the position of the eyes, the shape of the nose, and the color of the fur. The registration unit can record, for example, the pet's facial contour, the position of the eyes, the shape of the nose, and other details. The registration unit can also register multiple photos of the pet's facial features taken under different lighting conditions and angles. For example, photos of the pet's face taken in bright sunlight can be registered. This allows for detailed recording of the pet's facial features, thereby improving the accuracy of facial recognition. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input the pet's facial features into a generation AI and cause the generation AI to record the facial features.

[0078] The collection unit can collect the pet's behavioral patterns and past data. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and play frequency. For example, the collection unit can record the pet's movement history in detail and link it to the behavioral patterns. The collection unit can also collect data taking into account the pet's health condition and stress level. For example, the pet's health check results can be registered along with facial information. By collecting the pet's behavioral patterns and past data, the accuracy of predicting likely hiding places can be improved. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the pet's behavioral patterns into the generation AI and cause the generation AI to collect the behavioral patterns.

[0079] The prediction unit can predict possible hiding places based on the pet's behavioral patterns and past data. Possible hiding places are identified, for example, based on data on past hiding places and the results of analyzing the behavioral patterns. The prediction unit, for example, prioritizes data on places where the pet has previously hidden. The prediction unit can also predict hiding places taking into account the pet's current health condition and stress level. For example, if the pet's health condition is good, the prediction unit predicts a normal hiding place. This makes it easier to find a lost pet by predicting a likely hiding place based on the pet's behavioral patterns and past data. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input the pet's behavioral patterns and past data into the generation AI and have the generation AI predict a likely hiding place.

[0080] The providing unit can provide the predicted location to the user. Methods of providing the predicted location include, but are not limited to, a smartphone app, a notification, and a map display. For example, the providing unit can propose an optimal search route taking into account the user's current location and travel route. The providing unit can also estimate the user's emotions and adjust the providing method based on the estimated emotions. For example, if the user is relaxed, detailed information is provided. By providing the predicted location to the user, the user can efficiently search for the pet. 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 information about the predicted location to a generation AI and cause the generation AI to provide the information.

[0081] The authentication unit can analyze video captured by the smartphone camera in real time and recognize the pet's face. Real-time analysis includes, but is not limited to, the algorithm used and the speed of analysis. For example, the authentication unit can analyze the pet's facial features from the video captured by the smartphone camera and compare them with registered data. The authentication unit can also improve authentication accuracy by taking into account the pet's movements and changes in facial expression. For example, the authentication unit can analyze video of the pet moving and perform facial recognition. This allows for the video captured by the smartphone camera to be analyzed in real time and the pet's face recognized, allowing for the rapid identification of lost pets. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without AI. For example, the authentication unit can input video captured by the smartphone camera into a generation AI and have the generation AI perform facial recognition.

[0082] The registration unit can analyze the user's emotions and adjust the timing of registering the pet's facial information based on the analyzed user's emotions. Examples of the user's emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, if the user is relaxed, the registration unit can send a notification prompting the user to register the pet's facial information. Furthermore, if the user is busy, the registration unit can also set a reminder for later registration. This improves registration efficiency by adjusting the timing of registering the pet's facial information according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the user's emotional data into the generation AI and have the generation AI perform emotion analysis.

[0083] When registering facial information of a pet, the registration unit can register multiple photos taken under different lighting conditions and angles. Different lighting conditions and angles include, but are not limited to, daytime and nighttime, front and side views, and the like. The registration unit, for example, registers a facial photo of the pet taken under bright sunlight. The registration unit can also register a facial photo of the pet taken under indoor lighting conditions. This improves the accuracy of facial recognition by registering multiple photos taken under different lighting conditions and angles. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input facial photos of the pet taken under different lighting conditions and angles into the generation AI and cause the generation AI to register the facial information.

[0084] When registering the facial information of a pet, the registration unit can periodically update the information, taking into account changes in the pet's face as it grows. Examples of changes in the face as the pet grows include, but are not limited to, taking regular photographs and recording the pet's growth stages. For example, the registration unit periodically registers new facial photos as the pet grows. The registration unit can also re-record the pet's facial features according to the pet's growth stage. This maintains the accuracy of facial recognition by periodically updating the information, taking into account changes in the pet's face as it grows. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input changes in the pet's face as it grows into the generation AI and have the generation AI update the facial information.

[0085] When registering the facial information of a pet, the registration unit can also record the pet's characteristic movements and expressions. Characteristic movements and expressions include, but are not limited to, specific gestures and changes in facial expression. For example, the registration unit registers a facial photograph of the pet performing a specific movement. The registration unit can also record multiple photographs of the pet's characteristic expressions. This improves the accuracy of facial recognition by also recording the pet's characteristic movements and expressions. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the pet's characteristic movements and expressions into a generation AI and cause the generation AI to record the movements and expressions.

[0086] The registration unit can estimate the user's emotions and determine the priority of facial information to be registered based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, if the user is relaxed, the registration unit prioritizes registering detailed facial information. Furthermore, if the user is in a hurry, the registration unit can prioritize registering basic facial information. This improves registration efficiency by determining the priority of facial information to be registered according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the registration unit can be performed using, for example, AI, or without AI. For example, the registration unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0087] When registering the facial information of a pet, the registration unit can also record the pet's overall body characteristics. Examples of overall body characteristics include, but are not limited to, body size, hair color, and body shape. For example, the registration unit takes a full-body photo of the pet and registers it together with the facial information. The registration unit can also record the pet's body characteristics (hair color, pattern, etc.) in detail. This improves the accuracy of facial recognition by also recording the pet's overall body characteristics. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input the pet's overall body characteristics into the generation AI and cause the generation AI to record the overall body characteristics.

[0088] When registering the facial information of a pet, the registration unit can also record the pet's cries and voice data. The cries and voice data include, but are not limited to, for example, the frequency of the cries and voice patterns. For example, the registration unit records the pet's cries and registers them together with the facial information. The registration unit can also analyze specific voice patterns of the pet and store them in a database. By recording the pet's cries and voice data, the accuracy of facial recognition is improved. Some or all of the above-described processing in the registration unit may be performed using, or without, AI. For example, the registration unit can input the pet's cries and voice data into a generation AI and cause the generation AI to record the voice data.

[0089] When registering the pet's facial information, the registration unit can also record the pet's health condition and behavioral patterns. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and frequency of play. The registration unit, for example, registers the pet's health check results along with the facial information. The registration unit can also record the pet's behavioral patterns (eating, sleeping, exercise, etc.) in detail. This improves the accuracy of facial recognition by also recording the pet's health condition and behavioral patterns. Some or all of the above-described processing in the registration unit may be performed using, for example, AI, or may be performed without using AI. For example, the registration unit can input the pet's health condition and behavioral patterns into a generation AI and have the generation AI record the data.

[0090] The collection unit can estimate the user's emotions and adjust the behavioral patterns and data collection frequency based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, when the user is relaxed, the collection unit collects data at a normal collection frequency. Furthermore, when the user is busy, the collection unit can also collect data at a reduced collection frequency. This improves collection efficiency by adjusting the behavioral patterns and data collection frequency according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0091] When collecting the pet's behavioral patterns, the collection unit may also collect GPS data and movement history. Examples of GPS data include, but are not limited to, the accuracy of location information and collection frequency. Examples of movement history include, but are not limited to, past movement routes and stay times. The collection unit may, for example, collect location information from a GPS device attached to the pet's collar. The collection unit may also record the pet's movement history in detail and link it to the behavioral patterns. By collecting GPS data and movement history, the accuracy of behavioral pattern analysis is improved. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the pet's GPS data and movement history into the generation AI and cause the generation AI to collect data.

[0092] When collecting the behavioral patterns of a pet, the collection unit can also collect data on the pet's diet and sleep. The diet and sleep data includes, but is not limited to, for example, meal times and sleep quality. The collection unit, for example, records the pet's meal times and food amounts and links them to the behavioral patterns. The collection unit can also record the pet's sleep times and sleep quality in detail. By collecting the pet's diet and sleep data as well, the accuracy of analyzing the behavioral patterns can be improved. Some or all of the above-described processing by the collection unit can be performed using, or without, AI. For example, the collection unit can input the pet's diet and sleep data into the generation AI and have the generation AI collect the data.

[0093] When collecting the behavioral patterns of a pet, the collection unit can also collect data on the pet's play and exercise. The play and exercise data includes, for example, the frequency of play and the amount of exercise, but is not limited to these examples. The collection unit, for example, records the amount of time the pet plays and the type of play. The collection unit can also record the amount of exercise and the type of exercise in detail. By collecting data on the pet's play and exercise, the accuracy of analyzing the behavioral patterns can be improved. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without using AI. For example, the collection unit can input data on the pet's play and exercise into the generation AI and have the generation AI collect the data.

[0094] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, when the user is relaxed, the collection unit performs normal data collection. Furthermore, when the user is busy, the collection unit can also prioritize the collection of only important data. This improves collection efficiency by determining the priority of data to be collected according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0095] When collecting the behavioral patterns of a pet, the collection unit can also collect the pet's relationships with friends and other pets. Relationships with friends and other pets include, but are not limited to, for example, frequency of play and records of joint activities. The collection unit, for example, records the time and place where the pet plays with other pets. The collection unit can also record the pet's relationships with friends in detail. By collecting the pet's relationships with friends and other pets, the accuracy of behavioral pattern analysis can be improved. 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 relationships with friends and other pets into the generation AI and cause the generation AI to collect data.

[0096] When collecting the behavioral patterns of a pet, the collection unit can also collect the behavioral patterns of the pet owner. The behavioral patterns of the owner include, but are not limited to, for example, walking times and meal times. The collection unit, for example, records the time the owner spends with the pet. The collection unit can also link the behavioral patterns of the owner with the behavioral patterns of the pet. By collecting the behavioral patterns of the pet owner as well, the accuracy of analyzing the behavioral patterns is improved. 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 the behavioral patterns of the owner into the generation AI and cause the generation AI to collect data.

[0097] When collecting the behavioral patterns of the pet, the collection unit can also collect environmental data (temperature, humidity, noise, etc.) around the pet. Examples of environmental data include, but are not limited to, temperature, humidity, and noise levels. For example, the collection unit records the temperature and humidity of the place where the pet is spending time. The collection unit can also record the noise level around the pet in detail. By collecting environmental data around the pet as well, the accuracy of analyzing the behavioral patterns can be improved. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input environmental data around the pet into the generation AI and cause the generation AI to collect data.

[0098] The prediction unit can analyze the user's emotions and adjust the method for predicting possible hiding locations based on the analyzed user's emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, if the user is relaxed, the prediction unit uses a normal prediction method. Alternatively, if the user is in a hurry, the prediction unit can use a method that quickly provides prediction results. This improves the accuracy of predictions by adjusting the method for predicting possible hiding locations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the prediction unit can be performed using, for example, the generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0099] When predicting a likely hiding place, the prediction unit can improve prediction accuracy by weighting data on the pet's past hiding places. Data on past hiding places includes, but is not limited to, for example, frequency and duration of stay. For example, the prediction unit preferentially uses data on places where the pet has hidden in the past. The prediction unit can also make predictions taking into account the frequency of the pet's past hiding places. This makes it easier to find a lost pet by weighting the data on the pet's past hiding places to improve prediction accuracy. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit can input data on the pet's past hiding places into the generation AI and have the generation AI weight the data.

[0100] The prediction unit may take into account the pet's current health condition and stress level when predicting a likely hiding place. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of stress levels include, but are not limited to, behavioral changes and hormone levels. For example, the prediction unit may predict a typical hiding place if the pet is in good health. Furthermore, the prediction unit may preferentially predict a quiet place if the pet is stressed. Taking the pet's current health condition and stress level into consideration improves the accuracy of predictions. Some or all of the above-described processing in the prediction unit may be performed using, or without, a generation AI. For example, the prediction unit may input the pet's health condition and stress level into the generation AI and have the generation AI analyze the data.

[0101] The prediction unit can take into account environmental changes around the pet (such as moving house or rearrangement of furniture) when predicting likely hiding places. Environmental changes include, but are not limited to, moving house or rearrangement of furniture. For example, the prediction unit predicts hiding places taking into account the new environment after moving house. The prediction unit can also predict new hiding places after rearrangement of furniture. This improves the accuracy of predictions by taking into account changes in the environment around the pet. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input changes in the environment around the pet into the generation AI and have the generation AI analyze the data.

[0102] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, if the user is relaxed, the prediction unit can display detailed prediction results. Furthermore, if the user is in a hurry, the prediction unit can display concise prediction results. This allows information to be provided in a format that is easy for the user to understand by adjusting the display method of the prediction results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the prediction unit can be performed using, for example, the generation AI. For example, the prediction unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0103] The prediction unit can also take into account the behavioral patterns of the pet's friends and other pets when predicting a likely hiding place. The behavioral patterns of the friends and other pets include, but are not limited to, for example, the frequency of play and records of joint activities. The prediction unit makes a prediction, for example, by referring to the hiding places of the pet's friends. The prediction unit can also predict hiding places by taking into account the behavioral patterns of other pets. This improves the accuracy of predictions by taking into account the behavioral patterns of the pet's friends and other pets. Some or all of the above-described processing in the prediction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the prediction unit can input the behavioral patterns of the pet's friends and other pets into the generation AI and have the generation AI analyze the data.

[0104] The prediction unit can also take into account the behavioral patterns of the pet owner when predicting a likely hiding place. The behavioral patterns of the pet owner include, but are not limited to, for example, walk times and meal times. The prediction unit predicts a hiding place, for example, by referring to places the pet owner frequently visits. The prediction unit can also predict a hiding place by taking into account the behavioral patterns of the pet owner. This improves the accuracy of predictions by taking into account the behavioral patterns of the pet owner. Some or all of the above-described processing in the prediction unit can be performed, for example, using a generation AI or without using a generation AI. For example, the prediction unit can input the behavioral patterns of the pet owner into the generation AI and have the generation AI analyze the data.

[0105] The prediction unit can also take into account environmental data (temperature, humidity, noise, etc.) around the pet when predicting a likely hiding place. Environmental data includes, but is not limited to, temperature, humidity, noise level, etc. For example, the prediction unit predicts a hiding place by taking into account the temperature and humidity of the place where the pet is spending time. The prediction unit can also predict a hiding place by taking into account the noise level around the pet. This improves the accuracy of prediction by taking into account the environmental data around the pet. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the prediction unit can input environmental data around the pet into the generation AI and have the generation AI analyze the data.

[0106] The providing unit can analyze the user's emotions and adjust the method of providing the predicted location based on the analyzed user's emotions. Examples of the user's emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide concise information. By adjusting the method of providing the predicted location based on the user's emotions, the information can be provided in a format that is easy for the user to understand. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0107] When providing the predicted location, the providing unit can propose an optimal search route taking into account the user's current location and travel route. Examples of the current location and travel route include, but are not limited to, GPS data and travel history. The providing unit, for example, proposes the shortest route from the user's current location. The providing unit can also propose an efficient search route taking into account the user's travel route. This improves search efficiency by proposing an optimal search route taking into account the user's current location and travel route. 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 user's current location and travel route into the generating AI and cause the generating AI to propose an optimal route.

[0108] When providing a predicted location, the providing unit can customize the provided content by referring to the user's past search history. Past search history includes, but is not limited to, for example, search routes and search times. The providing unit provides information by referring to places the user has previously searched for. The providing unit can also provide customized information by taking the user's past search history into consideration. This improves search efficiency by customizing the provided content by referring to the user's past search history. Some or all of the above-described processing by the providing unit may be performed, for example, using AI or without AI. For example, the providing unit can input the user's past search history into a generation AI and cause the generation AI to customize the information.

[0109] When providing the predicted location, the providing unit can select the optimal display method according to the user's device information. Device information includes, but is not limited to, for example, a smartphone, tablet, or PC. For example, if the user is using a smartphone, the providing unit can provide a display method tailored to the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. This improves the visibility of the information by selecting the optimal display method according to the user's device information. Some or all of the above-described processing by 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 user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0110] The providing unit can estimate the user's emotions and determine the priority of information to be provided based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, when the user is relaxed, the providing unit can prioritize providing detailed information. Furthermore, when the user is in a hurry, the providing unit can prioritize providing important information. This improves the efficiency of information provision by determining the priority of information to be provided according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0111] When providing the predicted location, the providing unit may also provide information for obtaining cooperation from the user's friends and family. Cooperation from friends and family may include, but is not limited to, contact methods and details of cooperation. For example, the providing unit may send a message to the user's friends and family requesting their cooperation. The providing unit may also suggest locations and times when the user's friends and family can cooperate. By providing information for obtaining cooperation from the user's friends and family, the efficiency of the search is improved. Some or all of the above-described processing by the providing unit may be performed using, or without using, AI. For example, the providing unit may input information for obtaining cooperation from friends and family into the generation AI and cause the generation AI to provide the information.

[0112] When providing a predicted location, the providing unit may provide advice taking into account the pet's current health condition and stress level. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of stress levels include, but are not limited to, behavioral changes and hormone levels. For example, if the pet's health condition is good, the providing unit may suggest a normal search method. Furthermore, if the pet is feeling stressed, the providing unit may suggest a method of preferentially searching quiet places. This improves search efficiency by providing advice that takes into account the pet's current health condition and stress level. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit may input the pet's health condition and stress level into the generating AI and cause the generating AI to provide advice.

[0113] When providing a predicted location, the providing unit can analyze the user's social media activity to provide related information. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and comments. For example, the providing unit can provide information about places where the user has checked in on social media. The providing unit can also analyze the content of the user's social media posts to provide information about related tourist spots and stores. This improves search efficiency by analyzing the user's social media activity and providing related information. Some or all of the above-described processing by the providing unit can be performed using, or without, AI. For example, the providing unit can input the user's social media activity into a generation AI and cause the generation AI to provide related information.

[0114] The authentication unit can analyze the user's emotions and adjust the accuracy of facial recognition based on the analyzed user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, when the user is relaxed, the authentication unit uses normal facial recognition accuracy. Furthermore, when the user is in a hurry, the authentication unit can adjust the accuracy to perform facial recognition quickly. This improves the accuracy of authentication by adjusting the accuracy of facial recognition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the authentication unit may be performed using AI, or may be performed without AI. For example, the authentication unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0115] When performing facial recognition using a smartphone camera, the authentication unit can analyze images captured under different lighting conditions and angles. Examples of different lighting conditions and angles include, but are not limited to, daytime and nighttime, front and side views, etc. The authentication unit analyzes, for example, images captured under bright sunlight. The authentication unit can also analyze images captured under indoor lighting conditions. By analyzing images captured under different lighting conditions and angles, the accuracy of facial recognition is improved. Some or all of the above-described processing in the authentication unit may be performed using, or without, AI. For example, the authentication unit can input images captured under different lighting conditions and angles into a generation AI and have the generation AI analyze the images.

[0116] When performing facial recognition using a smartphone camera, the authentication unit can improve authentication accuracy by taking into account changes in the pet's movements and facial expressions. Changes in movements and facial expressions include, but are not limited to, specific gestures and changes in facial expressions. For example, the authentication unit analyzes video of the pet while it is moving and performs facial recognition. The authentication unit can also perform facial recognition by taking into account changes in the pet's facial expressions. This improves authentication accuracy by taking into account changes in the pet's movements and facial expressions. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input changes in the pet's movements and facial expressions into a generation AI and have the generation AI analyze the data.

[0117] When performing facial recognition using the smartphone camera, the authentication unit can also analyze the pet's cries and voice data. The cries and voice data include, but are not limited to, for example, the frequency of the cries and voice patterns. The authentication unit, for example, analyzes the pet's cries in real time to perform facial recognition. The authentication unit can also analyze the pet's voice data to improve the accuracy of facial recognition. By analyzing the pet's cries and voice data, the accuracy of authentication is improved. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the pet's cries and voice data into a generation AI and have the generation AI analyze the data.

[0118] The authentication unit can estimate the user's emotions and adjust the display method of the facial recognition results based on the estimated user emotions. Examples of user emotions include, but are not limited to, facial expression analysis, voice analysis, and survey results. For example, if the user is relaxed, the authentication unit can display detailed results. Alternatively, if the user is in a hurry, the authentication unit can display concise results. This allows the user to easily understand information by adjusting the display method of the facial recognition results according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the authentication unit may be performed using AI, or without AI. For example, the authentication unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0119] When performing facial recognition using the smartphone camera, the authentication unit can also analyze the pet's overall body characteristics. Examples of overall body characteristics include, but are not limited to, body size, hair color, and body shape. The authentication unit, for example, analyzes a full-body video of the pet to perform facial recognition. The authentication unit can also analyze the pet's body characteristics (hair color, pattern, etc.) in detail. This analysis of the pet's overall body characteristics improves the accuracy of authentication. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the pet's overall body characteristics into a generation AI and have the generation AI analyze the data.

[0120] When performing facial recognition using the smartphone camera, the authentication unit can also analyze the pet's health condition and behavioral patterns. Examples of health conditions include, but are not limited to, body temperature, heart rate, and activity level. Examples of behavioral patterns include, but are not limited to, movement history, eating patterns, and play frequency. The authentication unit, for example, analyzes the pet's health condition to improve the accuracy of facial recognition. The authentication unit can also analyze the pet's behavioral patterns to improve the accuracy of facial recognition. By analyzing the pet's health condition and behavioral patterns, the accuracy of authentication is improved. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input the pet's health condition and behavioral patterns into the generation AI and have the generation AI analyze the data.

[0121] When performing facial recognition using the smartphone camera, the authentication unit can also analyze facial information of the pet's friends and other pets. Facial information of friends and other pets includes, but is not limited to, facial features and behavioral patterns. For example, the authentication unit analyzes the facial information of the pet's friends to improve the accuracy of facial recognition. The authentication unit can also analyze the facial information of other pets to improve the accuracy of facial recognition. By analyzing the facial information of the pet's friends and other pets together, the accuracy of authentication is improved. Some or all of the above-described processing in the authentication unit may be performed using, for example, AI, or may be performed without using AI. For example, the authentication unit can input facial information of the pet's friends and other pets into a generation AI and have the generation AI analyze the data. === Hard Collateral 1-1 === Each of the multiple elements, including the registration unit, collection unit, prediction unit, provision unit, and authentication 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 registration unit captures a photograph of the pet's face using the camera 42 of the smart device 14 and registers the information using the specific processing unit 290 of the data processing device 12. The collection unit collects the pet's behavioral patterns and past data using, for example, the control unit 46A of the smart device 14 and transmits the collected data to the specific processing unit 290 of the data processing device 12. The prediction unit predicts a likely hiding location using a generation AI using the specific processing unit 290 of the data processing device 12. The provision unit notifies the user of the predicted location via, for example, the output device 40 of the smart device 14. The authentication unit analyzes the video captured using the camera 42 of the smart device 14 in real time and recognizes the pet's face using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned registration unit, collection unit, prediction unit, provision unit, and authentication unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit captures facial information of the pet using the camera 42 of the smart glasses 214 and registers the information using the specific processing unit 290 of the data processing device 12. The collection unit collects behavioral patterns and past data of the pet using, for example, the control unit 46A of the smart glasses 214 and transmits the collected data to the specific processing unit 290 of the data processing device 12. The prediction unit predicts a likely hiding location using generated AI using the specific processing unit 290 of the data processing device 12. The provision unit notifies the user of the predicted location via, for example, the speaker 240 of the smart glasses 214. The authentication unit analyzes the video captured using the camera 42 of the smart glasses 214 in real time and recognizes the pet's face using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, collection unit, prediction unit, provision unit, and authentication unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the registration unit captures facial information of the pet using the camera 42 of the headset-type terminal 314 and registers the information using the specific processing unit 290 of the data processing device 12. The collection unit collects behavioral patterns and past data of the pet using, for example, the control unit 46A of the headset-type terminal 314 and transmits the collected data to the specific processing unit 290 of the data processing device 12. The prediction unit predicts a likely hiding location using a generation AI using the specific processing unit 290 of the data processing device 12. The provision unit notifies the user of the predicted location via, for example, the display 343 of the headset-type terminal 314. The authentication unit analyzes in real time the video captured using the camera 42 of the headset-type terminal 314 and recognizes the pet's face using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, collection unit, prediction unit, provision unit, and authentication unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit photographs the pet's facial information using the camera 42 of the robot 414 and registers the information using the specific processing unit 290 of the data processing device 12. The collection unit collects the pet's behavioral patterns and past data using, for example, the control unit 46A of the robot 414 and transmits the collected data to the specific processing unit 290 of the data processing device 12. The prediction unit predicts a likely hiding location using a generation AI using the specific processing unit 290 of the data processing device 12. The provision unit notifies the user of the predicted location via, for example, the speaker 240 of the robot 414. The authentication unit analyzes the video captured using the camera 42 of the robot 414 in real time and recognizes the pet's face using the specific processing unit 290 of the data processing device 12.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The pet search system may further include a health management unit that monitors the health condition of the pet. The health management unit collects data such as the pet's body temperature, heart rate, and activity level, and monitors the pet's health condition in real time. For example, the health management unit may collect data from a sensor attached to the pet's collar to monitor the pet's health condition. The health management unit may also send a notification to the user if there is an abnormality in the pet's health condition. This allows the pet's safety to be ensured by constantly keeping track of the pet's health condition. Furthermore, the health management unit may also use data to predict where the pet is likely to be hiding, based on the pet's health condition. For example, if the pet is feeling unwell, quiet places may be predicted as a priority.

[0124] The pet search system may further include a behavior monitoring unit that monitors the behavior of the pet. The behavior monitoring unit monitors the pet's movements in real time and detects abnormal behavior. For example, if the pet is behaving differently than normal, a notification can be sent to the user. The behavior monitoring unit can also collect pet behavior data and analyze the pet's behavior patterns. This allows the pet's behavior to be constantly monitored, ensuring the pet's safety. Furthermore, the behavior monitoring unit can also use the pet behavior data as data for predicting where the pet is likely to be hiding. For example, if the pet tends to frequently hide in a specific place, that place can be predicted with priority.

[0125] The pet search system can further include an emotion estimation unit that estimates the emotion of the pet. The emotion estimation unit analyzes the facial expressions and behavior of the pet to estimate the emotion of the pet. For example, if the pet is relaxed, it can suggest a normal search method. Also, if the pet is stressed, it can suggest a method that prioritizes searching quiet places. This makes it easier to find the pet by suggesting a search method that suits the pet's emotion. Furthermore, the emotion estimation unit can also use the pet's emotion data as data for predicting places where the pet is likely to hide. For example, if the pet is feeling anxious, it can predict places where it is likely to hide first.

[0126] The pet search system may further include a location tracking unit that tracks the location information of the pet. The location tracking unit collects location information from a GPS device attached to the pet's collar and tracks the pet's current location in real time. For example, if the pet leaves a specific area, a notification can be sent to the user. The location tracking unit can also record the pet's movement history and analyze the pet's behavioral patterns. This allows the pet's location information to be constantly known, ensuring the pet's safety. Furthermore, the location tracking unit can also use the pet's location information as data for predicting where the pet is likely to be hiding. For example, if the pet tends to frequently hide in a specific area, that area can be given priority in predictions.

[0127] The pet search system can further include a health advice unit that suggests a search method that takes the pet's health condition into consideration. The health advice unit analyzes the pet's health condition and suggests a search method that corresponds to the pet's health condition. For example, if the pet's body temperature and heart rate are normal, it suggests a normal search method. Also, if the pet is not feeling well, it can suggest a method that prioritizes searching in quiet places. This makes it easier to find the pet by suggesting a search method that corresponds to the pet's health condition. Furthermore, the health advice unit can also use data to predict where the pet is likely to be hiding, based on the pet's health condition. For example, if the pet is not feeling well, it can predict that quiet places will be prioritized.

[0128] The pet search system may further include a behavior analysis unit that analyzes the behavioral patterns of a pet. The behavior analysis unit collects behavioral data of the pet and analyzes the behavioral patterns of the pet. For example, if a pet tends to hide in a specific place at a specific time of day, it can predict that place with priority. The behavior analysis unit can also use the data to predict where the pet is likely to hide based on the pet's behavioral patterns. This makes it easier to find the pet by analyzing the pet's behavioral patterns. Furthermore, the behavior analysis unit can also estimate the pet's health condition and emotions based on the pet's behavioral data. For example, if a pet is performing a specific behavior, it can estimate the pet's health condition and emotions from that behavior.

[0129] The pet search system may further include an emotion adjustment unit that estimates the pet's emotion and adjusts the search method based on the estimated emotion. The emotion adjustment unit analyzes the pet's facial expressions and behavior to estimate the pet's emotion. For example, if the pet is relaxed, it may suggest a normal search method. Also, if the pet is feeling stressed, it may suggest a method that prioritizes searching quiet places. This makes it easier to find the pet by suggesting a search method that suits the pet's emotion. Furthermore, the emotion adjustment unit may also use the pet's emotion data as data for predicting places where the pet is likely to hide. For example, if the pet is feeling anxious, it may predict places that are easy to hide.

[0130] The pet search system may further include an anomaly detection unit that monitors the pet's behavior and detects abnormalities. The anomaly detection unit monitors the pet's behavior data in real time and detects abnormal behavior. For example, if the pet is behaving differently than usual, a notification can be sent to the user. The anomaly detection unit can also use the pet's behavior data as data to predict where the pet is likely to be hiding. This allows the pet's safety to be ensured by constantly monitoring the pet's behavior. Furthermore, the anomaly detection unit can also estimate the pet's health condition and emotions based on the pet's behavior data. For example, if the pet is behaving in a specific manner, the pet's health condition and emotions can be estimated from that behavior.

[0131] The pet search system may further include an emotion route adjustment unit that estimates the pet's emotion and adjusts the search route based on the estimated emotion. The emotion route adjustment unit analyzes the pet's facial expressions and behavior to estimate the pet's emotion. For example, if the pet is relaxed, it may suggest a normal search route. Also, if the pet is stressed, it may suggest a route that prioritizes searching quiet places. This makes it easier to find the pet by suggesting a search route according to the pet's emotion. Furthermore, the emotion route adjustment unit may also use the pet's emotion data as data for predicting places where the pet is likely to hide. For example, if the pet is feeling anxious, it may prioritize predicting places where it is likely to hide.

[0132] The pet search system may further include an action suggestion unit that analyzes the behavioral patterns of the pet and suggests a search method based on the behavioral patterns of the pet. The action suggestion unit collects pet behavior data and analyzes the pet's behavioral patterns. For example, if the pet tends to hide in a specific place at a specific time of day, it can predict that place with priority. The action suggestion unit can also use the data to predict where the pet is likely to be hiding based on the pet's behavioral patterns. This makes it easier to find the pet by analyzing the pet's behavioral patterns. Furthermore, the action suggestion unit can also estimate the pet's health condition and emotions based on the pet's behavioral data. For example, if the pet is performing a specific behavior, it can estimate the pet's health condition and emotions from that behavior.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The registration unit registers the pet's facial information. This information includes photos, videos, and 3D models. The registration unit records the pet's facial contours, eye position, nose shape, and other details, and can register multiple photos taken under different lighting conditions and angles. Step 2: The collection unit collects the pet's behavioral patterns and past data. The behavioral patterns include movement history, eating patterns, and frequency of play. The collection unit records the pet's movement history in detail and links it to the behavioral patterns. It can also collect data taking into account the pet's health condition and stress level. Step 3: The prediction unit uses the generation AI to predict likely hiding places based on the data collected by the collection unit. Likely hiding places are identified based on data on past hiding places and the results of analyzing behavioral patterns. The prediction unit prioritizes data on places where the pet has previously hidden, and can also predict hiding places taking into account the pet's current health condition and stress level. Step 4: The providing unit provides the location predicted by the prediction unit to the user. Methods of providing the location include a smartphone app, notification, map display, etc. The providing unit proposes the optimal search route taking into account the user's current location and travel route, and can also adjust the method of providing the location by estimating the user's emotions. Step 5: The authentication unit analyzes the video captured by the smartphone camera in real time and recognizes the pet's face. The authentication unit analyzes the pet's facial features from the video captured by the smartphone camera and compares them with registered data. It is also possible to improve authentication accuracy by taking into account changes in the pet's movements and facial expressions.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] 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.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 registration unit for registering face information of a pet; a collection unit that collects behavior patterns and past data of pets based on the face information registered by the registration unit; a prediction unit that predicts a possible hiding location based on the data collected by the collection unit; a providing unit that provides the location predicted by the prediction unit to a user; An authentication unit that searches for the location provided by the provision unit and performs face authentication using a smartphone camera. A system characterized by:

2. The registration unit Record your pet's facial features specifically 2. The system of claim 1.

3. The collecting unit Collecting pet behavior patterns and past data 2. The system of claim 1.

4. The prediction unit Predict possible hiding places based on your pet's behavioral patterns and past data 2. The system of claim 1.

5. The providing unit Providing users with predicted locations 2. The system of claim 1.

6. The authentication unit Analyzes images taken with a smartphone camera in real time and recognizes pet faces 2. The system of claim 1.

7. The registration unit Analyzes the user's emotions and adjusts the timing of registering the pet's facial information based on the analyzed user emotions.

2. The system of claim 1.

8. The registration unit When registering your pet's face information, register multiple photos taken under different lighting conditions and angles.

2. The system of claim 1.

Images