system

Abstract

The system according to the embodiment aims to effectively alleviate the lack of exercise in pet cats. [Solution] A system according to an embodiment includes a detection unit, an analysis unit, and an operation unit. The detection unit detects the movements of a pet cat. The analysis unit analyzes the data detected by the detection unit. The operation unit causes a mouse-shaped robot to move based on the data analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately provide effective means for relieving the lack of exercise in pet cats, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively alleviate the lack of exercise in pet cats. [Means for solving the problem]

[0006] The system according to the embodiment includes a detection unit, an analysis unit, and an operation unit. The detection unit detects the movements of the pet cat. The analysis unit analyzes the data detected by the detection unit. The operation unit causes the mouse-shaped robot to move based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively alleviate the lack of exercise in pet cats. [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 system according to an embodiment of the present invention uses an AI-based mouse-shaped robot to eliminate a lack of exercise for pet cats. This system detects the movements of pet cats using sensors, and then uses AI to analyze the data, causing the mouse-shaped robot to automatically perform movements that attract the cat's attention. For example, the mouse-shaped robot can make sudden changes in direction and speed, thereby increasing the amount of exercise the pet cat gets. The mouse-shaped robot is also designed to incorporate random movements so that the pet cat does not get bored. This eliminates the lack of exercise for pet cats and helps maintain their health. This system eliminates the lack of exercise for pet cats and helps maintain their health. For example, if a pet cat becomes interested in the movements of the mouse-shaped robot and chases it, it will increase its amount of exercise. This helps maintain the health of pet cats and prevent health problems caused by lack of exercise.

[0029] The motion elimination system according to the embodiment includes a detection unit, an analysis unit, and an operation unit. The detection unit detects the movements of a domestic cat. Examples of domestic cat movements include, but are not limited to, walking, jumping, and playing. The detection unit detects the movements of the domestic cat using, for example, an infrared sensor or a camera. The detection unit can also collect data on the patterns, speed, and direction of the domestic cat's movements. For example, the detection unit uses a sensor to detect information such as the direction and speed at which the domestic cat is moving. The analysis unit analyzes the data detected by the detection unit. The analysis unit can, for example, analyze the patterns of the domestic cat's movements using AI to predict movements that the domestic cat will be interested in. For example, if the domestic cat often makes sudden changes of direction, the analysis unit can be programmed to cause the mouse-shaped robot to also make sudden changes of direction. The analysis unit can also learn the patterns of the domestic cat's movements and predict movements that the domestic cat will be interested in. The operation unit causes the mouse-shaped robot to move based on the data analyzed by the analysis unit. The movement unit attracts the interest of a pet cat, for example, by making the mouse-shaped robot suddenly change direction or change speed. The movement unit can also incorporate random movements to prevent the pet cat from getting bored. For example, the movement unit includes a random movement unit that incorporates random movements. In this way, the exercise relief system according to the embodiment can alleviate the lack of exercise in pet cats and help them maintain their health.

[0030] The motion unit includes a random motion unit that incorporates random motion. The random motion unit attracts the interest of pet cats by, for example, making the mouse-shaped robot suddenly change direction or change speed. The random motion unit can also incorporate random motion so that pet cats do not get bored. For example, the random motion unit can randomly set the range, frequency, and change of motion. In this way, by incorporating random motion, pet cats can continue exercising without getting bored.

[0031] The operating unit includes an interest attracting unit that performs a movement that attracts the interest of the pet cat. The interest attracting unit attracts the interest of the pet cat, for example, by making the mouse-shaped robot suddenly change direction or change speed. The interest attracting unit can also perform a movement that attracts the interest of the pet cat. For example, the interest attracting unit can adjust the speed, direction, pattern, etc. of the movement. This makes it possible to increase the amount of exercise the pet cat gets by performing a movement that attracts the interest of the pet cat.

[0032] The detection unit can detect the movements of a pet cat using an infrared sensor or a camera. The infrared sensor has performance such as detection range, accuracy, and reaction speed. The camera has performance such as resolution, frame rate, and viewing angle. The detection unit can accurately detect the movements of a pet cat using an infrared sensor or a camera. For example, the infrared sensor detects the movements of a pet cat within a detection range and collects data. The camera captures the movements of a pet cat at high resolution and collects data. As a result, the movements of a pet cat can be accurately detected by using an infrared sensor or a camera.

[0033] The analysis unit learns the movement patterns of the cat and can predict movements that interest it. The analysis unit learns the movement patterns of the cat, for example, using AI. For example, the analysis unit learns movement patterns based on the frequency of the cat's movements, how they change, and a learning algorithm. The analysis unit can also predict movements that interest the cat. For example, if the cat often makes sudden changes of direction, the analysis unit can be programmed to make sudden changes of direction as well. In this way, by learning the movement patterns of the cat, the mouse robot can perform more effective operations.

[0034] The detection unit can analyze the cat's past movement history and select the optimal detection method. The detection unit can adjust the detection timing based on, for example, time periods when the cat frequently moved in the past. The detection unit can also analyze the cat's past movement patterns and determine the optimal sensor placement. The detection unit can also adjust the detection sensitivity based on the cat's past movement speed. This makes it possible to select a more effective detection method by analyzing the cat's past movement history.

[0035] The detection unit can filter the motion based on the physical condition and activity level of the pet cat when detecting the motion. For example, if the pet cat is tired, the detection unit temporarily stops detecting the motion. Furthermore, if the pet cat is active, the detection unit can strengthen the detection of the motion. Furthermore, if the pet cat is sick, the detection unit can set the frequency of the detection of the motion to a low level. In this way, filtering can be performed according to the physical condition and activity level of the pet cat, allowing for more appropriate operation.

[0036] The detection unit can select the optimal detection means depending on the speed and direction of the cat's movement when detecting. For example, if the cat is moving at high speed, the detection unit can use a high-speed camera to detect it. If the cat is moving slowly, the detection unit can use a low-speed camera to detect it. If the cat is moving in a specific direction, the detection unit can use a sensor specialized for that direction to detect it. This allows for more accurate operation by selecting the optimal detection means depending on the speed and direction of the cat's movement.

[0037] During detection, the detection unit can prioritize detecting highly relevant movements by taking into account the location information of the pet cat. For example, if the pet cat is in the living room, the detection unit can prioritize detecting movements within the living room. Also, if the pet cat is in the bedroom, the detection unit can prioritize detecting movements within the bedroom. Also, if the pet cat is outside, the detection unit can prioritize detecting movements outside. In this way, by taking into account the location information of the pet cat, more relevant movements can be detected.

[0038] Upon detection, the detection unit can analyze the social media activity of the pet cat and detect related movements. For example, the detection unit can prioritize detection of movements at locations where the pet cat has checked in on social media. The detection unit can also analyze the content of posts made by the pet cat on social media to detect related movements. The detection unit can also detect related movements by referring to the activities of the pet cat's friends on social media. In this way, by analyzing the social media activity of the pet cat, more related movements can be detected.

[0039] The detection unit can customize the detection method by reflecting the cat's past feedback during detection. For example, the detection unit can prioritize detection of movements that the cat has liked in the past. The detection unit can also avoid detecting movements that the cat has avoided in the past. The detection unit can also select the optimal detection method based on the cat's past feedback. In this way, by reflecting the cat's past feedback, a more effective detection method can be selected.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the movement. For example, the analysis unit performs a detailed analysis for an important movement. Furthermore, the analysis unit can perform a concise analysis for an unimportant movement. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the movement. This allows for more effective analysis by adjusting the level of detail of the analysis according to the importance of the movement.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of movement. For example, in the case of a sudden movement, the analysis unit can apply an analysis algorithm specialized for sudden movements. In addition, in the case of a slow movement, the analysis unit can apply an analysis algorithm specialized for slow movements. In addition, in the case of movement in a specific direction, the analysis unit can apply an analysis algorithm specialized for that direction. In this way, by applying different analysis algorithms depending on the category of movement, more accurate analysis can be performed.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet cat. For example, the analysis unit improves the accuracy of the current analysis based on the past analysis results of the pet cat. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results of the pet cat. The analysis unit can also optimize the analysis parameters based on the past analysis results of the pet cat. In this way, the accuracy of the current analysis can be improved by referring to the past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of the movement. For example, the analysis unit prioritizes analysis of recent movements. The analysis unit can also postpone analysis of past movements. The analysis unit can also adjust the priority of analysis in stages depending on the time of occurrence of the movement. This allows for more effective analysis by determining the priority of analysis depending on the time of occurrence of the movement.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the movements. For example, the analysis unit prioritizes analysis of important movements. The analysis unit can also postpone analysis of unimportant movements. The analysis unit can also adjust the order of analysis in stages according to the relevance of the movements. This allows for more effective analysis by adjusting the order of analysis according to the relevance of the movements.

[0045] During analysis, the analysis unit can adjust the analysis method according to the age and health condition of the cat. For example, the analysis unit performs a detailed analysis for a young cat. The analysis unit can also perform a simple analysis for an elderly cat. The analysis unit can also perform a detailed analysis for a cat in good health. This allows for a more appropriate analysis to be performed by adjusting the analysis method according to the age and health condition of the cat.

[0046] When operating, the operation unit can analyze the past operation history of the domestic cat and select the optimal operation method. For example, the operation unit can prioritize the operation of the domestic cat that has been preferred in the past. The operation unit can also prevent the domestic cat from performing an operation that has been avoided in the past. The operation unit can also select the optimal operation method based on the past operation history of the domestic cat. In this way, by analyzing the past operation history, a more effective operation method can be selected.

[0047] The operating unit can customize the operating means based on the current activity level of the domestic cat during operation. For example, the operating unit can increase the frequency of the operation when the domestic cat is active. Furthermore, the operating unit can decrease the frequency of the operation when the domestic cat is tired. Furthermore, the operating unit can gradually adjust the operating means according to the activity level of the domestic cat. In this way, by customizing the operating means according to the activity level of the domestic cat, more appropriate operations can be performed.

[0048] The movement unit can improve the movement method by reflecting the feedback from the cat during the movement. For example, the movement unit can repeatedly perform a movement that the cat likes. The movement unit can also prevent the movement from being performed by the cat that the cat avoids. The movement unit can also improve the movement method in stages based on the feedback from the cat. In this way, by reflecting the feedback from the cat, a more effective movement method can be selected.

[0049] When operating, the operating unit can select the optimal operating method by taking into account the location information of the pet cat. For example, if the pet cat is in the living room, the operating unit can optimize the movements within the living room. Also, if the pet cat is in the bedroom, the operating unit can optimize the movements within the bedroom. Also, if the pet cat is outside, the operating unit can optimize the movements outside. In this way, by taking into account the location information of the pet cat, more appropriate operations can be performed.

[0050] During operation, the operation unit can analyze the social media activity of the cat and suggest a means of movement. For example, the operation unit can suggest an action based on a location where the cat has checked in on social media. The operation unit can also analyze the content of the cat's posts on social media and suggest a related action. The operation unit can also suggest a related action based on the activity of the cat's friends on social media. In this way, more relevant actions can be performed by analyzing the cat's social media activity.

[0051] The movement unit can customize the movement method by reflecting the cat's past feedback when moving. For example, the movement unit can prioritize movements that the cat has liked in the past. The movement unit can also prevent the cat from making movements that the cat has avoided in the past. The movement unit can also select the optimal movement method based on the cat's past feedback. In this way, by reflecting the cat's past feedback, a more effective movement method can be selected.

[0052] The random action unit can select an optimal random action pattern by analyzing the past action history of the domestic cat during random action. For example, the random action unit prioritizes random actions that the domestic cat has liked in the past. The random action unit can also prevent the domestic cat from performing random actions that the domestic cat has avoided in the past. The random action unit can also select an optimal random action pattern based on the past action history of the domestic cat. In this way, by analyzing the past action history, a more effective random action pattern can be selected.

[0053] The random action unit can customize the means of random action based on the current activity level of the domestic cat during random action. For example, if the domestic cat is active, the random action unit can increase the frequency of random action. Also, if the domestic cat is tired, the random action unit can decrease the frequency of random action. Also, the random action unit can gradually adjust the means of random action according to the activity level of the domestic cat. In this way, by customizing the means of random action according to the activity level of the domestic cat, more appropriate action can be performed.

[0054] The random action unit can select the optimal random action method by taking into account the location information of the pet cat during random action. For example, if the pet cat is in the living room, the random action unit can optimize the random action in the living room. Also, if the pet cat is in the bedroom, the random action unit can optimize the random action in the bedroom. Also, if the pet cat is outside, the random action unit can optimize the random action outside. In this way, by taking into account the location information of the pet cat, more appropriate random action can be performed.

[0055] The random action unit may analyze the social media activity of the cat to suggest a means for random action during the random action. For example, the random action unit may suggest a random action based on a location where the cat has checked in on social media. The random action unit may also analyze the content of the cat's social media posts to suggest a related random action. The random action unit may also suggest a related random action based on the activity of the cat's friends on social media. In this way, more relevant random actions can be performed by analyzing the cat's social media activity.

[0056] When performing an interest-attracting action, the interest-attracting unit can analyze the past behavior history of the domestic cat and select an optimal interest-attracting action pattern. For example, the interest-attracting unit prioritizes performing interest-attracting actions that the domestic cat has liked in the past. The interest-attracting unit can also prevent the domestic cat from performing interest-attracting actions that the domestic cat has avoided in the past. The interest-attracting unit can also select an optimal interest-attracting action pattern based on the past behavior history of the domestic cat. In this way, by analyzing the past behavior history, a more effective interest-attracting action pattern can be selected.

[0057] The interest attracting unit can customize the means of the interest attracting operation based on the current activity level of the domestic cat when performing the interest attracting operation. For example, the interest attracting unit can increase the frequency of the interest attracting operation when the domestic cat is active. Furthermore, the interest attracting unit can decrease the frequency of the interest attracting operation when the domestic cat is tired. Furthermore, the interest attracting unit can gradually adjust the means of the interest attracting operation according to the activity level of the domestic cat. In this way, by customizing the means of the interest attracting operation according to the activity level of the domestic cat, more appropriate operations can be performed.

[0058] The interest attracting unit can select the optimal interest attracting operation method by taking into account location information of the pet cat when performing the interest attracting operation. For example, if the pet cat is in the living room, the interest attracting unit can optimize the interest attracting operation in the living room. Furthermore, if the pet cat is in the bedroom, the interest attracting unit can optimize the interest attracting operation in the bedroom. Furthermore, if the pet cat is outside, the interest attracting unit can optimize the interest attracting operation outside. In this way, by taking into account location information of the pet cat, a more appropriate interest attracting operation can be performed.

[0059] The interest generation unit can analyze the social media activity of the pet cat to suggest a means for performing the interest generation action when performing the interest generation action. For example, the interest generation unit can suggest an interest generation action based on a location where the pet cat has checked in on social media. The interest generation unit can also analyze the content of the pet cat's social media posts to suggest a related interest generation action. The interest generation unit can also suggest a related interest generation action by referring to the activity of the pet cat's friends on social media. In this way, by analyzing the social media activity of the pet cat, a more relevant interest generation action can be performed.

[0060] When using the sensor, the infrared sensor or camera can select optimal sensor settings by analyzing the cat's past behavior history. The infrared sensor or camera can adjust the sensitivity of the sensor based on, for example, the time periods when the cat frequently moved in the past. The infrared sensor or camera can also analyze the cat's past movement patterns to determine the optimal sensor placement. The infrared sensor or camera can also adjust the sensitivity of the sensor based on the cat's past movement speed. In this way, by analyzing the past behavior history, more effective sensor settings can be selected.

[0061] When the infrared sensor or camera is in use, the sensor settings can be customized based on the cat's current activity level. For example, the infrared sensor or camera can increase the sensitivity of the sensor when the cat is active. Alternatively, the infrared sensor or camera can decrease the sensitivity of the sensor when the cat is tired. Furthermore, the infrared sensor or camera can gradually adjust the sensor settings according to the cat's activity level. This allows the sensor settings to be customized according to the cat's activity level, resulting in more appropriate operation.

[0062] When using the sensor, the infrared sensor or camera can select the optimal sensor settings by taking into account the location information of the pet cat. For example, if the pet cat is in the living room, the infrared sensor or camera can prioritize detecting movement within the living room. Also, if the pet cat is in the bedroom, the infrared sensor or camera can prioritize detecting movement within the bedroom. Also, if the pet cat is outside, the infrared sensor or camera can prioritize detecting movement outside. This allows for more appropriate operation by taking into account the location information of the pet cat.

[0063] When the infrared sensor or camera is in use, it can analyze the cat's social media activity to suggest sensor settings. For example, the infrared sensor or camera can suggest sensor settings based on the location where the cat has checked in on social media. The infrared sensor or camera can also analyze the content of the cat's social media posts to suggest relevant sensor settings. The infrared sensor or camera can also suggest relevant sensor settings based on the activity of the cat's friends on social media. This allows for more relevant behavior to be performed by analyzing the cat's social media activity.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The analysis unit can take into account the cat's physical condition data when analyzing the cat's movement patterns. For example, based on the results of the cat's health check, the analysis unit can predict the cat's movements according to its physical condition. Furthermore, the analysis unit can recommend a more relaxed movement if the cat is not feeling well. Furthermore, the analysis unit can recommend a more active movement if the cat is in good physical condition. This makes it possible to provide appropriate exercise according to the cat's physical condition.

[0066] The movement unit can have a function to imitate the movements of a pet cat. For example, if the pet cat jumps, the robot mouse can also jump. If the pet cat runs in a specific direction, the robot mouse can also run in the same direction. Furthermore, if the pet cat repeats a specific movement, the robot mouse can also imitate that movement. This allows the pet cat to follow its movements and continue exercising with more interest.

[0067] The detector can detect not only the movements of the pet cat but also data on the surrounding environment. For example, it can detect the temperature and humidity of the room to provide a comfortable environment for the pet cat to exercise. The detector can also detect the brightness of the room and set appropriate lighting conditions. Furthermore, the detector can detect the noise level of the room and provide a quiet environment. This allows for a comfortable environment for the pet cat to exercise.

[0068] When analyzing the movement patterns of a pet cat, the analysis unit can refer to data on other pet cats. For example, the analysis unit can predict the movement patterns of a pet cat based on movement data of cats of the same species. Also, based on data on other pet cats, the analysis unit can predict movements that the pet cat will be interested in. Furthermore, based on data on other pet cats, the analysis unit can suggest optimal movements to help the pet cat overcome a lack of exercise. This makes it possible to provide more effective exercise.

[0069] The movement unit can predict the movements of the cat and perform movements based on the prediction. For example, it can predict the direction the cat will move next and perform movements in that direction in advance. It can also predict what movement the cat will make next and perform movements in accordance with that movement. It can also predict how fast the cat will move next and perform movements in accordance with that speed. This makes it possible to provide more effective exercise by anticipating the movements of the cat.

[0070] The detection unit can detect not only the movements of a pet cat, but also its meows. For example, if a pet cat meows, the detection unit can analyze the pattern of the meows to estimate the cat's emotions. It can also detect the frequency and volume of the cat's meows to understand the cat's condition. Furthermore, it can predict the movements that the cat will be interested in based on the cat's meows. This makes it possible to provide more effective exercise by utilizing the cat's meows.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The detection unit detects the movement of the cat. Examples of the cat's movements include, but are not limited to, walking, jumping, and playing. The detection unit uses an infrared sensor and a camera to detect the cat's movements and collect data such as the movement pattern, speed, and direction. For example, the sensor detects information such as the direction in which the cat is moving and how fast it is moving. Step 2: The analysis unit analyzes the data detected by the detection unit. The analysis unit uses AI to analyze the cat's movement patterns and predict movements that will interest the cat. For example, if the cat often makes sudden changes of direction, the mouse robot is programmed to make sudden changes of direction as well. The analysis unit can also learn the cat's movement patterns and predict movements that will interest the cat. Step 3: The operating unit causes the mouse-shaped robot to move based on the data analyzed by the analyzing unit. The operating unit attracts the interest of the pet cat by making the mouse-shaped robot suddenly change direction or speed. The operating unit can also incorporate random movements to prevent the pet cat from getting bored. For example, the operating unit may include a random operating unit that incorporates random movements.

[0073] (Example 2) A system according to an embodiment of the present invention uses an AI-based mouse-shaped robot to eliminate a lack of exercise for pet cats. This system detects the movements of pet cats using sensors, and then uses AI to analyze the data, causing the mouse-shaped robot to automatically perform movements that attract the cat's attention. For example, the mouse-shaped robot can make sudden changes in direction and speed, thereby increasing the amount of exercise the pet cat gets. The mouse-shaped robot is also designed to incorporate random movements so that the pet cat does not get bored. This eliminates the lack of exercise for pet cats and helps maintain their health. This system eliminates the lack of exercise for pet cats and helps maintain their health. For example, if a pet cat becomes interested in the movements of the mouse-shaped robot and chases it, it will increase its amount of exercise. This helps maintain the health of pet cats and prevent health problems caused by lack of exercise.

[0074] The motion elimination system according to the embodiment includes a detection unit, an analysis unit, and an operation unit. The detection unit detects the movements of a domestic cat. Examples of domestic cat movements include, but are not limited to, walking, jumping, and playing. The detection unit detects the movements of the domestic cat using, for example, an infrared sensor or a camera. The detection unit can also collect data on the patterns, speed, and direction of the domestic cat's movements. For example, the detection unit uses a sensor to detect information such as the direction and speed at which the domestic cat is moving. The analysis unit analyzes the data detected by the detection unit. The analysis unit can, for example, analyze the patterns of the domestic cat's movements using AI to predict movements that the domestic cat will be interested in. For example, if the domestic cat often makes sudden changes of direction, the analysis unit can be programmed to cause the mouse-shaped robot to also make sudden changes of direction. The analysis unit can also learn the patterns of the domestic cat's movements and predict movements that the domestic cat will be interested in. The operation unit causes the mouse-shaped robot to move based on the data analyzed by the analysis unit. The movement unit attracts the interest of a pet cat, for example, by making the mouse-shaped robot suddenly change direction or change speed. The movement unit can also incorporate random movements to prevent the pet cat from getting bored. For example, the movement unit includes a random movement unit that incorporates random movements. In this way, the exercise relief system according to the embodiment can alleviate the lack of exercise in pet cats and help them maintain their health.

[0075] The motion unit includes a random motion unit that incorporates random motion. The random motion unit attracts the interest of pet cats by, for example, making the mouse-shaped robot suddenly change direction or change speed. The random motion unit can also incorporate random motion so that pet cats do not get bored. For example, the random motion unit can randomly set the range, frequency, and change of motion. In this way, by incorporating random motion, pet cats can continue exercising without getting bored.

[0076] The operating unit includes an interest attracting unit that performs a movement that attracts the interest of the pet cat. The interest attracting unit attracts the interest of the pet cat, for example, by making the mouse-shaped robot suddenly change direction or change speed. The interest attracting unit can also perform a movement that attracts the interest of the pet cat. For example, the interest attracting unit can adjust the speed, direction, pattern, etc. of the movement. This makes it possible to increase the amount of exercise the pet cat gets by performing a movement that attracts the interest of the pet cat.

[0077] The detection unit can detect the movements of a pet cat using an infrared sensor or a camera. The infrared sensor has performance such as detection range, accuracy, and reaction speed. The camera has performance such as resolution, frame rate, and viewing angle. The detection unit can accurately detect the movements of a pet cat using an infrared sensor or a camera. For example, the infrared sensor detects the movements of a pet cat within a detection range and collects data. The camera captures the movements of a pet cat at high resolution and collects data. As a result, the movements of a pet cat can be accurately detected by using an infrared sensor or a camera.

[0078] The analysis unit learns the movement patterns of the cat and can predict movements that interest it. The analysis unit learns the movement patterns of the cat, for example, using AI. For example, the analysis unit learns movement patterns based on the frequency of the cat's movements, how they change, and a learning algorithm. The analysis unit can also predict movements that interest the cat. For example, if the cat often makes sudden changes of direction, the analysis unit can be programmed to make sudden changes of direction as well. In this way, by learning the movement patterns of the cat, the mouse robot can perform more effective operations.

[0079] The detection unit can estimate the emotion of the cat and adjust the accuracy of movement detection based on the estimated emotion of the cat. For example, when the cat is excited, the detection unit can increase the detection accuracy to detect even small movements. Furthermore, when the cat is relaxed, the detection unit can decrease the detection accuracy to detect only large movements. Furthermore, when the cat is stressed, the detection unit can set the detection accuracy to a medium level to avoid excessive detection. This allows for more appropriate operation by adjusting the detection accuracy according to the emotion of the cat. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0080] The detection unit can analyze the cat's past movement history and select the optimal detection method. The detection unit can adjust the detection timing based on, for example, time periods when the cat frequently moved in the past. The detection unit can also analyze the cat's past movement patterns and determine the optimal sensor placement. The detection unit can also adjust the detection sensitivity based on the cat's past movement speed. This makes it possible to select a more effective detection method by analyzing the cat's past movement history.

[0081] The detection unit can filter the motion based on the physical condition and activity level of the pet cat when detecting the motion. For example, if the pet cat is tired, the detection unit temporarily stops detecting the motion. Furthermore, if the pet cat is active, the detection unit can strengthen the detection of the motion. Furthermore, if the pet cat is sick, the detection unit can set the frequency of the detection of the motion to a low level. In this way, filtering can be performed according to the physical condition and activity level of the pet cat, allowing for more appropriate operation.

[0082] The detection unit can select the optimal detection means depending on the speed and direction of the cat's movement when detecting. For example, if the cat is moving at high speed, the detection unit can use a high-speed camera to detect it. If the cat is moving slowly, the detection unit can use a low-speed camera to detect it. If the cat is moving in a specific direction, the detection unit can use a sensor specialized for that direction to detect it. This allows for more accurate operation by selecting the optimal detection means depending on the speed and direction of the cat's movement.

[0083] The detection unit can estimate the emotion of the cat and determine the priority of the movements to be detected based on the estimated emotion of the cat. For example, if the cat is excited, the detection unit can preferentially detect sudden movements. Furthermore, if the cat is relaxed, the detection unit can preferentially detect slow movements. Furthermore, if the cat is stressed, the detection unit can preferentially detect specific movements. This allows for more effective movements to be performed by prioritizing movements according to the emotion of the cat. Emotion estimation is realized using an emotion estimation function, for example, using 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.

[0084] During detection, the detection unit can prioritize detecting highly relevant movements by taking into account the location information of the pet cat. For example, if the pet cat is in the living room, the detection unit can prioritize detecting movements within the living room. Also, if the pet cat is in the bedroom, the detection unit can prioritize detecting movements within the bedroom. Also, if the pet cat is outside, the detection unit can prioritize detecting movements outside. In this way, by taking into account the location information of the pet cat, more relevant movements can be detected.

[0085] Upon detection, the detection unit can analyze the social media activity of the pet cat and detect related movements. For example, the detection unit can prioritize detection of movements at locations where the pet cat has checked in on social media. The detection unit can also analyze the content of posts made by the pet cat on social media to detect related movements. The detection unit can also detect related movements by referring to the activities of the pet cat's friends on social media. In this way, by analyzing the social media activity of the pet cat, more related movements can be detected.

[0086] The detection unit can customize the detection method by reflecting the cat's past feedback during detection. For example, the detection unit can prioritize detection of movements that the cat has liked in the past. The detection unit can also avoid detecting movements that the cat has avoided in the past. The detection unit can also select the optimal detection method based on the cat's past feedback. In this way, by reflecting the cat's past feedback, a more effective detection method can be selected.

[0087] The analysis unit can estimate the emotion of the cat and adjust the way the analysis is expressed based on the estimated emotion of the cat. For example, if the cat is excited, the analysis unit can provide a detailed analysis result. Furthermore, if the cat is relaxed, the analysis unit can provide a concise analysis result. Furthermore, if the cat is stressed, the analysis unit can provide an analysis result for stress reduction. In this way, by adjusting the way the analysis is expressed according to the emotion of the cat, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0088] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the movement. For example, the analysis unit performs a detailed analysis for an important movement. Furthermore, the analysis unit can perform a concise analysis for an unimportant movement. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance of the movement. This allows for more effective analysis by adjusting the level of detail of the analysis according to the importance of the movement.

[0089] During analysis, the analysis unit can apply different analysis algorithms depending on the category of movement. For example, in the case of a sudden movement, the analysis unit can apply an analysis algorithm specialized for sudden movements. In addition, in the case of a slow movement, the analysis unit can apply an analysis algorithm specialized for slow movements. In addition, in the case of movement in a specific direction, the analysis unit can apply an analysis algorithm specialized for that direction. In this way, by applying different analysis algorithms depending on the category of movement, more accurate analysis can be performed.

[0090] During analysis, the analysis unit can improve the accuracy of the analysis by referring to past analysis results of the pet cat. For example, the analysis unit improves the accuracy of the current analysis based on the past analysis results of the pet cat. The analysis unit can also adjust the analysis algorithm by referring to the past analysis results of the pet cat. The analysis unit can also optimize the analysis parameters based on the past analysis results of the pet cat. In this way, the accuracy of the current analysis can be improved by referring to the past analysis results.

[0091] The analysis unit can estimate the emotion of the cat and adjust the length of the analysis based on the estimated emotion of the cat. For example, if the cat is excited, the analysis unit can perform a detailed analysis. If the cat is relaxed, the analysis unit can perform a brief analysis. If the cat is stressed, the analysis unit can perform an analysis to reduce stress. By adjusting the length of the analysis according to the emotion of the cat, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] During analysis, the analysis unit can determine the priority of analysis based on the time of occurrence of the movement. For example, the analysis unit prioritizes analysis of recent movements. The analysis unit can also postpone analysis of past movements. The analysis unit can also adjust the priority of analysis in stages depending on the time of occurrence of the movement. This allows for more effective analysis by determining the priority of analysis depending on the time of occurrence of the movement.

[0093] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the movements. For example, the analysis unit prioritizes analysis of important movements. The analysis unit can also postpone analysis of unimportant movements. The analysis unit can also adjust the order of analysis in stages according to the relevance of the movements. This allows for more effective analysis by adjusting the order of analysis according to the relevance of the movements.

[0094] During analysis, the analysis unit can adjust the analysis method according to the age and health condition of the cat. For example, the analysis unit performs a detailed analysis for a young cat. The analysis unit can also perform a simple analysis for an elderly cat. The analysis unit can also perform a detailed analysis for a cat in good health. This allows for a more appropriate analysis to be performed by adjusting the analysis method according to the age and health condition of the cat.

[0095] The movement unit can estimate the emotion of the domestic cat and adjust the method of movement based on the estimated emotion of the domestic cat. For example, if the domestic cat is excited, the movement unit can make a sudden movement. Furthermore, if the domestic cat is relaxed, the movement unit can make a slow movement. Furthermore, if the domestic cat is stressed, the movement unit can make a movement to reduce stress. In this way, by adjusting the method of movement according to the emotion of the domestic cat, more effective movement can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] When operating, the operation unit can analyze the past operation history of the domestic cat and select the optimal operation method. For example, the operation unit can prioritize the operation of the domestic cat that has been preferred in the past. The operation unit can also prevent the domestic cat from performing an operation that has been avoided in the past. The operation unit can also select the optimal operation method based on the past operation history of the domestic cat. In this way, by analyzing the past operation history, a more effective operation method can be selected.

[0097] The operating unit can customize the operating means based on the current activity level of the domestic cat during operation. For example, the operating unit can increase the frequency of the operation when the domestic cat is active. Furthermore, the operating unit can decrease the frequency of the operation when the domestic cat is tired. Furthermore, the operating unit can gradually adjust the operating means according to the activity level of the domestic cat. In this way, by customizing the operating means according to the activity level of the domestic cat, more appropriate operations can be performed.

[0098] The movement unit can improve the movement method by reflecting the feedback from the cat during the movement. For example, the movement unit can repeatedly perform a movement that the cat likes. The movement unit can also prevent the movement from being performed by the cat that the cat avoids. The movement unit can also improve the movement method in stages based on the feedback from the cat. In this way, by reflecting the feedback from the cat, a more effective movement method can be selected.

[0099] The operation unit can estimate the emotion of the domestic cat and determine the priority of actions based on the estimated emotion of the domestic cat. For example, if the domestic cat is excited, the operation unit can prioritize sudden movements. Furthermore, if the domestic cat is relaxed, the operation unit can prioritize slow movements. Furthermore, if the domestic cat is stressed, the operation unit can prioritize specific movements. In this way, by determining the priority of actions according to the emotion of the domestic cat, more effective actions can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0100] When operating, the operating unit can select the optimal operating method by taking into account the location information of the pet cat. For example, if the pet cat is in the living room, the operating unit can optimize the movements within the living room. Also, if the pet cat is in the bedroom, the operating unit can optimize the movements within the bedroom. Also, if the pet cat is outside, the operating unit can optimize the movements outside. In this way, by taking into account the location information of the pet cat, more appropriate operations can be performed.

[0101] During operation, the operation unit can analyze the social media activity of the cat and suggest a means of movement. For example, the operation unit can suggest an action based on a location where the cat has checked in on social media. The operation unit can also analyze the content of the cat's posts on social media and suggest a related action. The operation unit can also suggest a related action based on the activity of the cat's friends on social media. In this way, more relevant actions can be performed by analyzing the cat's social media activity.

[0102] The movement unit can customize the movement method by reflecting the cat's past feedback when moving. For example, the movement unit can prioritize movements that the cat has liked in the past. The movement unit can also prevent the cat from making movements that the cat has avoided in the past. The movement unit can also select the optimal movement method based on the cat's past feedback. In this way, by reflecting the cat's past feedback, a more effective movement method can be selected.

[0103] The random action unit can estimate the emotion of the cat and adjust the random action pattern based on the estimated emotion of the cat. For example, if the cat is excited, the random action unit can incorporate many sudden changes in direction. If the cat is relaxed, the random action unit can incorporate many slow movements. If the cat is stressed, the random action unit can incorporate many movements to reduce stress. In this way, by adjusting the random action pattern according to the emotion of the cat, more effective actions can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0104] The random action unit can select an optimal random action pattern by analyzing the past action history of the domestic cat during random action. For example, the random action unit prioritizes random actions that the domestic cat has liked in the past. The random action unit can also prevent the domestic cat from performing random actions that the domestic cat has avoided in the past. The random action unit can also select an optimal random action pattern based on the past action history of the domestic cat. In this way, by analyzing the past action history, a more effective random action pattern can be selected.

[0105] The random action unit can customize the means of random action based on the current activity level of the domestic cat during random action. For example, if the domestic cat is active, the random action unit can increase the frequency of random action. Also, if the domestic cat is tired, the random action unit can decrease the frequency of random action. Also, the random action unit can gradually adjust the means of random action according to the activity level of the domestic cat. In this way, by customizing the means of random action according to the activity level of the domestic cat, more appropriate action can be performed.

[0106] The random action unit can estimate the emotion of the cat and determine the priority of random actions based on the estimated emotion of the cat. For example, if the cat is excited, the random action unit can prioritize sudden movements. Furthermore, if the cat is relaxed, the random action unit can prioritize slow movements. Furthermore, if the cat is stressed, the random action unit can prioritize specific movements. In this way, by determining the priority of random actions according to the emotion of the cat, more effective actions can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The random action unit can select the optimal random action method by taking into account the location information of the pet cat during random action. For example, if the pet cat is in the living room, the random action unit can optimize the random action in the living room. Also, if the pet cat is in the bedroom, the random action unit can optimize the random action in the bedroom. Also, if the pet cat is outside, the random action unit can optimize the random action outside. In this way, by taking into account the location information of the pet cat, more appropriate random action can be performed.

[0108] The random action unit may analyze the social media activity of the cat to suggest a means for random action during the random action. For example, the random action unit may suggest a random action based on a location where the cat has checked in on social media. The random action unit may also analyze the content of the cat's social media posts to suggest a related random action. The random action unit may also suggest a related random action based on the activity of the cat's friends on social media. In this way, more relevant random actions can be performed by analyzing the cat's social media activity.

[0109] The interest attracting unit can estimate the emotion of the domestic cat and adjust the pattern of the interest attracting behavior based on the estimated emotion of the domestic cat. For example, if the domestic cat is excited, the interest attracting unit can incorporate many sudden changes of direction. Also, if the domestic cat is relaxed, the interest attracting unit can incorporate many slow movements. Also, if the domestic cat is stressed, the interest attracting unit can incorporate many movements to reduce stress. In this way, by adjusting the pattern of the interest attracting behavior according to the emotion of the domestic cat, more effective behavior can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] When performing an interest-attracting action, the interest-attracting unit can analyze the past behavior history of the domestic cat and select an optimal interest-attracting action pattern. For example, the interest-attracting unit prioritizes performing interest-attracting actions that the domestic cat has liked in the past. The interest-attracting unit can also prevent the domestic cat from performing interest-attracting actions that the domestic cat has avoided in the past. The interest-attracting unit can also select an optimal interest-attracting action pattern based on the past behavior history of the domestic cat. In this way, by analyzing the past behavior history, a more effective interest-attracting action pattern can be selected.

[0111] The interest attracting unit can customize the means of the interest attracting operation based on the current activity level of the domestic cat when performing the interest attracting operation. For example, the interest attracting unit can increase the frequency of the interest attracting operation when the domestic cat is active. Furthermore, the interest attracting unit can decrease the frequency of the interest attracting operation when the domestic cat is tired. Furthermore, the interest attracting unit can gradually adjust the means of the interest attracting operation according to the activity level of the domestic cat. In this way, by customizing the means of the interest attracting operation according to the activity level of the domestic cat, more appropriate operations can be performed.

[0112] The interest attracting unit can estimate the emotion of the domestic cat and determine the priority of interest attracting actions based on the estimated emotion of the domestic cat. For example, if the domestic cat is excited, the interest attracting unit can prioritize sudden movements. Furthermore, if the domestic cat is relaxed, the interest attracting unit can prioritize slow movements. Furthermore, if the domestic cat is stressed, the interest attracting unit can prioritize specific movements. In this way, by determining the priority of interest attracting actions according to the emotion of the domestic cat, more effective actions can be performed. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0113] The interest attracting unit can select the optimal interest attracting operation method by taking into account location information of the pet cat when performing the interest attracting operation. For example, if the pet cat is in the living room, the interest attracting unit can optimize the interest attracting operation in the living room. Furthermore, if the pet cat is in the bedroom, the interest attracting unit can optimize the interest attracting operation in the bedroom. Furthermore, if the pet cat is outside, the interest attracting unit can optimize the interest attracting operation outside. In this way, by taking into account location information of the pet cat, a more appropriate interest attracting operation can be performed.

[0114] The interest generation unit can analyze the social media activity of the pet cat to suggest a means for performing the interest generation action when performing the interest generation action. For example, the interest generation unit can suggest an interest generation action based on a location where the pet cat has checked in on social media. The interest generation unit can also analyze the content of the pet cat's social media posts to suggest a related interest generation action. The interest generation unit can also suggest a related interest generation action by referring to the activity of the pet cat's friends on social media. In this way, by analyzing the social media activity of the pet cat, a more relevant interest generation action can be performed.

[0115] The infrared sensor or camera can estimate the cat's emotions and adjust the sensitivity of the sensor based on the estimated emotions. For example, if the cat is excited, the infrared sensor or camera can increase the sensitivity of the sensor to detect even small movements. If the cat is relaxed, the infrared sensor or camera can decrease the sensitivity of the sensor to detect only large movements. If the cat is stressed, the infrared sensor or camera can set the sensitivity of the sensor to a medium level to avoid excessive detection. This allows for more accurate operation by adjusting the sensitivity of the sensor according to the cat's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] When using the sensor, the infrared sensor or camera can select optimal sensor settings by analyzing the cat's past behavior history. The infrared sensor or camera can adjust the sensitivity of the sensor based on, for example, the time periods when the cat frequently moved in the past. The infrared sensor or camera can also analyze the cat's past movement patterns to determine the optimal sensor placement. The infrared sensor or camera can also adjust the sensitivity of the sensor based on the cat's past movement speed. In this way, by analyzing the past behavior history, more effective sensor settings can be selected.

[0117] When the infrared sensor or camera is in use, the sensor settings can be customized based on the cat's current activity level. For example, the infrared sensor or camera can increase the sensitivity of the sensor when the cat is active. Alternatively, the infrared sensor or camera can decrease the sensitivity of the sensor when the cat is tired. Furthermore, the infrared sensor or camera can gradually adjust the sensor settings according to the cat's activity level. This allows the sensor settings to be customized according to the cat's activity level, resulting in more appropriate operation.

[0118] The infrared sensor or camera can estimate the cat's emotions and determine the priority of sensors based on the estimated emotions of the cat. For example, if the cat is excited, the infrared sensor or camera can prioritize detecting sudden movements. Furthermore, if the cat is relaxed, the infrared sensor or camera can prioritize detecting slow movements. Furthermore, if the cat is stressed, the infrared sensor or camera can prioritize detecting specific movements. Thus, by determining the priority of sensors according to the cat's emotions, more effective operation can be performed. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] When using the sensor, the infrared sensor or camera can select the optimal sensor settings by taking into account the location information of the pet cat. For example, if the pet cat is in the living room, the infrared sensor or camera can prioritize detecting movement within the living room. Also, if the pet cat is in the bedroom, the infrared sensor or camera can prioritize detecting movement within the bedroom. Also, if the pet cat is outside, the infrared sensor or camera can prioritize detecting movement outside. This allows for more appropriate operation by taking into account the location information of the pet cat.

[0120] When the infrared sensor or camera is in use, it can analyze the cat's social media activity to suggest sensor settings. For example, the infrared sensor or camera can suggest sensor settings based on the location where the cat has checked in on social media. The infrared sensor or camera can also analyze the content of the cat's social media posts to suggest relevant sensor settings. The infrared sensor or camera can also suggest relevant sensor settings based on the activity of the cat's friends on social media. This allows for more relevant behavior to be performed by analyzing the cat's social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the detection unit, analysis unit, operation unit, and random operation 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 detection unit detects the movements of the pet cat using the camera 42 or an infrared sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the movement patterns of the pet cat using AI. The operation unit is realized, for example, by the control unit 46A of the smart device 14 and controls the movements of the mouse-shaped robot. The random operation unit is realized, for example, by the control unit 46A of the smart device 14 and introduces random movements. === Hard Collateral 1-2 === Each of the multiple elements including the above-described detection unit, analysis unit, operation unit, and random operation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the detection unit detects the movement of the pet cat using the camera 42 or an infrared sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the movement pattern of the pet cat using AI. The operation unit is realized, for example, by the control unit 46A of the smart glasses 214 and controls the movement of the mouse-shaped robot. The random operation unit is realized, for example, by the control unit 46A of the smart glasses 214 and introduces random movement. === Hard Collateral 1-3 === Each of the multiple elements including the above-described detection unit, analysis unit, operation unit, and random operation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the detection unit detects the movements of the pet cat using the camera 42 or an infrared sensor of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the movement patterns of the pet cat using AI. The operation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and controls the movements of the mouse-type robot. The random operation unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and introduces random movements. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned detection unit, analysis unit, operation unit, and random operation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the detection unit detects the movements of the pet cat using the camera 42 or an infrared sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the movement patterns of the pet cat using AI. The operation unit is realized, for example, by the control unit 46A of the robot 414, and controls the movements of the mouse-type robot. The random operation unit is realized, for example, by the control unit 46A of the robot 414, and introduces random movements.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The analysis unit can take into account the cat's physical condition data when analyzing the cat's movement patterns. For example, based on the results of the cat's health check, the analysis unit can predict the cat's movements according to its physical condition. Furthermore, the analysis unit can recommend a more relaxed movement if the cat is not feeling well. Furthermore, the analysis unit can recommend a more active movement if the cat is in good physical condition. This makes it possible to provide appropriate exercise according to the cat's physical condition.

[0123] The movement unit can have a function to imitate the movements of a pet cat. For example, if the pet cat jumps, the robot mouse can also jump. If the pet cat runs in a specific direction, the robot mouse can also run in the same direction. Furthermore, if the pet cat repeats a specific movement, the robot mouse can also imitate that movement. This allows the pet cat to follow its movements and continue exercising with more interest.

[0124] The movement unit can estimate the emotion of the cat and adjust the difficulty of the movement based on the estimated emotion. For example, if the cat is excited, the movement unit can perform a more difficult movement. If the cat is relaxed, the movement unit can perform an easy movement. Furthermore, if the cat is stressed, the movement unit can perform a movement to reduce the stress. This makes it possible to provide appropriate exercise according to the cat's emotion.

[0125] The detector can detect not only the movements of the pet cat but also data on the surrounding environment. For example, it can detect the temperature and humidity of the room to provide a comfortable environment for the pet cat to exercise. The detector can also detect the brightness of the room and set appropriate lighting conditions. Furthermore, the detector can detect the noise level of the room and provide a quiet environment. This allows for a comfortable environment for the pet cat to exercise.

[0126] When analyzing the movement patterns of a pet cat, the analysis unit can refer to data on other pet cats. For example, the analysis unit can predict the movement patterns of a pet cat based on movement data of cats of the same species. Also, based on data on other pet cats, the analysis unit can predict movements that the pet cat will be interested in. Furthermore, based on data on other pet cats, the analysis unit can suggest optimal movements to help the pet cat overcome a lack of exercise. This makes it possible to provide more effective exercise.

[0127] The detection unit can estimate the emotion of the cat and adjust the movement detection range based on the estimated emotion. For example, if the cat is excited, the detection range can be widened to detect detailed movements. If the cat is relaxed, the detection range can be narrowed to detect general movements. Furthermore, if the cat is stressed, the detection range can be set to a medium level to avoid excessive detection. This allows for appropriate detection according to the cat's emotion.

[0128] The movement unit can predict the movements of the cat and perform movements based on the prediction. For example, it can predict the direction the cat will move next and perform movements in that direction in advance. It can also predict what movement the cat will make next and perform movements in accordance with that movement. It can also predict how fast the cat will move next and perform movements in accordance with that speed. This makes it possible to provide more effective exercise by anticipating the movements of the cat.

[0129] The action unit can estimate the emotion of the cat and adjust the timing of the action based on the estimated emotion. For example, if the cat is excited, the action timing can be sped up to make the cat more active. If the cat is relaxed, the action timing can be slowed down to make the cat more gentle. Furthermore, if the cat is stressed, the action timing can be adjusted to reduce the stress. This makes it possible to provide appropriate actions according to the emotion of the cat.

[0130] The detection unit can detect not only the movements of a pet cat, but also its meows. For example, if a pet cat meows, the detection unit can analyze the pattern of the meows to estimate the cat's emotions. It can also detect the frequency and volume of the cat's meows to understand the cat's condition. Furthermore, it can predict the movements that the cat will be interested in based on the cat's meows. This makes it possible to provide more effective exercise by utilizing the cat's meows.

[0131] The operation unit can estimate the emotion of the cat and select the type of movement based on the estimated emotion. For example, if the cat is excited, the operation unit can select a movement such as a jump or a sudden change of direction. If the cat is relaxed, the operation unit can select a slow or gentle movement. Furthermore, if the cat is stressed, the operation unit can select a movement to reduce stress. In this way, it is possible to provide an appropriate movement according to the emotion of the cat.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The detection unit detects the movement of the cat. Examples of the cat's movements include, but are not limited to, walking, jumping, and playing. The detection unit uses an infrared sensor and a camera to detect the cat's movements and collect data such as the movement pattern, speed, and direction. For example, the sensor detects information such as the direction in which the cat is moving and how fast it is moving. Step 2: The analysis unit analyzes the data detected by the detection unit. The analysis unit uses AI to analyze the cat's movement patterns and predict movements that will interest the cat. For example, if the cat often makes sudden changes of direction, the mouse robot is programmed to make sudden changes of direction as well. The analysis unit can also learn the cat's movement patterns and predict movements that will interest the cat. Step 3: The operating unit causes the mouse-shaped robot to move based on the data analyzed by the analyzing unit. The operating unit attracts the interest of the pet cat by making the mouse-shaped robot suddenly change direction or speed. The operating unit can also incorporate random movements to prevent the pet cat from getting bored. For example, the operating unit may include a random operating unit that incorporates random movements.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] [Explanation of symbols]

[0206] 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 detection unit that detects the movement of a pet cat; an analysis unit that analyzes the data detected by the detection unit; an operating unit that causes the mouse-type robot to move based on the data analyzed by the analyzing unit; Equipped with A system characterized by:

2. The operating unit Equipped with a random movement section that incorporates random movements 2. The system of claim 1.

3. The operating unit Equipped with an attraction part that moves to attract the interest of pet cats 2. The system of claim 1.

4. The detection unit Use an infrared sensor or camera to detect your cat's movements 2. The system of claim 1.

5. The analysis unit Learns your cat's movement patterns and predicts what it will do to interest it 2. The system of claim 1.

6. The detection unit Estimate the emotions of your pet cat and adjust the accuracy of its motion detection based on the estimated emotions.

2. The system of claim 1.

7. The detection unit Analyze your cat's past movement history and select the optimal detection method 2. The system of claim 1.

8. The detection unit Upon detection, filtering is performed based on the cat's physical condition and activity level.

2. The system of claim 1.

9. The detection unit When detecting, the most appropriate detection method is selected according to the speed and direction of the cat's movement.

2. The system of claim 1.

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