system

Abstract

An object of a system according to an embodiment is to detect an abnormal value of skeleton information and more accurately predict a current state.SOLUTION: A system according to an embodiment includes an acquisition unit, an analysis unit, a provision unit, and a prediction unit. The acquisition unit acquires skeleton information. The analysis unit analyzes the skeleton information acquired by the acquisition unit and detects an abnormal value. The providing unit passes the skeleton information or the image information to the LLM based on the abnormal value detected by the analyzing unit. The prediction unit predicts the current situation based on the information passed by the provision unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, even if abnormal values ​​in skeletal information are detected, the subsequent current state prediction may not be accurate, and there is room for improvement.

[0005] The system according to the embodiment aims to detect abnormal values ​​in skeletal information and predict the current situation more accurately. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, an analysis unit, a provision unit, and a prediction unit. The acquisition unit acquires skeletal information. The analysis unit analyzes the skeletal information acquired by the acquisition unit and detects abnormal values. The provision unit passes the skeletal information or image information to the LLM based on the abnormal values ​​detected by the analysis unit. The prediction unit predicts the current situation based on the information passed by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect abnormal values ​​in the skeletal information and predict the current situation more accurately. [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 skeletal estimation system according to an embodiment of the present invention detects abnormal values ​​in skeletal information and performs highly accurate predictions using a large-scale language model (LLM). The skeletal estimation system performs standard skeletal estimation, and only if an abnormal value is detected, it passes skeletal information and image information to the LLM to predict the current situation. For example, the skeletal estimation system uses a camera to capture a person's movements and identifies the position of each joint using a skeletal estimation algorithm. The acquired skeletal information is then analyzed, and only if an abnormal value is detected, it passes the skeletal information and image information to the LLM. The LLM predicts the current situation based on this information. This allows for highly accurate predictions of abnormal situations while conserving machine power during standard skeletal estimation. This allows the skeletal estimation system to monitor the movements of athletes and patients in real time during sports training and rehabilitation, and respond quickly when an abnormality is detected. Furthermore, in surveillance camera systems, highly accurate predictions when abnormal movements are detected enable rapid response.

[0029] A skeletal estimation system according to an embodiment includes an acquisition unit, an analysis unit, a provision unit, and a prediction unit. The acquisition unit acquires skeletal information. For example, the acquisition unit may use a camera to capture a person's movements and identify the position of each joint using a skeletal estimation algorithm. For example, the acquisition unit may use a camera to capture a person's movements and identify the position of each joint using a skeletal estimation algorithm. The acquisition unit may also use multiple cameras to capture images from different angles to acquire more accurate skeletal information. The acquisition unit may also acquire skeletal information using a sensor. For example, the acquisition unit may acquire skeletal information using a motion capture sensor. The analysis unit analyzes the skeletal information acquired by the acquisition unit and detects abnormal values. For example, the analysis unit may use an anomaly detection algorithm using a statistical method based on past data or machine learning. The analysis unit may detect abnormal values ​​using a statistical method based on past data. The analysis unit may also use an anomaly detection algorithm using machine learning. The analysis unit may analyze the skeletal information in real time and detect abnormal values. The provision unit passes the skeletal information and image information to the LLM based on the abnormal values ​​detected by the analysis unit. For example, the providing unit passes skeletal information and image information to the LLM only when an abnormal value is detected. The providing unit can pass skeletal information and image information to the LLM only when an abnormal value is detected. Furthermore, the providing unit can pass information to the LLM in real time when an abnormal value is detected. Furthermore, the providing unit can pass information, including past data, to the LLM when an abnormal value is detected. The prediction unit predicts the current situation based on the information passed by the providing unit. For example, the prediction unit predicts falls, poor posture, abnormal gait, etc. The prediction unit can predict falls, poor posture, abnormal gait, etc. The prediction unit can also learn based on data of past abnormal situations and data of normal states. Furthermore, the prediction unit can predict the current situation in real time. As a result, the skeleton estimation system according to the embodiment can detect abnormal values ​​in skeletal information and perform highly accurate predictions using the LLM.For example, a skeletal structure estimation system can monitor the movements of athletes or patients in real time during sports training or rehabilitation, enabling rapid response when abnormalities are detected. Surveillance camera systems can also rapidly respond by making highly accurate predictions when abnormal movements are detected.

[0030] The acquisition unit can capture a person's movements using a camera and identify the position of each joint using a skeletal estimation algorithm. For example, the acquisition unit can capture a person's movements using a camera and identify the position of each joint using a skeletal estimation algorithm. For example, the acquisition unit can capture a person's movements using a camera and identify the position of each joint using a skeletal estimation algorithm. The acquisition unit can also capture images from different angles using multiple cameras to acquire more accurate skeletal information. Furthermore, the acquisition unit can acquire skeletal information using a sensor. For example, the acquisition unit can acquire skeletal information using a motion capture sensor. This makes it possible to accurately capture a person's movements and acquire skeletal information using a camera. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input image data captured by a camera to a generation AI and cause the generation AI to generate skeletal information from the image data.

[0031] The analysis unit can use an anomaly detection algorithm that uses a statistical method based on past data or machine learning. The analysis unit can detect outliers, for example, using a statistical method based on past data. For example, the analysis unit can detect outliers using a statistical method based on past data. The analysis unit can also use an anomaly detection algorithm that uses machine learning. For example, the analysis unit can detect outliers using a machine learning algorithm such as a support vector machine or a random forest. Furthermore, the analysis unit can analyze skeletal information in real time to detect outliers. For example, the analysis unit can analyze skeletal information in real time to detect outliers. This allows for improving the accuracy of anomaly detection by utilizing past data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to detect outliers.

[0032] The providing unit can pass skeletal information or image information to the LLM only when an abnormal value is detected. For example, the providing unit can pass skeletal information or image information to the LLM only when an abnormal value is detected. For example, the providing unit can pass skeletal information or image information to the LLM only when an abnormal value is detected. The providing unit can also pass information to the LLM in real time when an abnormal value is detected. Furthermore, the providing unit can pass information, including past data, to the LLM when an abnormal value is detected. This allows machine power to be saved by using the LLM only when an abnormal value is detected. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, when an abnormal value is detected, the providing unit can input skeletal information or image information to the generation AI and cause the generation AI to provide information.

[0033] The prediction unit can predict falls, poor posture, abnormal gait, etc. The prediction unit can predict falls, poor posture, abnormal gait, etc. For example, the prediction unit can predict falls, poor posture, abnormal gait, etc. The prediction unit can also learn based on data of past abnormal situations and data of normal states. For example, the prediction unit can learn based on data of past abnormal situations and data of normal states. The prediction unit can also predict the current situation in real time. For example, the prediction unit can predict the current situation in real time. This allows for highly accurate prediction of abnormal situations. Some or all of the above-described processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input skeletal information and image information into an LLM and cause the LLM to predict the current situation.

[0034] The prediction unit can learn based on data of past abnormal situations or data of normal situations. The prediction unit learns, for example, based on data of past abnormal situations or data of normal situations. For example, the prediction unit can learn based on data of past abnormal situations or data of normal situations. Furthermore, the prediction unit can improve the accuracy of prediction by learning based on data of past abnormal situations or data of normal situations. Furthermore, the prediction unit can predict the current situation in real time. For example, the prediction unit can predict the current situation in real time. As a result, the accuracy of prediction can be improved by learning based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input data of past abnormal situations or data of normal situations into an LLM and cause the LLM to perform prediction learning.

[0035] The acquisition unit can analyze the user's past motion history and select the optimal acquisition method when acquiring skeletal information. For example, when acquiring skeletal information, the acquisition unit analyzes the user's past motion history and selects the optimal acquisition method. For example, if the user has frequently performed a specific motion in the past, the acquisition unit selects the optimal acquisition method for that motion. The acquisition unit can also identify motions that are prone to abnormalities from the user's past motion history and acquire skeletal information focusing on those motions. The acquisition unit can also select the most efficient acquisition method based on the user's past motion history and improve data accuracy. By selecting the optimal acquisition method based on the past motion history, data accuracy can be improved. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's past motion history data into a generation AI and cause the generation AI to select the optimal acquisition method.

[0036] The acquisition unit can perform filtering based on the user's current activity status or environment when acquiring skeletal information. For example, the acquisition unit performs filtering based on the user's current activity status or environment when acquiring skeletal information. For example, when the user is exercising, the acquisition unit filters the skeletal information according to the intensity of the movement. Furthermore, when the user is stationary, the acquisition unit can adjust filtering to capture even subtle movements. Furthermore, when the user is outdoors, the acquisition unit can perform filtering to remove environmental noise. In this way, more accurate skeletal information can be acquired by filtering according to the activity status and environment. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current activity status and environmental data to the generation AI and cause the generation AI to adjust the filtering.

[0037] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring the skeletal information. For example, the acquisition unit selects the optimal acquisition means depending on the user's input method (voice, text, image, etc.) when acquiring the skeletal information. For example, if the user uses voice input, the acquisition unit can acquire the skeletal information using voice recognition technology. Also, if the user uses text input, the acquisition unit can acquire the skeletal information using text analysis technology. Also, if the user uses image input, the acquisition unit can acquire the skeletal information using image analysis technology. This allows for efficient acquisition of skeletal information by selecting the optimal acquisition means depending on the input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0038] When acquiring skeletal information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when acquiring skeletal information, the acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific location, the acquisition unit prioritizes acquiring skeletal information related to the location. Furthermore, when the user is moving, the acquisition unit can also prioritize acquiring skeletal information related to the movement. Furthermore, when the user is in a specific area, the acquisition unit can also prioritize acquiring skeletal information suitable for the environment of that area. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize acquiring highly relevant information.

[0039] The acquisition unit can analyze the user's social media activity when acquiring the skeletal information and acquire related information. For example, the acquisition unit can analyze the user's social media activity when acquiring the skeletal information and acquire related information. For example, the acquisition unit can acquire skeletal information related to a location where the user checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related skeletal information. The acquisition unit can also acquire related skeletal information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently acquired by analyzing social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0040] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring skeletal information. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring skeletal information. For example, the acquisition unit customizes the acquisition method based on feedback provided by the user in the past. The acquisition unit can also preferentially use a specific acquisition method based on the user's past feedback. The acquisition unit can also analyze the user's past feedback and suggest an optimal acquisition method. In this way, the acquisition method can be customized and accuracy can be improved by reflecting the past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0041] The analysis unit can optimize the anomaly detection algorithm by referring to past data when detecting an abnormal value. For example, the analysis unit optimizes the anomaly detection algorithm by referring to past data when detecting an abnormal value. For example, the analysis unit optimizes the anomaly detection algorithm based on past abnormal data. The analysis unit can also optimize the anomaly detection algorithm based on past normal data. The analysis unit can also optimize the anomaly detection algorithm by comparing past abnormal and normal data. In this way, by referring to past data, the anomaly detection algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and cause the generation AI to optimize the anomaly detection algorithm.

[0042] The analysis unit can analyze the user's movement patterns when an abnormal value is detected, thereby improving the accuracy of anomaly detection. For example, the analysis unit can analyze the user's movement patterns when an abnormal value is detected, thereby improving the accuracy of anomaly detection. For example, the analysis unit can analyze the user's past movement patterns to improve the accuracy of anomaly detection. The analysis unit can also analyze the user's current movement patterns in real time to improve the accuracy of anomaly detection. The analysis unit can also optimize the anomaly detection algorithm based on the user's movement patterns. In this way, the accuracy of anomaly detection can be improved by analyzing the movement patterns. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's movement pattern data to the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0043] When detecting an abnormal value, the analysis unit can perform anomaly detection based on the user's attribute information. For example, when detecting an abnormal value, the analysis unit performs anomaly detection taking into account the user's attribute information. For example, the analysis unit performs anomaly detection taking into account the user's age. The analysis unit can also perform anomaly detection taking into account the user's gender. The analysis unit can also perform anomaly detection taking into account the user's health condition. This enables more individualized anomaly detection by taking attribute information into account. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's attribute information into the generation AI and cause the generation AI to perform anomaly detection.

[0044] The analysis unit can perform anomaly detection taking into account the geographical distribution of users when detecting anomalies. For example, when detecting anomalies, the analysis unit performs anomaly detection taking into account the geographical distribution of users. For example, when a user is in a specific area, the analysis unit performs anomaly detection appropriate for the environment of that area. Furthermore, when a user is traveling, the analysis unit can perform anomaly detection related to the user's movement. Furthermore, when a user is in a specific location, the analysis unit can perform anomaly detection related to the location. This enables anomaly detection appropriate for the environment by taking the geographical distribution into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of users into a generation AI and cause the generation AI to perform anomaly detection.

[0045] The analysis unit can improve the accuracy of anomaly detection by referring to related literature when detecting an abnormal value. For example, the analysis unit can improve the accuracy of anomaly detection by referring to related literature when detecting an abnormal value. For example, the analysis unit optimizes the anomaly detection algorithm based on the related literature. The analysis unit can also improve the accuracy of anomaly detection by referring to data in the related literature. The analysis unit can also incorporate knowledge from the related literature to improve the anomaly detection algorithm. In this way, the accuracy of anomaly detection can be improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0046] When detecting an abnormal value, the analysis unit can perform anomaly detection based on the market value of the anomaly. For example, when detecting an abnormal value, the analysis unit performs anomaly detection taking into account the market value of the anomaly. For example, the analysis unit optimizes the anomaly detection algorithm based on the market value of the anomaly. The analysis unit can also prioritize detection of important anomalies by taking into account the market value of the anomaly. The analysis unit can also analyze the market value of the anomaly and improve the accuracy of anomaly detection. In this way, by taking into account the market value, important anomalies can be prioritized for detection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data of the anomaly to the generation AI and cause the generation AI to perform anomaly detection.

[0047] The providing unit can optimize the providing algorithm based on data of past abnormal situations when providing information. For example, the providing unit optimizes the providing algorithm by referring to data of past abnormal situations when providing information. For example, the providing unit optimizes the providing algorithm based on data of past abnormal situations. The providing unit can also optimize the providing algorithm based on data of past normal situations. The providing unit can also optimize the providing algorithm by comparing data of past abnormal and normal situations. In this way, by referring to past data, the providing algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past data to the generating AI and cause the generating AI to optimize the providing algorithm.

[0048] The providing unit can analyze the user's movement patterns when providing information and improve the accuracy of the information provided. For example, the providing unit can analyze the user's movement patterns when providing information and improve the accuracy of the information provided. For example, the providing unit can analyze the user's past movement patterns and improve the accuracy of the information provided. The providing unit can also analyze the user's current movement patterns in real time and improve the accuracy of the information provided. The providing unit can also optimize the providing algorithm based on the user's movement patterns. In this way, the accuracy of the information provided can be improved by analyzing the movement patterns. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's movement pattern data to a generation AI and cause the generation AI to improve the accuracy of the information provided.

[0049] The providing unit can provide information based on the user's attribute information when providing information. For example, the providing unit provides information taking into consideration the user's attribute information when providing information. For example, the providing unit provides information taking into consideration the user's age. The providing unit can also provide information taking into consideration the user's gender. The providing unit can also provide information taking into consideration the user's health condition. This makes it possible to provide more personalized information by taking into consideration the attribute information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's attribute information into the generation AI and cause the generation AI to provide information.

[0050] The providing unit can provide information based on the geographical distribution of users when providing information. For example, the providing unit can provide information taking into account the geographical distribution of users when providing information. For example, when a user is in a specific area, the providing unit can provide information related to that area. Furthermore, when a user is traveling, the providing unit can provide information related to the user's travel. Furthermore, when a user is in a specific location, the providing unit can provide information related to the location. This makes it possible to provide information appropriate to the environment by taking geographical distribution into consideration. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input user geographical distribution data into a generation AI and cause the generation AI to provide information.

[0051] The providing unit can improve the accuracy of the information provided by referring to related literature when providing information. For example, the providing unit can improve the accuracy of the information provided by referring to related literature when providing information. For example, the providing unit can optimize the providing algorithm based on the related literature. The providing unit can also improve the accuracy of the information provided by referring to data from related literature. The providing unit can also incorporate knowledge from related literature and improve the providing algorithm. In this way, the accuracy of the information provided can be improved by referring to related literature. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input related literature data into the generating AI and cause the generating AI to improve the accuracy of the information provided.

[0052] The providing unit can provide the information based on the market value of the anomaly when providing the information. For example, the providing unit provides the information taking into account the market value of the anomaly. For example, the providing unit optimizes a providing algorithm based on the market value of the anomaly. The providing unit can also take into account the market value of the anomaly and provide important anomalies preferentially. The providing unit can also analyze the market value of the anomaly and improve the accuracy of the provision. In this way, important anomalies can be provided preferentially by taking market value into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input market value data of the anomaly to a generation AI and cause the generation AI to provide the information.

[0053] The prediction unit can optimize the prediction algorithm based on data on past abnormal situations during prediction. For example, the prediction unit optimizes the prediction algorithm by referring to data on past abnormal situations during prediction. For example, the prediction unit optimizes the prediction algorithm based on data on past abnormal situations. The prediction unit can also optimize the prediction algorithm based on data on past normal situations. The prediction unit can also optimize the prediction algorithm by comparing data on past abnormal and normal situations. In this way, by referring to past data, the prediction algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input past data into the generation AI and cause the generation AI to optimize the prediction algorithm.

[0054] The prediction unit can analyze the user's movement patterns during prediction and improve the accuracy of the prediction. For example, the prediction unit can analyze the user's movement patterns during prediction and improve the accuracy of the prediction. For example, the prediction unit can analyze the user's past movement patterns and improve the accuracy of the prediction. The prediction unit can also analyze the user's current movement patterns in real time and improve the accuracy of the prediction. The prediction unit can also optimize the prediction algorithm based on the user's movement patterns. In this way, the accuracy of the prediction can be improved by analyzing the movement patterns. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an LLM, or can be performed without using an LLM. For example, the prediction unit can input the user's movement pattern data into a generation AI and cause the generation AI to improve the accuracy of the prediction.

[0055] The prediction unit can make predictions based on the user's attribute information when making predictions. For example, the prediction unit makes predictions taking into account the user's attribute information when making predictions. For example, the prediction unit makes predictions taking into account the user's age. The prediction unit can also make predictions taking into account the user's gender. The prediction unit can also make predictions taking into account the user's health condition. This allows for more personalized predictions by taking into account the attribute information. Some or all of the above-described processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input the user's attribute information into a generation AI and cause the generation AI to perform a prediction.

[0056] The prediction unit can make predictions based on the geographical distribution of users when making predictions. For example, the prediction unit makes predictions taking into account the geographical distribution of users when making predictions. For example, if a user is in a specific area, the prediction unit makes predictions appropriate for the environment of that area. Furthermore, if a user is traveling, the prediction unit can make predictions related to the user's travel. Furthermore, if a user is in a specific location, the prediction unit can make predictions related to the location. This makes it possible to make predictions appropriate for the environment by taking the geographical distribution into consideration. Some or all of the above-described processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input the user's geographical distribution data into a generation AI and have the generation AI perform a prediction.

[0057] The prediction unit can improve the accuracy of the prediction by referring to related literature during prediction. For example, the prediction unit can improve the accuracy of the prediction by referring to related literature during prediction. For example, the prediction unit can optimize the prediction algorithm based on related literature. The prediction unit can also improve the accuracy of the prediction by referring to data from related literature. The prediction unit can also incorporate knowledge from related literature to improve the prediction algorithm. In this way, the accuracy of the prediction can be improved by referring to related literature. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an LLM, or can be performed without using an LLM. For example, the prediction unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0058] The prediction unit can make predictions based on the market value of anomalies when making predictions. For example, the prediction unit makes predictions taking into account the market value of anomalies when making predictions. For example, the prediction unit optimizes a prediction algorithm based on the market value of anomalies. The prediction unit can also take into account the market value of anomalies and prioritize predicting important anomalies. The prediction unit can also analyze the market value of anomalies and improve the accuracy of the predictions. This allows for prioritized prediction of important anomalies by taking market value into account. Some or all of the above-described processing in the prediction unit can be performed using, for example, an LLM, or can be performed without using an LLM. For example, the prediction unit can input market value data of anomalies into a generation AI and have the generation AI execute a prediction.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The acquisition unit acquires biometric information of the user and can improve the accuracy of acquiring skeletal information based on the acquired information. For example, the acquisition unit acquires biometric information such as heart rate and body temperature, and adjusts the timing of acquiring skeletal information based on the acquired information. The acquisition unit can also analyze the user's breathing pattern and acquire skeletal information in accordance with the breathing rhythm. Furthermore, the acquisition unit can measure the user's myoelectric potential and acquire skeletal information based on muscle movement. This makes it possible to acquire more accurate skeletal information by utilizing the biometric information.

[0061] When an abnormal value is detected, the providing unit can customize the information to be passed to the LLM by referring to the user's past behavioral history. For example, if the user has frequently performed a specific behavior in the past, the providing unit will prioritize passing information related to that behavior to the LLM. The providing unit can also identify behaviors that are likely to cause abnormalities from the user's past behavioral history and pass information related to those behaviors to the LLM. Furthermore, the providing unit can pass the most relevant information to the LLM based on the user's past behavioral history. This makes it possible to provide more appropriate information to the LLM by utilizing past behavioral history.

[0062] The acquisition unit can optimize the method of acquiring skeletal information by taking into account the user's geographical location information. For example, when the user is outdoors, the acquisition unit performs filtering to remove environmental noise. Furthermore, when the user is in a specific area, the acquisition unit can select an acquisition method suitable for the environment of that area. Furthermore, when the user is moving, the acquisition unit can prioritize acquiring skeletal information related to movement. In this way, by taking into account the geographical location information, more accurate skeletal information can be acquired.

[0063] When detecting an anomaly, the analysis unit can analyze the user's social media activity to improve the accuracy of anomaly detection. For example, the analysis unit can detect anomalies related to the locations where the user checked in on social media. The analysis unit can also analyze the content of the user's social media posts to improve the accuracy of anomaly detection. Furthermore, the analysis unit can also improve the accuracy of anomaly detection by referring to the activities of the user's friends on social media. In this way, the accuracy of anomaly detection can be improved by analyzing social media activity.

[0064] The prediction unit can customize the prediction algorithm by reflecting the user's past feedback when making predictions. For example, the prediction unit customizes the prediction algorithm based on feedback provided by the user in the past. The prediction unit can also preferentially use a specific prediction method based on the user's past feedback. Furthermore, the prediction unit can analyze the user's past feedback and suggest an optimal prediction method. In this way, the prediction algorithm can be customized by reflecting past feedback, thereby improving accuracy.

[0065] When detecting an abnormal value, the analysis unit can improve the accuracy of anomaly detection by referring to related literature. For example, the analysis unit can optimize the anomaly detection algorithm based on the related literature. The analysis unit can also improve the accuracy of anomaly detection by referring to data in the related literature. Furthermore, the analysis unit can incorporate knowledge from the related literature and improve the anomaly detection algorithm. In this way, by referring to the related literature, the accuracy of anomaly detection can be improved.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The acquisition unit acquires skeletal information. For example, the acquisition unit uses a camera to capture a person's movements and identifies the position of each joint using a skeletal estimation algorithm. It is also possible to use multiple cameras to capture images from different angles to acquire more accurate skeletal information. It is also possible to acquire skeletal information using a motion capture sensor. Step 2: The analysis unit analyzes the skeletal information acquired by the acquisition unit and detects abnormal values. For example, it uses statistical methods based on past data or anomaly detection algorithms that use machine learning. It is also possible to analyze skeletal information in real time and detect abnormal values. Step 3: The provider passes skeletal and image information to the LLM based on the abnormal values ​​detected by the analyzer. For example, information can be passed to the LLM in real time only when an abnormal value is detected. It is also possible to pass information to the LLM, including past data. Step 4: The prediction unit predicts the current situation based on the information provided by the provision unit. For example, it predicts falls, poor posture, abnormal walking, etc. It can also learn from data on past abnormal situations and data on normal conditions, and predict the current situation in real time.

[0068] (Example 2) A skeletal estimation system according to an embodiment of the present invention detects abnormal values ​​in skeletal information and performs highly accurate predictions using a large-scale language model (LLM). The skeletal estimation system performs standard skeletal estimation, and only if an abnormal value is detected, it passes skeletal information and image information to the LLM to predict the current situation. For example, the skeletal estimation system uses a camera to capture a person's movements and identifies the position of each joint using a skeletal estimation algorithm. The acquired skeletal information is then analyzed, and only if an abnormal value is detected, it passes the skeletal information and image information to the LLM. The LLM predicts the current situation based on this information. This allows for highly accurate predictions of abnormal situations while conserving machine power during standard skeletal estimation. This allows the skeletal estimation system to monitor the movements of athletes and patients in real time during sports training and rehabilitation, and respond quickly when an abnormality is detected. Furthermore, in surveillance camera systems, highly accurate predictions when abnormal movements are detected enable rapid response.

[0069] A skeletal estimation system according to an embodiment includes an acquisition unit, an analysis unit, a provision unit, and a prediction unit. The acquisition unit acquires skeletal information. For example, the acquisition unit may use a camera to capture a person's movements and identify the position of each joint using a skeletal estimation algorithm. For example, the acquisition unit may use a camera to capture a person's movements and identify the position of each joint using a skeletal estimation algorithm. The acquisition unit may also use multiple cameras to capture images from different angles to acquire more accurate skeletal information. The acquisition unit may also acquire skeletal information using a sensor. For example, the acquisition unit may acquire skeletal information using a motion capture sensor. The analysis unit analyzes the skeletal information acquired by the acquisition unit and detects abnormal values. For example, the analysis unit may use an anomaly detection algorithm using a statistical method based on past data or machine learning. The analysis unit may detect abnormal values ​​using a statistical method based on past data. The analysis unit may also use an anomaly detection algorithm using machine learning. The analysis unit may analyze the skeletal information in real time and detect abnormal values. The provision unit passes the skeletal information and image information to the LLM based on the abnormal values ​​detected by the analysis unit. For example, the providing unit passes skeletal information and image information to the LLM only when an abnormal value is detected. The providing unit can pass skeletal information and image information to the LLM only when an abnormal value is detected. Furthermore, the providing unit can pass information to the LLM in real time when an abnormal value is detected. Furthermore, the providing unit can pass information, including past data, to the LLM when an abnormal value is detected. The prediction unit predicts the current situation based on the information passed by the providing unit. For example, the prediction unit predicts falls, poor posture, abnormal gait, etc. The prediction unit can predict falls, poor posture, abnormal gait, etc. The prediction unit can also learn based on data of past abnormal situations and data of normal states. Furthermore, the prediction unit can predict the current situation in real time. As a result, the skeleton estimation system according to the embodiment can detect abnormal values ​​in skeletal information and perform highly accurate predictions using the LLM.For example, a skeletal structure estimation system can monitor the movements of athletes or patients in real time during sports training or rehabilitation, enabling rapid response when abnormalities are detected. Surveillance camera systems can also rapidly respond by making highly accurate predictions when abnormal movements are detected.

[0070] The acquisition unit can capture a person's movements using a camera and identify the position of each joint using a skeletal estimation algorithm. For example, the acquisition unit can capture a person's movements using a camera and identify the position of each joint using a skeletal estimation algorithm. For example, the acquisition unit can capture a person's movements using a camera and identify the position of each joint using a skeletal estimation algorithm. The acquisition unit can also capture images from different angles using multiple cameras to acquire more accurate skeletal information. Furthermore, the acquisition unit can acquire skeletal information using a sensor. For example, the acquisition unit can acquire skeletal information using a motion capture sensor. This makes it possible to accurately capture a person's movements and acquire skeletal information using a camera. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input image data captured by a camera to a generation AI and cause the generation AI to generate skeletal information from the image data.

[0071] The analysis unit can use an anomaly detection algorithm that uses a statistical method based on past data or machine learning. The analysis unit can detect outliers, for example, using a statistical method based on past data. For example, the analysis unit can detect outliers using a statistical method based on past data. The analysis unit can also use an anomaly detection algorithm that uses machine learning. For example, the analysis unit can detect outliers using a machine learning algorithm such as a support vector machine or a random forest. Furthermore, the analysis unit can analyze skeletal information in real time to detect outliers. For example, the analysis unit can analyze skeletal information in real time to detect outliers. This allows for improving the accuracy of anomaly detection by utilizing past data. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past data into the generation AI and cause the generation AI to detect outliers.

[0072] The providing unit can pass skeletal information or image information to the LLM only when an abnormal value is detected. For example, the providing unit can pass skeletal information or image information to the LLM only when an abnormal value is detected. For example, the providing unit can pass skeletal information or image information to the LLM only when an abnormal value is detected. The providing unit can also pass information to the LLM in real time when an abnormal value is detected. Furthermore, the providing unit can pass information, including past data, to the LLM when an abnormal value is detected. This allows machine power to be saved by using the LLM only when an abnormal value is detected. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, when an abnormal value is detected, the providing unit can input skeletal information or image information to the generation AI and cause the generation AI to provide information.

[0073] The prediction unit can predict falls, poor posture, abnormal gait, etc. The prediction unit can predict falls, poor posture, abnormal gait, etc. For example, the prediction unit can predict falls, poor posture, abnormal gait, etc. The prediction unit can also learn based on data of past abnormal situations and data of normal states. For example, the prediction unit can learn based on data of past abnormal situations and data of normal states. The prediction unit can also predict the current situation in real time. For example, the prediction unit can predict the current situation in real time. This allows for highly accurate prediction of abnormal situations. Some or all of the above-described processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input skeletal information and image information into an LLM and cause the LLM to predict the current situation.

[0074] The prediction unit can learn based on data of past abnormal situations or data of normal situations. The prediction unit learns, for example, based on data of past abnormal situations or data of normal situations. For example, the prediction unit can learn based on data of past abnormal situations or data of normal situations. Furthermore, the prediction unit can improve the accuracy of prediction by learning based on data of past abnormal situations or data of normal situations. Furthermore, the prediction unit can predict the current situation in real time. For example, the prediction unit can predict the current situation in real time. As a result, the accuracy of prediction can be improved by learning based on past data. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input data of past abnormal situations or data of normal situations into an LLM and cause the LLM to perform prediction learning.

[0075] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring skeletal information based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and adjusts the timing of acquiring skeletal information based on the estimated user's emotion. For example, if the user is nervous, the acquisition unit temporarily delays acquiring skeletal information until the user relaxes. Furthermore, if the user is relaxed, the acquisition unit can also acquire skeletal information at a normal timing. Furthermore, if the user is excited, the acquisition unit can increase the frequency of acquiring skeletal information to collect more detailed data. This allows for adjusting the timing of acquiring skeletal information according to the user's emotion, thereby acquiring more appropriate data. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the acquisition timing based on the emotion.

[0076] The acquisition unit can analyze the user's past motion history and select the optimal acquisition method when acquiring skeletal information. For example, when acquiring skeletal information, the acquisition unit analyzes the user's past motion history and selects the optimal acquisition method. For example, if the user has frequently performed a specific motion in the past, the acquisition unit selects the optimal acquisition method for that motion. The acquisition unit can also identify motions that are prone to abnormalities from the user's past motion history and acquire skeletal information focusing on those motions. The acquisition unit can also select the most efficient acquisition method based on the user's past motion history and improve data accuracy. By selecting the optimal acquisition method based on the past motion history, data accuracy can be improved. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's past motion history data into a generation AI and cause the generation AI to select the optimal acquisition method.

[0077] The acquisition unit can perform filtering based on the user's current activity status or environment when acquiring skeletal information. For example, the acquisition unit performs filtering based on the user's current activity status or environment when acquiring skeletal information. For example, when the user is exercising, the acquisition unit filters the skeletal information according to the intensity of the movement. Furthermore, when the user is stationary, the acquisition unit can adjust filtering to capture even subtle movements. Furthermore, when the user is outdoors, the acquisition unit can perform filtering to remove environmental noise. In this way, more accurate skeletal information can be acquired by filtering according to the activity status and environment. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's current activity status and environmental data to the generation AI and cause the generation AI to adjust the filtering.

[0078] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring the skeletal information. For example, the acquisition unit selects the optimal acquisition means depending on the user's input method (voice, text, image, etc.) when acquiring the skeletal information. For example, if the user uses voice input, the acquisition unit can acquire the skeletal information using voice recognition technology. Also, if the user uses text input, the acquisition unit can acquire the skeletal information using text analysis technology. Also, if the user uses image input, the acquisition unit can acquire the skeletal information using image analysis technology. This allows for efficient acquisition of skeletal information by selecting the optimal acquisition means depending on the input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to a generation AI and cause the generation AI to select the optimal acquisition means.

[0079] The acquisition unit can estimate the user's emotion and determine the priority of the skeletal information to be acquired based on the estimated user's emotion. For example, the acquisition unit can estimate the user's emotion and determine the priority of the skeletal information to be acquired based on the estimated user's emotion. For example, when the user is nervous, the acquisition unit prioritizes acquiring skeletal information that is important for relaxation. Furthermore, when the user is relaxed, the acquisition unit can also evenly acquire overall skeletal information. Furthermore, when the user is excited, the acquisition unit can also prioritize acquiring skeletal information related to movements. Thus, by determining the priority of the skeletal information according to the user's emotion, important information can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority based on the emotion.

[0080] When acquiring skeletal information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when acquiring skeletal information, the acquisition unit prioritizes acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific location, the acquisition unit prioritizes acquiring skeletal information related to the location. Furthermore, when the user is moving, the acquisition unit can also prioritize acquiring skeletal information related to the movement. Furthermore, when the user is in a specific area, the acquisition unit can also prioritize acquiring skeletal information suitable for the environment of that area. In this way, highly relevant information can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using AI, for example, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize acquiring highly relevant information.

[0081] The acquisition unit can analyze the user's social media activity when acquiring the skeletal information and acquire related information. For example, the acquisition unit can analyze the user's social media activity when acquiring the skeletal information and acquire related information. For example, the acquisition unit can acquire skeletal information related to a location where the user checked in on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related skeletal information. The acquisition unit can also acquire related skeletal information by referring to the activities of the user's friends on social media. In this way, related information can be efficiently acquired by analyzing social media activity. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0082] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring skeletal information. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring skeletal information. For example, the acquisition unit customizes the acquisition method based on feedback provided by the user in the past. The acquisition unit can also preferentially use a specific acquisition method based on the user's past feedback. The acquisition unit can also analyze the user's past feedback and suggest an optimal acquisition method. In this way, the acquisition method can be customized and accuracy can be improved by reflecting the past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0083] The analysis unit can estimate the user's emotions and adjust the outlier detection criteria based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the outlier detection criteria based on the estimated user emotions. For example, the analysis unit relaxes the outlier detection criteria when the user is nervous. The analysis unit can also use normal outlier detection criteria when the user is relaxed. The analysis unit can also tighten the outlier detection criteria when the user is excited. This enables more appropriate anomaly detection by adjusting the outlier detection criteria according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the detection criteria based on the emotion.

[0084] The analysis unit can optimize the anomaly detection algorithm by referring to past data when detecting an abnormal value. For example, the analysis unit optimizes the anomaly detection algorithm by referring to past data when detecting an abnormal value. For example, the analysis unit optimizes the anomaly detection algorithm based on past abnormal data. The analysis unit can also optimize the anomaly detection algorithm based on past normal data. The analysis unit can also optimize the anomaly detection algorithm by comparing past abnormal and normal data. In this way, by referring to past data, the anomaly detection algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past data to the generation AI and cause the generation AI to optimize the anomaly detection algorithm.

[0085] The analysis unit can analyze the user's movement patterns when an abnormal value is detected, thereby improving the accuracy of anomaly detection. For example, the analysis unit can analyze the user's movement patterns when an abnormal value is detected, thereby improving the accuracy of anomaly detection. For example, the analysis unit can analyze the user's past movement patterns to improve the accuracy of anomaly detection. The analysis unit can also analyze the user's current movement patterns in real time to improve the accuracy of anomaly detection. The analysis unit can also optimize the anomaly detection algorithm based on the user's movement patterns. In this way, the accuracy of anomaly detection can be improved by analyzing the movement patterns. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's movement pattern data to the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0086] When detecting an abnormal value, the analysis unit can perform anomaly detection based on the user's attribute information. For example, when detecting an abnormal value, the analysis unit performs anomaly detection taking into account the user's attribute information. For example, the analysis unit performs anomaly detection taking into account the user's age. The analysis unit can also perform anomaly detection taking into account the user's gender. The analysis unit can also perform anomaly detection taking into account the user's health condition. This enables more individualized anomaly detection by taking attribute information into account. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the user's attribute information into the generation AI and cause the generation AI to perform anomaly detection.

[0087] The analysis unit can estimate the user's emotions and adjust the display method of abnormal values ​​based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of abnormal values ​​based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is excited, the analysis unit can provide a visually stimulating display method. This improves visibility by adjusting the display method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0088] The analysis unit can perform anomaly detection taking into account the geographical distribution of users when detecting anomalies. For example, when detecting anomalies, the analysis unit performs anomaly detection taking into account the geographical distribution of users. For example, when a user is in a specific area, the analysis unit performs anomaly detection appropriate for the environment of that area. Furthermore, when a user is traveling, the analysis unit can perform anomaly detection related to the user's movement. Furthermore, when a user is in a specific location, the analysis unit can perform anomaly detection related to the location. This enables anomaly detection appropriate for the environment by taking the geographical distribution into account. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data of users into a generation AI and cause the generation AI to perform anomaly detection.

[0089] The analysis unit can improve the accuracy of anomaly detection by referring to related literature when detecting an abnormal value. For example, the analysis unit can improve the accuracy of anomaly detection by referring to related literature when detecting an abnormal value. For example, the analysis unit optimizes the anomaly detection algorithm based on the related literature. The analysis unit can also improve the accuracy of anomaly detection by referring to data in the related literature. The analysis unit can also incorporate knowledge from the related literature to improve the anomaly detection algorithm. In this way, the accuracy of anomaly detection can be improved by referring to the related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of anomaly detection.

[0090] When detecting an abnormal value, the analysis unit can perform anomaly detection based on the market value of the anomaly. For example, when detecting an abnormal value, the analysis unit performs anomaly detection taking into account the market value of the anomaly. For example, the analysis unit optimizes the anomaly detection algorithm based on the market value of the anomaly. The analysis unit can also prioritize detection of important anomalies by taking into account the market value of the anomaly. The analysis unit can also analyze the market value of the anomaly and improve the accuracy of anomaly detection. In this way, by taking into account the market value, important anomalies can be prioritized for detection. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data of the anomaly to the generation AI and cause the generation AI to perform anomaly detection.

[0091] The providing unit can estimate the user's emotions and determine the priority of information to be passed to the LLM based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of information to be passed to the LLM based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize passing important information to the LLM. Also, if the user is relaxed, the providing unit can evenly pass overall information to the LLM. Also, if the user is excited, the providing unit can prioritize passing detailed information to the LLM. In this way, by determining the priority of information according to the user's emotions, important information can be prioritized and passed to the LLM. 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., an LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority of information based on emotions.

[0092] The providing unit can optimize the providing algorithm based on data of past abnormal situations when providing information. For example, the providing unit optimizes the providing algorithm by referring to data of past abnormal situations when providing information. For example, the providing unit optimizes the providing algorithm based on data of past abnormal situations. The providing unit can also optimize the providing algorithm based on data of past normal situations. The providing unit can also optimize the providing algorithm by comparing data of past abnormal and normal situations. In this way, by referring to past data, the providing algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input past data to the generating AI and cause the generating AI to optimize the providing algorithm.

[0093] The providing unit can analyze the user's movement patterns when providing information and improve the accuracy of the information provided. For example, the providing unit can analyze the user's movement patterns when providing information and improve the accuracy of the information provided. For example, the providing unit can analyze the user's past movement patterns and improve the accuracy of the information provided. The providing unit can also analyze the user's current movement patterns in real time and improve the accuracy of the information provided. The providing unit can also optimize the providing algorithm based on the user's movement patterns. In this way, the accuracy of the information provided can be improved by analyzing the movement patterns. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's movement pattern data to a generation AI and cause the generation AI to improve the accuracy of the information provided.

[0094] The providing unit can provide information based on the user's attribute information when providing information. For example, the providing unit provides information taking into consideration the user's attribute information when providing information. For example, the providing unit provides information taking into consideration the user's age. The providing unit can also provide information taking into consideration the user's gender. The providing unit can also provide information taking into consideration the user's health condition. This makes it possible to provide more personalized information by taking into consideration the attribute information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's attribute information into the generation AI and cause the generation AI to provide information.

[0095] The providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the display method of the information to be provided based on the estimated user's emotion. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is excited, the providing unit can provide a visually stimulating display method. This improves visibility by adjusting the display method according to the user's emotion. 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. Some or all of the above-described processing in the providing unit can be performed using, for example, an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0096] The providing unit can provide information based on the geographical distribution of users when providing information. For example, the providing unit can provide information taking into account the geographical distribution of users when providing information. For example, when a user is in a specific area, the providing unit can provide information related to that area. Furthermore, when a user is traveling, the providing unit can provide information related to the user's travel. Furthermore, when a user is in a specific location, the providing unit can provide information related to the location. This makes it possible to provide information appropriate to the environment by taking geographical distribution into consideration. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input user geographical distribution data into a generation AI and cause the generation AI to provide information.

[0097] The providing unit can improve the accuracy of the information provided by referring to related literature when providing information. For example, the providing unit can improve the accuracy of the information provided by referring to related literature when providing information. For example, the providing unit can optimize the providing algorithm based on the related literature. The providing unit can also improve the accuracy of the information provided by referring to data from related literature. The providing unit can also incorporate knowledge from related literature and improve the providing algorithm. In this way, the accuracy of the information provided can be improved by referring to related literature. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or can be performed without using AI. For example, the providing unit can input related literature data into the generating AI and cause the generating AI to improve the accuracy of the information provided.

[0098] The providing unit can provide the information based on the market value of the anomaly when providing the information. For example, the providing unit provides the information taking into account the market value of the anomaly. For example, the providing unit optimizes a providing algorithm based on the market value of the anomaly. The providing unit can also take into account the market value of the anomaly and provide important anomalies preferentially. The providing unit can also analyze the market value of the anomaly and improve the accuracy of the provision. In this way, important anomalies can be provided preferentially by taking market value into account. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input market value data of the anomaly to a generation AI and cause the generation AI to provide the information.

[0099] The prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. For example, the prediction unit can estimate the user's emotion and adjust the prediction method based on the estimated user's emotion. For example, if the user is nervous, the prediction unit uses a prediction method for relaxation. If the user is relaxed, the prediction unit can also use a normal prediction method. If the user is excited, the prediction unit can also use a detailed prediction method. This enables more accurate prediction by adjusting the prediction method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, 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 such examples. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an LLM, or without an LLM. For example, the prediction unit can input user's emotion data into the generation AI and cause the generation AI to adjust the prediction method based on the emotion.

[0100] The prediction unit can optimize the prediction algorithm based on data on past abnormal situations during prediction. For example, the prediction unit optimizes the prediction algorithm by referring to data on past abnormal situations during prediction. For example, the prediction unit optimizes the prediction algorithm based on data on past abnormal situations. The prediction unit can also optimize the prediction algorithm based on data on past normal situations. The prediction unit can also optimize the prediction algorithm by comparing data on past abnormal and normal situations. In this way, by referring to past data, the prediction algorithm can be optimized and accuracy can be improved. Some or all of the above-mentioned processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input past data into the generation AI and cause the generation AI to optimize the prediction algorithm.

[0101] The prediction unit can analyze the user's movement patterns during prediction and improve the accuracy of the prediction. For example, the prediction unit can analyze the user's movement patterns during prediction and improve the accuracy of the prediction. For example, the prediction unit can analyze the user's past movement patterns and improve the accuracy of the prediction. The prediction unit can also analyze the user's current movement patterns in real time and improve the accuracy of the prediction. The prediction unit can also optimize the prediction algorithm based on the user's movement patterns. In this way, the accuracy of the prediction can be improved by analyzing the movement patterns. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an LLM, or can be performed without using an LLM. For example, the prediction unit can input the user's movement pattern data into a generation AI and cause the generation AI to improve the accuracy of the prediction.

[0102] The prediction unit can make predictions based on the user's attribute information when making predictions. For example, the prediction unit makes predictions taking into account the user's attribute information when making predictions. For example, the prediction unit makes predictions taking into account the user's age. The prediction unit can also make predictions taking into account the user's gender. The prediction unit can also make predictions taking into account the user's health condition. This allows for more personalized predictions by taking into account the attribute information. Some or all of the above-described processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input the user's attribute information into a generation AI and cause the generation AI to perform a prediction.

[0103] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, the prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can also provide a display method including detailed information. If the user is excited, the prediction unit can also provide a visually stimulating display method. This improves visibility by adjusting the display method according to the user's emotion. 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 such examples. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an LLM, or without an LLM. For example, the prediction unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0104] The prediction unit can make predictions based on the geographical distribution of users when making predictions. For example, the prediction unit makes predictions taking into account the geographical distribution of users when making predictions. For example, if a user is in a specific area, the prediction unit makes predictions appropriate for the environment of that area. Furthermore, if a user is traveling, the prediction unit can make predictions related to the user's travel. Furthermore, if a user is in a specific location, the prediction unit can make predictions related to the location. This makes it possible to make predictions appropriate for the environment by taking the geographical distribution into consideration. Some or all of the above-described processing in the prediction unit may be performed using, for example, an LLM, or may be performed without using an LLM. For example, the prediction unit can input the user's geographical distribution data into a generation AI and have the generation AI perform a prediction.

[0105] The prediction unit can improve the accuracy of the prediction by referring to related literature during prediction. For example, the prediction unit can improve the accuracy of the prediction by referring to related literature during prediction. For example, the prediction unit can optimize the prediction algorithm based on related literature. The prediction unit can also improve the accuracy of the prediction by referring to data from related literature. The prediction unit can also incorporate knowledge from related literature to improve the prediction algorithm. In this way, the accuracy of the prediction can be improved by referring to related literature. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, an LLM, or can be performed without using an LLM. For example, the prediction unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0106] The prediction unit can make predictions based on the market value of anomalies when making predictions. For example, the prediction unit makes predictions taking into account the market value of anomalies when making predictions. For example, the prediction unit optimizes a prediction algorithm based on the market value of anomalies. The prediction unit can also take into account the market value of anomalies and prioritize predicting important anomalies. The prediction unit can also analyze the market value of anomalies and improve the accuracy of the predictions. This allows for prioritized prediction of important anomalies by taking market value into account. Some or all of the above-described processing in the prediction unit can be performed using, for example, an LLM, or can be performed without using an LLM. For example, the prediction unit can input market value data of anomalies into a generation AI and have the generation AI execute a prediction. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, analysis unit, provision unit, and prediction 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 acquisition unit captures a person's movements using the camera 42 of the smart device 14, and identifies the position of each joint by executing a skeleton estimation algorithm using the control unit 46A. The analysis unit analyzes the skeleton information acquired by the specific processing unit 290 of the data processing device 12, for example, and detects abnormal values. The provision unit transmits the skeleton information and image information to the LLM by the specific processing unit 290 of the data processing device 12, for example, only when an abnormal value is detected. The prediction unit predicts the current situation based on the information transmitted from the provision unit by the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, analysis unit, provision unit, and prediction unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit captures a person's movements using the camera 42 of the smart glasses 214, and identifies the position of each joint by executing a skeleton estimation algorithm using the control unit 46A. The analysis unit analyzes the skeleton information acquired by, for example, the specific processing unit 290 of the data processing device 12 and detects abnormal values. For example, the provision unit passes the skeleton information and image information to the LLM via the specific processing unit 290 of the data processing device 12 only when an abnormal value is detected. For example, the prediction unit predicts the current situation based on the information passed from the provision unit via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, analysis unit, provision unit, and prediction unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit captures a person's movements using the camera 42 of the headset-type terminal 314 and identifies the position of each joint by executing a skeleton estimation algorithm using the control unit 46A. The analysis unit analyzes the skeleton information acquired by, for example, the specific processing unit 290 of the data processing device 12 and detects abnormal values. For example, only when an abnormal value is detected, the provision unit passes the skeleton information and image information to the LLM via the specific processing unit 290 of the data processing device 12. For example, the prediction unit predicts the current situation based on the information passed from the provision unit by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, analysis unit, provision unit, and prediction unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit captures the movement of a person using the camera 42 of the robot 414 and identifies the position of each joint by executing a skeleton estimation algorithm using the control unit 46A. The analysis unit analyzes the skeleton information acquired by, for example, the specific processing unit 290 of the data processing device 12 and detects abnormal values. For example, only when an abnormal value is detected, the provision unit passes the skeleton information and image information to the LLM via the specific processing unit 290 of the data processing device 12. For example, the prediction unit predicts the current situation based on the information passed from the provision unit via the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The acquisition unit acquires biometric information of the user and can improve the accuracy of acquiring skeletal information based on the acquired information. For example, the acquisition unit acquires biometric information such as heart rate and body temperature, and adjusts the timing of acquiring skeletal information based on the acquired information. The acquisition unit can also analyze the user's breathing pattern and acquire skeletal information in accordance with the breathing rhythm. Furthermore, the acquisition unit can measure the user's myoelectric potential and acquire skeletal information based on muscle movement. This makes it possible to acquire more accurate skeletal information by utilizing the biometric information.

[0109] The analysis unit can estimate the user's emotions and dynamically change the outlier detection algorithm based on the estimated user emotions. For example, the analysis unit relaxes the outlier detection criteria when the user is stressed. Alternatively, the analysis unit can use normal outlier detection criteria when the user is relaxed. Furthermore, the analysis unit can tighten the outlier detection criteria when the user is excited. This allows for more appropriate anomaly detection by adjusting the outlier detection algorithm according to the user's emotions.

[0110] When an abnormal value is detected, the providing unit can customize the information to be passed to the LLM by referring to the user's past behavioral history. For example, if the user has frequently performed a specific behavior in the past, the providing unit will prioritize passing information related to that behavior to the LLM. The providing unit can also identify behaviors that are likely to cause abnormalities from the user's past behavioral history and pass information related to those behaviors to the LLM. Furthermore, the providing unit can pass the most relevant information to the LLM based on the user's past behavioral history. This makes it possible to provide more appropriate information to the LLM by utilizing past behavioral history.

[0111] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the prediction unit can also provide a visually stimulating display method. In this way, visibility is improved by adjusting the display method according to the user's emotions.

[0112] The acquisition unit can optimize the method of acquiring skeletal information by taking into account the user's geographical location information. For example, when the user is outdoors, the acquisition unit performs filtering to remove environmental noise. Furthermore, when the user is in a specific area, the acquisition unit can select an acquisition method suitable for the environment of that area. Furthermore, when the user is moving, the acquisition unit can prioritize acquiring skeletal information related to movement. In this way, by taking into account the geographical location information, more accurate skeletal information can be acquired.

[0113] When detecting an anomaly, the analysis unit can analyze the user's social media activity to improve the accuracy of anomaly detection. For example, the analysis unit can detect anomalies related to the locations where the user checked in on social media. The analysis unit can also analyze the content of the user's social media posts to improve the accuracy of anomaly detection. Furthermore, the analysis unit can also improve the accuracy of anomaly detection by referring to the activities of the user's friends on social media. In this way, the accuracy of anomaly detection can be improved by analyzing social media activity.

[0114] The providing unit can estimate the user's emotions and determine the priority of information to be passed to the LLM based on the estimated user's emotions. For example, if the user is nervous, the providing unit can prioritize passing important information to the LLM. Also, if the user is relaxed, the providing unit can pass overall information evenly to the LLM. Furthermore, if the user is excited, the providing unit can prioritize passing detailed information to the LLM. In this way, by determining the priority of information according to the user's emotions, important information can be passed to the LLM with priority.

[0115] The prediction unit can customize the prediction algorithm by reflecting the user's past feedback when making predictions. For example, the prediction unit customizes the prediction algorithm based on feedback provided by the user in the past. The prediction unit can also preferentially use a specific prediction method based on the user's past feedback. Furthermore, the prediction unit can analyze the user's past feedback and suggest an optimal prediction method. In this way, the prediction algorithm can be customized by reflecting past feedback, thereby improving accuracy.

[0116] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring skeletal information based on the estimated user's emotions. For example, if the user is nervous, the acquisition unit temporarily delays acquiring skeletal information until the user relaxes. Also, if the user is relaxed, the acquisition unit can acquire skeletal information at a normal timing. Furthermore, if the user is excited, the acquisition unit can increase the frequency of acquiring skeletal information to collect more detailed data. In this way, by adjusting the timing of acquiring skeletal information according to the user's emotions, more appropriate data can be acquired.

[0117] When detecting an abnormal value, the analysis unit can improve the accuracy of anomaly detection by referring to related literature. For example, the analysis unit can optimize the anomaly detection algorithm based on the related literature. The analysis unit can also improve the accuracy of anomaly detection by referring to data in the related literature. Furthermore, the analysis unit can incorporate knowledge from the related literature and improve the anomaly detection algorithm. In this way, by referring to the related literature, the accuracy of anomaly detection can be improved.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The acquisition unit acquires skeletal information. For example, the acquisition unit uses a camera to capture a person's movements and identifies the position of each joint using a skeletal estimation algorithm. It is also possible to use multiple cameras to capture images from different angles to acquire more accurate skeletal information. It is also possible to acquire skeletal information using a motion capture sensor. Step 2: The analysis unit analyzes the skeletal information acquired by the acquisition unit and detects abnormal values. For example, it uses statistical methods based on past data or anomaly detection algorithms that use machine learning. It is also possible to analyze skeletal information in real time and detect abnormal values. Step 3: The provider passes skeletal and image information to the LLM based on the abnormal values ​​detected by the analyzer. For example, information can be passed to the LLM in real time only when an abnormal value is detected. It is also possible to pass information to the LLM, including past data. Step 4: The prediction unit predicts the current situation based on the information provided by the provision unit. For example, it predicts falls, poor posture, abnormal walking, etc. It can also learn from data on past abnormal situations and data on normal conditions, and predict the current situation in real time.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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. an acquisition unit for acquiring skeletal information; an analysis unit that analyzes the skeletal information acquired by the acquisition unit and detects abnormal values; a providing unit that transmits skeletal information or image information to the LLM based on the abnormal value detected by the analyzing unit; a prediction unit that predicts a current situation based on the information provided by the provision unit; Equipped with A system characterized by:

2. The acquisition unit A camera captures the person's movements, and a skeletal estimation algorithm is used to identify the position of each joint.

2. The system of claim 1.

3. The analysis unit Use statistical or machine learning anomaly detection algorithms based on historical data 2. The system of claim 1.

4. The providing unit Only when an abnormal value is detected, skeletal information or image information is passed to LLM.

2. The system of claim 1.

5. The prediction unit Predict falls, poor posture, and gait abnormalities 2. The system of claim 1.

6. The prediction unit Learn from past data on abnormal situations or normal conditions 2. The system of claim 1.

7. The acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring skeletal information based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit When acquiring skeletal information, the system analyzes the user's past movement history and selects the optimal acquisition method.

2. The system of claim 1.

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