System

Abstract

An object of a system according to an embodiment is to realize an AI that collects and analyzes information from a moment and a place and autonomously makes a determination.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a learning unit, and a determination unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The learning unit performs learning based on the data obtained by the analysis unit. The determination unit performs determination based on a result learned by the learning 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 technology, there is room for improvement in the collection and analysis of information from different moments and places, in order to realize AI that can make independent decisions like humans.

[0005] The system of the embodiment aims to realize AI that collects and analyzes information from moments and places and makes decisions independently. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a learning unit, and a determination unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The learning unit performs learning based on data obtained by the analysis unit. The determination unit makes a determination based on the results of learning by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze information from any moment and place, and realize AI that can make decisions independently. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A system according to an embodiment of the present invention enables AI to make independent decisions like humans. This system utilizes IoT technology to collect moment-to-moment human behavior and outcomes and stream them online. This enables AI to learn from large amounts of data and make more sophisticated decisions. For example, various sensors and devices are used to collect human behavior and outcomes in real time. For example, smartphones, wearable devices, and smart home appliances function as sensors to collect audio, video, motion, and location information. This information is then transmitted to a cloud server via the internet. The collected data is then analyzed on the cloud server to generate a dataset for AI learning. Based on this dataset, AI learns human judgment and behavior patterns in various situations. For example, it can learn appropriate responses and predicted outcomes in specific situations. Furthermore, the trained AI can make decisions and predictions in real time. For example, in a smart home, AI that learns the behavioral patterns of residents automatically adjusts lighting and air conditioning according to the residents' return home times. Additionally, in the medical field, AI can detect abnormalities based on a patient's vital signs and notify a doctor early on. This enables AI to make decisions independently, just like humans, and is expected to be applied in a variety of fields. For example, in smart cities, it can analyze traffic conditions in real time and suggest optimal routes. In the field of education, it can analyze students' learning progress and provide optimal learning plans for each individual student. In this way, by utilizing IoT technology to collect human behavior and results and allowing AI to learn, it becomes possible to make more advanced decisions. This makes it expected that AI systems will be applied in a variety of fields.

[0029] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a learning unit, and a judgment unit. The collection unit collects information. The information includes, for example, text data, numerical data, and image data, but is not limited to these examples. The collection unit can collect, for example, audio, video, motion, and location information. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed by, for example, statistical analysis or application of a machine learning algorithm, but is not limited to these examples. The analysis unit can analyze the collected data and extract patterns, for example. The learning unit performs learning based on the data obtained by the analysis unit. The learning can be performed by, for example, supervised learning, unsupervised learning, reinforcement learning, or other methods, but is not limited to these examples. The learning unit can perform learning based on, for example, the analyzed data. The judgment unit makes judgments based on the results of learning by the learning unit. The judgments can be performed by, for example, rule-based judgments or probabilistic judgments, but are not limited to these examples. The judgment unit can make judgments in real time based on the learning results, for example. As a result, the AI ​​system according to the embodiment collects information, analyzes it, learns from it, and makes decisions, enabling the AI ​​to make decisions independently like a human.

[0030] The collection unit can collect audio, video, motion, and location information. The collection unit can use, for example, a microphone to collect audio data. For example, the collection unit can use WAV, MP3, or the like as the audio file format. The collection unit can also use a camera to collect video data. For example, the collection unit can use MP4, AVI, or the like as the video file format. The collection unit can also use a motion sensor to collect motion data. For example, the collection unit can collect walking, running, or the like as the type of motion. The collection unit can also use GPS to collect location information. For example, the collection unit can have location information accuracy of several meters. This allows for the comprehensive acquisition of information necessary for AI learning by collecting a variety of data.

[0031] The analysis unit can analyze the collected data and extract patterns. The analysis unit can, for example, perform statistical analysis on the collected data. For example, the analysis unit can calculate the average value and standard deviation of the data. The analysis unit can also apply a machine learning algorithm to the collected data. For example, the analysis unit can classify the data using a clustering algorithm. The analysis unit can also extract temporal patterns from the collected data. For example, the analysis unit can perform time series analysis of the data to identify patterns in specific time periods. The analysis unit can also extract spatial patterns from the collected data. For example, the analysis unit can perform spatial analysis of the data to identify patterns in specific locations. This improves the learning accuracy of the AI ​​through data analysis and pattern extraction.

[0032] The learning unit can perform learning based on the analyzed data. For example, the learning unit can perform supervised learning based on the analyzed data. For example, the learning unit can train a model using labeled data. The learning unit can also perform unsupervised learning based on the analyzed data. For example, the learning unit can classify data using a clustering algorithm. The learning unit can also perform reinforcement learning based on the analyzed data. For example, the learning unit can learn optimal actions while the agent interacts with the environment. As a result, learning based on the analyzed data improves the AI's decision-making ability.

[0033] The judgment unit can make a judgment in real time based on the learning result. The judgment unit can, for example, make a rule-based judgment based on the learning result. For example, the judgment unit can make a judgment according to a predefined rule. The judgment unit can also make a probabilistic judgment based on the learning result. For example, the judgment unit can make a judgment using a probabilistic model. The judgment unit can also make a judgment in real time based on the learning result. For example, the judgment unit can make an instantaneous judgment based on the learning result. This makes it possible to respond quickly and appropriately by making a judgment in real time based on the learning result.

[0034] The determination unit can analyze the behavioral patterns of residents in a smart home and automatically adjust lighting and air conditioning according to the time the resident returns home. The determination unit can, for example, analyze the behavioral patterns of residents in a smart home. For example, the determination unit can identify the behavioral patterns based on the resident's past behavioral data. The determination unit can also automatically adjust lighting and air conditioning according to the time the resident returns home. For example, the determination unit can turn on the lights according to the time the resident returns home. The determination unit can also adjust the air conditioning according to the time the resident returns home. This makes it possible to provide a comfortable living environment by automatically adjusting lighting and air conditioning based on the resident's behavioral patterns.

[0035] In the medical field, the determination unit can detect abnormalities based on a patient's vital data and quickly notify a doctor. In the medical field, the determination unit can, for example, detect abnormalities based on a patient's vital data. For example, the determination unit can analyze vital data such as heart rate, blood pressure, and body temperature to detect abnormalities. Furthermore, the determination unit can quickly notify a doctor when an abnormality is detected. For example, the determination unit can send an alert to a doctor when an abnormality is detected. This allows for early detection of abnormalities based on a patient's vital data and notifying a doctor, enabling rapid medical response.

[0036] At the time of collection, the collection unit can analyze the user's past behavioral history and select an appropriate collection method. The collection unit can, for example, analyze the user's past behavioral history. For example, the collection unit can analyze past movement history and operation history. The collection unit can also select an appropriate collection method based on the user's past behavioral history. For example, the collection unit can prioritize data collection from devices that the user has frequently used in the past. The collection unit can also concentrate collection in specific time periods based on the user's past behavioral patterns. The collection unit can also strengthen data collection in specific locations based on the user's past behavioral history. In this way, the optimal collection method can be selected by analyzing the user's past behavioral history.

[0037] The collection unit can perform filtering based on the user's current activity status and environment at the time of collection. The collection unit can, for example, collect the user's current activity status. For example, when the user is exercising, the collection unit can prioritize collection of motion data and filter other data. Furthermore, when the user is stationary, the collection unit can prioritize collection of voice data and filter the motion data. Furthermore, when the user is in a meeting, the collection unit can limit collection of voice data and prioritize collection of location information. This makes it possible to collect appropriate data by filtering based on the user's current activity status and environment.

[0038] The collection unit can select an appropriate collection means according to the user's input method at the time of collection. The collection unit can, for example, detect the user's input method. For example, if the user is using voice input, the collection unit can prioritize collection of voice data. Furthermore, if the user is using text input, the collection unit can prioritize collection of text data. Furthermore, if the user is using image input, the collection unit can prioritize collection of image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method.

[0039] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, collect the user's geographical location information. For example, the collection unit can acquire the user's location information using GPS data. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to the location. When the user is traveling, the collection unit can prioritize collecting data related to the user's travel route. When the user is at home, the collection unit can prioritize collecting data from devices in the home. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0040] At the time of collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit can, for example, analyze the user's social media activities. For example, the collection unit can collect related data based on content posted by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also collect related data based on the user's check-in information on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback during collection. The collection unit can, for example, collect the user's past feedback. For example, the collection unit can collect user ratings, comments, survey results, etc. The collection unit can also customize the collection method based on the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize specific data collection means based on the user's past feedback. The collection unit can also customize the settings of the collection device based on the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit can, for example, evaluate the importance of the data. For example, the analysis unit can evaluate the importance based on the frequency or impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. The analysis unit can, for example, classify the categories of data. For example, the analysis unit can classify data into categories such as text data, image data, and numerical data. The analysis unit can also apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply an audio analysis algorithm to audio data. The analysis unit can also apply a video analysis algorithm to video data. The analysis unit can also apply a motion analysis algorithm to motion data. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of analysis is improved.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, collect the user's past analysis results. For example, the analysis unit can collect past reports, analysis logs, etc. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm based on the past analysis results. The analysis unit can also extract specific patterns from the past analysis results and reflect them in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit can, for example, evaluate the time when the data was collected. For example, the analysis unit can evaluate the time when the data was collected based on the timestamp or collection frequency of the data. The analysis unit can also determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. The analysis unit can also prioritize analyzing data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the time when the data was collected.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit can, for example, evaluate the relevance of the data. For example, the analysis unit can evaluate the relevance based on a correlation coefficient or a co-occurrence frequency. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit can, for example, evaluate the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise based on the user's occupation and past learning history. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can use a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also select appropriate terms according to the user's level of expertise. As a result, by adjusting the use of technical terms in the analysis according to the user's level of expertise, appropriate analysis results can be provided.

[0048] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can, for example, collect past learning data. For example, the learning unit can collect past training data sets, learning logs, etc. The learning unit can also optimize the learning algorithm by referring to the past learning data. For example, the learning unit can adjust the learning algorithm based on the past learning data. The learning unit can also extract specific patterns from the past learning data and reflect them in learning. The learning unit can also improve the accuracy of learning by referring to the past learning data. In this way, the learning algorithm is optimized by referring to the past learning data, and the accuracy of learning is improved.

[0049] The learning unit can update the learning data by reflecting user feedback during learning. The learning unit can, for example, collect user feedback. For example, the learning unit can collect user ratings, comments, survey results, etc. The learning unit can also update the learning data by reflecting user feedback. For example, the learning unit can adjust the learning data based on user feedback. The learning unit can also prioritize learning specific data from user feedback. The learning unit can also update the learning data by referring to user feedback. In this way, the learning data is updated by reflecting user feedback, and the accuracy of learning is improved.

[0050] The learning unit can improve the accuracy of learning by taking into account the interrelationships of data during learning. The learning unit can, for example, evaluate the interrelationships of data. For example, the learning unit can evaluate the interrelationships of data based on correlation coefficients or co-occurrence frequencies. The learning unit can also improve the accuracy of learning by taking into account the interrelationships of data. For example, the learning unit can analyze the interrelationships of data and reflect them in the learning algorithm. The learning unit can also select learning data based on the interrelationships of data. The learning unit can also improve the accuracy of learning by taking into account the interrelationships of data. In this way, the accuracy of learning is improved by taking into account the interrelationships of data.

[0051] During learning, the learning unit can weight the learning data based on the time when the data was collected. The learning unit can, for example, evaluate the time when the data was collected. For example, the learning unit can evaluate the time when the data was collected based on the timestamp or collection frequency of the data. The learning unit can also weight the learning data based on the time when the data was collected. For example, the learning unit can assign a higher weight to the most recent data. The learning unit can also assign a lower weight to older data. The learning unit can also assign an appropriate weight to data collected during a specific period. In this way, weighting the learning data based on the time when the data was collected improves the accuracy of learning.

[0052] During learning, the learning unit can integrate information from different data sources to expand the learning data. The learning unit can, for example, collect information from different data sources. For example, the learning unit can collect sensor data, data from an external API, and the like. The learning unit can also integrate information from different data sources to expand the learning data. For example, the learning unit can integrate audio data and video data to learn. The learning unit can also integrate motion data and location information to learn. The learning unit can also integrate social media data and sensor data to learn. In this way, by integrating information from different data sources, the learning data is expanded and the accuracy of learning is improved.

[0053] During learning, the learning unit can adjust the learning algorithm according to the user's level of expertise. The learning unit can, for example, evaluate the user's level of expertise. For example, the learning unit can evaluate the user's level of expertise based on the user's occupation and past learning history. The learning unit can also adjust the learning algorithm according to the user's level of expertise. For example, if the user has specialized knowledge, the learning unit can prioritize learning specialized data. Furthermore, if the user does not have specialized knowledge, the learning unit can prioritize learning basic data. The learning unit can also adjust the learning algorithm according to the user's level of expertise. As a result, appropriate learning is possible by adjusting the learning algorithm according to the user's level of expertise.

[0054] When making a judgment, the judgment unit can analyze the user's past behavioral patterns and select the optimal judgment method. The judgment unit can, for example, analyze the user's past behavioral patterns. For example, the judgment unit can analyze daily behavioral patterns and behavioral patterns during specific events. The judgment unit can also select the optimal judgment method based on the user's past behavioral patterns. For example, the judgment unit can select the optimal judgment method based on the user's past behavioral patterns. The judgment unit can also prioritize a specific judgment method based on the user's past behavioral patterns. The judgment unit can also customize the judgment method by referring to the user's past behavioral patterns. In this way, the optimal judgment method can be selected by analyzing the user's past behavioral patterns.

[0055] The determination unit can customize the determination means based on the user's current situation at the time of determination. The determination unit can, for example, collect the user's current situation. For example, the determination unit can collect the user's current work content or exercise status. The determination unit can also customize the determination means based on the user's current situation. For example, the determination unit can provide a simple determination means when the user is exercising. The determination unit can also provide a detailed determination means when the user is stationary. The determination unit can also provide a quiet determination means when the user is in a meeting. In this way, by customizing the determination means based on the user's current situation, appropriate determination is possible.

[0056] The judgment unit can improve the judgment method by reflecting user feedback when making a judgment. The judgment unit can, for example, collect user feedback. For example, the judgment unit can collect user ratings, comments, survey results, etc. The judgment unit can also improve the judgment method by reflecting user feedback. For example, the judgment unit can adjust the judgment method based on user feedback. The judgment unit can also prioritize a specific judgment means based on user feedback. The judgment unit can also improve the judgment method by referring to user feedback. In this way, the judgment method can be improved by reflecting user feedback, making it possible to make an appropriate judgment.

[0057] When making a judgment, the judgment unit can select an optimal judgment method by taking into consideration the user's geographical location information. The judgment unit can, for example, collect the user's geographical location information. For example, the judgment unit can acquire the user's location information using GPS data. The judgment unit can also select an optimal judgment method based on the user's geographical location information. For example, when the user is in a specific location, the judgment unit can select a judgment method related to the location. When the user is traveling, the judgment unit can select a judgment method related to the user's travel route. When the user is at home, the judgment unit can select a judgment method related to a device in the home. In this way, the optimal judgment method can be selected by taking into consideration the user's geographical location information.

[0058] When making a judgment, the judgment unit can analyze the user's social media activity and suggest a judgment means. The judgment unit can, for example, analyze the user's social media activity. For example, the judgment unit can suggest a relevant judgment means based on the content posted by the user on social media. The judgment unit can also suggest a relevant judgment means by referring to the activity of the user's friends on social media. The judgment unit can also suggest a relevant judgment means based on the user's check-in information on social media. In this way, an appropriate judgment means can be suggested by analyzing the user's social media activity.

[0059] The determination unit can customize the determination method by reflecting the user's past feedback when making a determination. The determination unit can, for example, collect the user's past feedback. For example, the determination unit can collect user ratings, comments, survey results, etc. The determination unit can also customize the determination method by reflecting the user's past feedback. For example, the determination unit can adjust the determination method based on the user's past feedback. The determination unit can also prioritize a specific determination means based on the user's past feedback. The determination unit can also customize the determination method by referring to the user's feedback. In this way, the determination method can be customized by reflecting the user's past feedback, enabling appropriate determination.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When collecting data, the collection unit can analyze the user's past behavioral history and select an appropriate collection method. For example, the collection unit can analyze the user's past movement history and operation history. The collection unit can also select an appropriate collection method based on the user's past behavioral history. For example, the collection unit can prioritize data collection from devices that the user has frequently used in the past. The collection unit can also concentrate collection during specific time periods based on the user's past behavioral patterns. The collection unit can also strengthen data collection in specific locations based on the user's past behavioral history. This makes it possible to select the optimal collection method by analyzing the user's past behavioral history.

[0062] The collection unit can perform filtering based on the user's current activity status and environment at the time of collection. For example, the collection unit can collect the user's current activity status. For example, when the user is exercising, the collection unit can prioritize collection of motion data and filter other data. Furthermore, when the user is stationary, the collection unit can prioritize collection of voice data and filter the motion data. Furthermore, when the user is in a meeting, the collection unit can limit collection of voice data and prioritize collection of location information. This makes it possible to collect appropriate data by filtering based on the user's current activity status and environment.

[0063] The collection unit can select an appropriate collection means according to the user's input method at the time of collection. For example, the collection unit can detect the user's input method. For example, if the user is using voice input, the collection unit can prioritize collection of voice data. Furthermore, if the user is using text input, the collection unit can prioritize collection of text data. Furthermore, if the user is using image input, the collection unit can prioritize collection of image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method.

[0064] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can evaluate the importance of the data. For example, the analysis unit can evaluate the importance based on the frequency or impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0065] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can classify the categories of data. For example, the analysis unit can classify data into categories such as text data, image data, and numerical data. The analysis unit can also apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply an audio analysis algorithm to audio data. The analysis unit can also apply a video analysis algorithm to video data. The analysis unit can also apply a motion analysis algorithm to motion data. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of analysis is improved.

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

[0067] Step 1: The collection unit collects information, such as text data, numerical data, image data, audio data, video data, motion data, and location data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis can be performed, for example, by statistical analysis or by applying machine learning algorithms to extract patterns from the collected data. Step 3: The learning unit performs learning based on the data obtained by the analysis unit. The learning is performed by a method such as supervised learning, unsupervised learning, or reinforcement learning. Step 4: The judgment unit makes a judgment based on the results learned by the learning unit. The judgment is made using methods such as rule-based judgment or probabilistic judgment, and can make a judgment in real time based on the learning results.

[0068] (Example 2) A system according to an embodiment of the present invention enables AI to make independent decisions like humans. This system utilizes IoT technology to collect moment-to-moment human behavior and outcomes and stream them online. This enables AI to learn from large amounts of data and make more sophisticated decisions. For example, various sensors and devices are used to collect human behavior and outcomes in real time. For example, smartphones, wearable devices, and smart home appliances function as sensors to collect audio, video, motion, and location information. This information is then transmitted to a cloud server via the internet. The collected data is then analyzed on the cloud server to generate a dataset for AI learning. Based on this dataset, AI learns human judgment and behavior patterns in various situations. For example, it can learn appropriate responses and predicted outcomes in specific situations. Furthermore, the trained AI can make decisions and predictions in real time. For example, in a smart home, AI that learns the behavioral patterns of residents automatically adjusts lighting and air conditioning according to the residents' return home times. Additionally, in the medical field, AI can detect abnormalities based on a patient's vital signs and notify a doctor early on. This enables AI to make decisions independently, just like humans, and is expected to be applied in a variety of fields. For example, in smart cities, it can analyze traffic conditions in real time and suggest optimal routes. In the field of education, it can analyze students' learning progress and provide optimal learning plans for each individual student. In this way, by utilizing IoT technology to collect human behavior and results and allowing AI to learn, it becomes possible to make more advanced decisions. This makes it expected that AI systems will be applied in a variety of fields.

[0069] The AI ​​system according to the embodiment includes a collection unit, an analysis unit, a learning unit, and a judgment unit. The collection unit collects information. The information includes, for example, text data, numerical data, and image data, but is not limited to these examples. The collection unit can collect, for example, audio, video, motion, and location information. The analysis unit analyzes the information collected by the collection unit. The analysis can be performed by, for example, statistical analysis or application of a machine learning algorithm, but is not limited to these examples. The analysis unit can analyze the collected data and extract patterns, for example. The learning unit performs learning based on the data obtained by the analysis unit. The learning can be performed by, for example, supervised learning, unsupervised learning, reinforcement learning, or other methods, but is not limited to these examples. The learning unit can perform learning based on, for example, the analyzed data. The judgment unit makes judgments based on the results of learning by the learning unit. The judgments can be performed by, for example, rule-based judgments or probabilistic judgments, but are not limited to these examples. The judgment unit can make judgments in real time based on the learning results, for example. As a result, the AI ​​system according to the embodiment collects information, analyzes it, learns from it, and makes decisions, enabling the AI ​​to make decisions independently like a human.

[0070] The collection unit can collect audio, video, motion, and location information. The collection unit can use, for example, a microphone to collect audio data. For example, the collection unit can use WAV, MP3, or the like as the audio file format. The collection unit can also use a camera to collect video data. For example, the collection unit can use MP4, AVI, or the like as the video file format. The collection unit can also use a motion sensor to collect motion data. For example, the collection unit can collect walking, running, or the like as the type of motion. The collection unit can also use GPS to collect location information. For example, the collection unit can have location information accuracy of several meters. This allows for the comprehensive acquisition of information necessary for AI learning by collecting a variety of data.

[0071] The analysis unit can analyze the collected data and extract patterns. The analysis unit can, for example, perform statistical analysis on the collected data. For example, the analysis unit can calculate the average value and standard deviation of the data. The analysis unit can also apply a machine learning algorithm to the collected data. For example, the analysis unit can classify the data using a clustering algorithm. The analysis unit can also extract temporal patterns from the collected data. For example, the analysis unit can perform time series analysis of the data to identify patterns in specific time periods. The analysis unit can also extract spatial patterns from the collected data. For example, the analysis unit can perform spatial analysis of the data to identify patterns in specific locations. This improves the learning accuracy of the AI ​​through data analysis and pattern extraction.

[0072] The learning unit can perform learning based on the analyzed data. For example, the learning unit can perform supervised learning based on the analyzed data. For example, the learning unit can train a model using labeled data. The learning unit can also perform unsupervised learning based on the analyzed data. For example, the learning unit can classify data using a clustering algorithm. The learning unit can also perform reinforcement learning based on the analyzed data. For example, the learning unit can learn optimal actions while the agent interacts with the environment. As a result, learning based on the analyzed data improves the AI's decision-making ability.

[0073] The judgment unit can make a judgment in real time based on the learning result. The judgment unit can, for example, make a rule-based judgment based on the learning result. For example, the judgment unit can make a judgment according to a predefined rule. The judgment unit can also make a probabilistic judgment based on the learning result. For example, the judgment unit can make a judgment using a probabilistic model. The judgment unit can also make a judgment in real time based on the learning result. For example, the judgment unit can make an instantaneous judgment based on the learning result. This makes it possible to respond quickly and appropriately by making a judgment in real time based on the learning result.

[0074] The determination unit can analyze the behavioral patterns of residents in a smart home and automatically adjust lighting and air conditioning according to the time the resident returns home. The determination unit can, for example, analyze the behavioral patterns of residents in a smart home. For example, the determination unit can identify the behavioral patterns based on the resident's past behavioral data. The determination unit can also automatically adjust lighting and air conditioning according to the time the resident returns home. For example, the determination unit can turn on the lights according to the time the resident returns home. The determination unit can also adjust the air conditioning according to the time the resident returns home. This makes it possible to provide a comfortable living environment by automatically adjusting lighting and air conditioning based on the resident's behavioral patterns.

[0075] In the medical field, the determination unit can detect abnormalities based on a patient's vital data and quickly notify a doctor. In the medical field, the determination unit can, for example, detect abnormalities based on a patient's vital data. For example, the determination unit can analyze vital data such as heart rate, blood pressure, and body temperature to detect abnormalities. Furthermore, the determination unit can quickly notify a doctor when an abnormality is detected. For example, the determination unit can send an alert to a doctor when an abnormality is detected. This allows for early detection of abnormalities based on a patient's vital data and notifying a doctor, enabling rapid medical response.

[0076] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. The collection unit can, for example, estimate the user's emotion. For example, the collection unit can estimate the user's emotion using facial expression analysis or voice analysis. The collection unit can also adjust the type of data to be collected based on the estimated user's emotion. For example, if the user is feeling stressed, the collection unit can prioritize collection of voice data and identify the cause of the stress. If the user is relaxed, the collection unit can prioritize collection of video data and analyze the factors behind the relaxation. If the user is excited, the collection unit can prioritize collection of motion data and identify the trigger for the excitement. This allows for more appropriate data collection by adjusting the type of data to be collected according to the user's emotion.

[0077] At the time of collection, the collection unit can analyze the user's past behavioral history and select an appropriate collection method. The collection unit can, for example, analyze the user's past behavioral history. For example, the collection unit can analyze past movement history and operation history. The collection unit can also select an appropriate collection method based on the user's past behavioral history. For example, the collection unit can prioritize data collection from devices that the user has frequently used in the past. The collection unit can also concentrate collection in specific time periods based on the user's past behavioral patterns. The collection unit can also strengthen data collection in specific locations based on the user's past behavioral history. In this way, the optimal collection method can be selected by analyzing the user's past behavioral history.

[0078] The collection unit can perform filtering based on the user's current activity status and environment at the time of collection. The collection unit can, for example, collect the user's current activity status. For example, when the user is exercising, the collection unit can prioritize collection of motion data and filter other data. Furthermore, when the user is stationary, the collection unit can prioritize collection of voice data and filter the motion data. Furthermore, when the user is in a meeting, the collection unit can limit collection of voice data and prioritize collection of location information. This makes it possible to collect appropriate data by filtering based on the user's current activity status and environment.

[0079] The collection unit can select an appropriate collection means according to the user's input method at the time of collection. The collection unit can, for example, detect the user's input method. For example, if the user is using voice input, the collection unit can prioritize collection of voice data. Furthermore, if the user is using text input, the collection unit can prioritize collection of text data. Furthermore, if the user is using image input, the collection unit can prioritize collection of image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method.

[0080] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. The collection unit can, for example, estimate the user's emotion. For example, the collection unit can estimate the user's emotion using facial expression analysis or voice analysis. The collection unit can also determine the priority of data to be collected based on the estimated user's emotion. For example, the collection unit can prioritize collecting stress-related data when the user is feeling stressed. The collection unit can prioritize collecting relaxation-related data when the user is relaxed. The collection unit can prioritize collecting excitement-related data when the user is excited. In this way, by determining the priority of data based on the user's emotion, important data can be collected preferentially.

[0081] During collection, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, collect the user's geographical location information. For example, the collection unit can acquire the user's location information using GPS data. The collection unit can also prioritize collecting highly relevant data based on the user's geographical location information. For example, when the user is in a specific location, the collection unit can prioritize collecting data related to the location. When the user is traveling, the collection unit can prioritize collecting data related to the user's travel route. When the user is at home, the collection unit can prioritize collecting data from devices in the home. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0082] At the time of collection, the collection unit can analyze the user's social media activities and collect related data. The collection unit can, for example, analyze the user's social media activities. For example, the collection unit can collect related data based on content posted by the user on social media. The collection unit can also collect related data by referring to the activities of the user's friends on social media. The collection unit can also collect related data based on the user's check-in information on social media. In this way, related data can be efficiently collected by analyzing the user's social media activities.

[0083] The collection unit can customize the collection method by reflecting the user's past feedback during collection. The collection unit can, for example, collect the user's past feedback. For example, the collection unit can collect user ratings, comments, survey results, etc. The collection unit can also customize the collection method based on the user's past feedback. For example, the collection unit can adjust the collection method based on feedback provided by the user in the past. The collection unit can also prioritize specific data collection means based on the user's past feedback. The collection unit can also customize the settings of the collection device based on the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0084] The analysis unit can estimate the user's emotion and adjust the method of presentation of the analysis based on the estimated user's emotion. The analysis unit can, for example, estimate the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression analysis or voice analysis. The analysis unit can also adjust the method of presentation of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a simple and highly visible analysis result when the user is feeling stressed. The analysis unit can provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually stimulating analysis result when the user is excited. In this way, by adjusting the method of presentation of the analysis based on the user's emotion, more appropriate analysis results can be provided.

[0085] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit can, for example, evaluate the importance of the data. For example, the analysis unit can evaluate the importance based on the frequency or impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0086] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. The analysis unit can, for example, classify the categories of data. For example, the analysis unit can classify data into categories such as text data, image data, and numerical data. The analysis unit can also apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply an audio analysis algorithm to audio data. The analysis unit can also apply a video analysis algorithm to video data. The analysis unit can also apply a motion analysis algorithm to motion data. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of analysis is improved.

[0087] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, collect the user's past analysis results. For example, the analysis unit can collect past reports, analysis logs, etc. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm based on the past analysis results. The analysis unit can also extract specific patterns from the past analysis results and reflect them in the analysis. The analysis unit can also improve the accuracy of the analysis by referring to the past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0088] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can, for example, estimate the user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression analysis or voice analysis. The analysis unit can also adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can provide a short and to-the-point analysis result when the user is in a hurry. The analysis unit can provide a detailed analysis result when the user is relaxed. The analysis unit can provide a visually stimulating analysis result when the user is excited. In this way, by adjusting the length of the analysis based on the user's emotion, it is possible to provide an appropriate analysis result.

[0089] During analysis, the analysis unit can determine the priority of analysis based on the time when the data was collected. The analysis unit can, for example, evaluate the time when the data was collected. For example, the analysis unit can evaluate the time when the data was collected based on the timestamp or collection frequency of the data. The analysis unit can also determine the priority of analysis based on the time when the data was collected. For example, the analysis unit can prioritize analyzing the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. The analysis unit can also prioritize analyzing data collected during a specific period. This enables efficient analysis by determining the priority of analysis based on the time when the data was collected.

[0090] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit can, for example, evaluate the relevance of the data. For example, the analysis unit can evaluate the relevance based on a correlation coefficient or a co-occurrence frequency. The analysis unit can also adjust the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also optimize the order of analysis based on the relevance of the data. As a result, adjusting the order of analysis based on the relevance of the data enables efficient analysis.

[0091] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit can, for example, evaluate the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise based on the user's occupation and past learning history. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can use a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also select appropriate terms according to the user's level of expertise. As a result, by adjusting the use of technical terms in the analysis according to the user's level of expertise, appropriate analysis results can be provided.

[0092] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. The learning unit can, for example, estimate the user's emotions. For example, the learning unit can estimate the user's emotions using facial expression analysis or voice analysis. The learning unit can also select learning data based on the estimated user's emotions. For example, if the user is feeling stressed, the learning unit can prioritize learning stress-related data. If the user is relaxed, the learning unit can prioritize learning relaxation-related data. If the user is excited, the learning unit can prioritize learning excitement-related data. This enables more appropriate learning by selecting learning data based on the user's emotions.

[0093] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can, for example, collect past learning data. For example, the learning unit can collect past training data sets, learning logs, etc. The learning unit can also optimize the learning algorithm by referring to the past learning data. For example, the learning unit can adjust the learning algorithm based on the past learning data. The learning unit can also extract specific patterns from the past learning data and reflect them in learning. The learning unit can also improve the accuracy of learning by referring to the past learning data. In this way, the learning algorithm is optimized by referring to the past learning data, and the accuracy of learning is improved.

[0094] The learning unit can update the learning data by reflecting user feedback during learning. The learning unit can, for example, collect user feedback. For example, the learning unit can collect user ratings, comments, survey results, etc. The learning unit can also update the learning data by reflecting user feedback. For example, the learning unit can adjust the learning data based on user feedback. The learning unit can also prioritize learning specific data from user feedback. The learning unit can also update the learning data by referring to user feedback. In this way, the learning data is updated by reflecting user feedback, and the accuracy of learning is improved.

[0095] The learning unit can improve the accuracy of learning by taking into account the interrelationships of data during learning. The learning unit can, for example, evaluate the interrelationships of data. For example, the learning unit can evaluate the interrelationships of data based on correlation coefficients or co-occurrence frequencies. The learning unit can also improve the accuracy of learning by taking into account the interrelationships of data. For example, the learning unit can analyze the interrelationships of data and reflect them in the learning algorithm. The learning unit can also select learning data based on the interrelationships of data. The learning unit can also improve the accuracy of learning by taking into account the interrelationships of data. In this way, the accuracy of learning is improved by taking into account the interrelationships of data.

[0096] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user's emotions. The learning unit can, for example, estimate the user's emotions. For example, the learning unit can estimate the user's emotions using facial expression analysis or voice analysis. The learning unit can also adjust the frequency of learning based on the estimated user's emotions. For example, the learning unit can reduce the frequency of learning when the user is feeling stressed. The learning unit can increase the frequency of learning when the user is relaxed. The learning unit can also adjust the frequency of learning when the user is excited. In this way, adjusting the frequency of learning based on the user's emotions enables appropriate learning.

[0097] During learning, the learning unit can weight the learning data based on the time when the data was collected. The learning unit can, for example, evaluate the time when the data was collected. For example, the learning unit can evaluate the time when the data was collected based on the timestamp or collection frequency of the data. The learning unit can also weight the learning data based on the time when the data was collected. For example, the learning unit can assign a higher weight to the most recent data. The learning unit can also assign a lower weight to older data. The learning unit can also assign an appropriate weight to data collected during a specific period. In this way, weighting the learning data based on the time when the data was collected improves the accuracy of learning.

[0098] During learning, the learning unit can integrate information from different data sources to expand the learning data. The learning unit can, for example, collect information from different data sources. For example, the learning unit can collect sensor data, data from an external API, and the like. The learning unit can also integrate information from different data sources to expand the learning data. For example, the learning unit can integrate audio data and video data to learn. The learning unit can also integrate motion data and location information to learn. The learning unit can also integrate social media data and sensor data to learn. In this way, by integrating information from different data sources, the learning data is expanded and the accuracy of learning is improved.

[0099] During learning, the learning unit can adjust the learning algorithm according to the user's level of expertise. The learning unit can, for example, evaluate the user's level of expertise. For example, the learning unit can evaluate the user's level of expertise based on the user's occupation and past learning history. The learning unit can also adjust the learning algorithm according to the user's level of expertise. For example, if the user has specialized knowledge, the learning unit can prioritize learning specialized data. Furthermore, if the user does not have specialized knowledge, the learning unit can prioritize learning basic data. The learning unit can also adjust the learning algorithm according to the user's level of expertise. As a result, appropriate learning is possible by adjusting the learning algorithm according to the user's level of expertise.

[0100] The determination unit can estimate the user's emotion and adjust the determination method based on the estimated user's emotion. The determination unit can, for example, estimate the user's emotion. For example, the determination unit can estimate the user's emotion using facial expression analysis or voice analysis. The determination unit can also adjust the determination method based on the estimated user's emotion. For example, the determination unit can provide a simple determination method when the user is feeling stressed. The determination unit can also provide a detailed determination method when the user is relaxed. The determination unit can also provide a visually stimulating determination method when the user is excited. In this way, by adjusting the determination method based on the user's emotion, appropriate judgment is possible.

[0101] When making a judgment, the judgment unit can analyze the user's past behavioral patterns and select the optimal judgment method. The judgment unit can, for example, analyze the user's past behavioral patterns. For example, the judgment unit can analyze daily behavioral patterns and behavioral patterns during specific events. The judgment unit can also select the optimal judgment method based on the user's past behavioral patterns. For example, the judgment unit can select the optimal judgment method based on the user's past behavioral patterns. The judgment unit can also prioritize a specific judgment method based on the user's past behavioral patterns. The judgment unit can also customize the judgment method by referring to the user's past behavioral patterns. In this way, the optimal judgment method can be selected by analyzing the user's past behavioral patterns.

[0102] The determination unit can customize the determination means based on the user's current situation at the time of determination. The determination unit can, for example, collect the user's current situation. For example, the determination unit can collect the user's current work content or exercise status. The determination unit can also customize the determination means based on the user's current situation. For example, the determination unit can provide a simple determination means when the user is exercising. The determination unit can also provide a detailed determination means when the user is stationary. The determination unit can also provide a quiet determination means when the user is in a meeting. In this way, by customizing the determination means based on the user's current situation, appropriate determination is possible.

[0103] The judgment unit can improve the judgment method by reflecting user feedback when making a judgment. The judgment unit can, for example, collect user feedback. For example, the judgment unit can collect user ratings, comments, survey results, etc. The judgment unit can also improve the judgment method by reflecting user feedback. For example, the judgment unit can adjust the judgment method based on user feedback. The judgment unit can also prioritize a specific judgment means based on user feedback. The judgment unit can also improve the judgment method by referring to user feedback. In this way, the judgment method can be improved by reflecting user feedback, making it possible to make an appropriate judgment.

[0104] The determination unit can estimate the user's emotion and determine the priority of determination based on the estimated user's emotion. The determination unit can, for example, estimate the user's emotion. For example, the determination unit can estimate the user's emotion using facial expression analysis or voice analysis. The determination unit can also determine the priority of determination based on the estimated user's emotion. For example, the determination unit can prioritize stress-related determinations when the user is feeling stressed. The determination unit can also prioritize relaxation-related determinations when the user is relaxed. The determination unit can also prioritize excitement-related determinations when the user is excited. In this way, by determining the priority of determinations based on the user's emotion, important decisions can be made with priority.

[0105] When making a judgment, the judgment unit can select an optimal judgment method by taking into consideration the user's geographical location information. The judgment unit can, for example, collect the user's geographical location information. For example, the judgment unit can acquire the user's location information using GPS data. The judgment unit can also select an optimal judgment method based on the user's geographical location information. For example, when the user is in a specific location, the judgment unit can select a judgment method related to the location. When the user is traveling, the judgment unit can select a judgment method related to the user's travel route. When the user is at home, the judgment unit can select a judgment method related to a device in the home. In this way, the optimal judgment method can be selected by taking into consideration the user's geographical location information.

[0106] When making a judgment, the judgment unit can analyze the user's social media activity and suggest a judgment means. The judgment unit can, for example, analyze the user's social media activity. For example, the judgment unit can suggest a relevant judgment means based on the content posted by the user on social media. The judgment unit can also suggest a relevant judgment means by referring to the activity of the user's friends on social media. The judgment unit can also suggest a relevant judgment means based on the user's check-in information on social media. In this way, an appropriate judgment means can be suggested by analyzing the user's social media activity.

[0107] The determination unit can customize the determination method by reflecting the user's past feedback when making a determination. The determination unit can, for example, collect the user's past feedback. For example, the determination unit can collect user ratings, comments, survey results, etc. The determination unit can also customize the determination method by reflecting the user's past feedback. For example, the determination unit can adjust the determination method based on the user's past feedback. The determination unit can also prioritize a specific determination means based on the user's past feedback. The determination unit can also customize the determination method by referring to the user's feedback. In this way, the determination method can be customized by reflecting the user's past feedback, enabling appropriate determination. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and judgment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects audio, video, motion, and location information using the camera 42 and microphone 38B of the smart device 14 and transmits the collected information to the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and extracts patterns. The learning unit performs learning by the specific processing unit 290 of the data processing device 12 based on the analyzed data. The judgment unit performs judgment in real time by the specific processing unit 290 of the data processing device 12 based on the learning results. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and judgment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects audio, video, motion, and position information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected information to the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and extracts patterns. The learning unit performs learning by the specific processing unit 290 of the data processing device 12 based on the analyzed data. The judgment unit performs judgment in real time by the specific processing unit 290 of the data processing device 12 based on the learning results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and judgment unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects audio, video, movement, position information, etc. using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the collected information to the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and extracts patterns. The learning unit performs learning by the specific processing unit 290 of the data processing device 12 based on the analyzed data. The judgment unit makes a judgment in real time by the specific processing unit 290 of the data processing device 12 based on the learning results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, learning unit, and judgment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects audio, video, movement, position information, etc. using the camera 42 and microphone 238 of the robot 414 and transmits the collected information to the data processing device 12. The analysis unit analyzes the collected data by the specific processing unit 290 of the data processing device 12 and extracts patterns. The learning unit performs learning by the specific processing unit 290 of the data processing device 12 based on the analyzed data. The judgment unit makes a judgment in real time by the specific processing unit 290 of the data processing device 12 based on the learning results.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The collection unit can estimate the user's emotions and adjust the type of data to be collected based on the estimated user's emotions. For example, the collection unit can estimate the user's emotions using facial expression analysis or voice analysis. The collection unit can also adjust the type of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collection of voice data and identify the cause of the stress. If the user is relaxed, the collection unit can prioritize collection of video data and analyze the factors that cause relaxation. If the user is excited, the collection unit can prioritize collection of motion data and identify the trigger for the excitement. This allows for more appropriate data collection by adjusting the type of data to be collected according to the user's emotions.

[0110] The analysis unit can estimate the user's emotion and adjust the method of presentation of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion using facial expression analysis or voice analysis. The analysis unit can also adjust the method of presentation of the analysis based on the estimated user's emotion. For example, if the user is feeling stressed, the analysis unit can provide a simple and highly visible analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. If the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, by adjusting the method of presentation of the analysis based on the user's emotion, more appropriate analysis results can be provided.

[0111] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, the learning unit can estimate the user's emotions using facial expression analysis or voice analysis. The learning unit can also select learning data based on the estimated user's emotions. For example, if the user is feeling stressed, the learning unit can prioritize learning stress-related data. If the user is relaxed, the learning unit can prioritize learning relaxation-related data. If the user is excited, the learning unit can prioritize learning excitement-related data. This allows for more appropriate learning by selecting learning data based on the user's emotions.

[0112] The determination unit can estimate the user's emotion and adjust the determination method based on the estimated user's emotion. For example, the determination unit can estimate the user's emotion using facial expression analysis or voice analysis. The determination unit can also adjust the determination method based on the estimated user's emotion. For example, the determination unit can provide a simple determination method when the user is feeling stressed. The determination unit can also provide a detailed determination method when the user is relaxed. The determination unit can also provide a visually stimulating determination method when the user is excited. In this way, by adjusting the determination method based on the user's emotion, appropriate judgment is possible.

[0113] The determination unit can estimate the user's emotions and determine the priority of determination based on the estimated user's emotions. For example, the determination unit can estimate the user's emotions using facial expression analysis or voice analysis. The determination unit can also determine the priority of determination based on the estimated user's emotions. For example, if the user is feeling stressed, the determination unit can prioritize stress-related determinations. If the user is relaxed, the determination unit can prioritize relaxation-related determinations. If the user is excited, the determination unit can prioritize excitement-related determinations. Thus, by determining the priority of determination based on the user's emotions, important decisions can be made with priority.

[0114] When collecting data, the collection unit can analyze the user's past behavioral history and select an appropriate collection method. For example, the collection unit can analyze the user's past movement history and operation history. The collection unit can also select an appropriate collection method based on the user's past behavioral history. For example, the collection unit can prioritize data collection from devices that the user has frequently used in the past. The collection unit can also concentrate collection during specific time periods based on the user's past behavioral patterns. The collection unit can also strengthen data collection in specific locations based on the user's past behavioral history. This makes it possible to select the optimal collection method by analyzing the user's past behavioral history.

[0115] The collection unit can perform filtering based on the user's current activity status and environment at the time of collection. For example, the collection unit can collect the user's current activity status. For example, when the user is exercising, the collection unit can prioritize collection of motion data and filter other data. Furthermore, when the user is stationary, the collection unit can prioritize collection of voice data and filter the motion data. Furthermore, when the user is in a meeting, the collection unit can limit collection of voice data and prioritize collection of location information. This makes it possible to collect appropriate data by filtering based on the user's current activity status and environment.

[0116] The collection unit can select an appropriate collection means according to the user's input method at the time of collection. For example, the collection unit can detect the user's input method. For example, if the user is using voice input, the collection unit can prioritize collection of voice data. Furthermore, if the user is using text input, the collection unit can prioritize collection of text data. Furthermore, if the user is using image input, the collection unit can prioritize collection of image data. This enables efficient data collection by selecting the optimal collection means according to the user's input method.

[0117] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can evaluate the importance of the data. For example, the analysis unit can evaluate the importance based on the frequency or impact of the data. The analysis unit can also adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0118] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can classify the categories of data. For example, the analysis unit can classify data into categories such as text data, image data, and numerical data. The analysis unit can also apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply an audio analysis algorithm to audio data. The analysis unit can also apply a video analysis algorithm to video data. The analysis unit can also apply a motion analysis algorithm to motion data. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of analysis is improved.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects information, such as text data, numerical data, image data, audio data, video data, motion data, and location data. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis can be performed, for example, by statistical analysis or by applying machine learning algorithms to extract patterns from the collected data. Step 3: The learning unit performs learning based on the data obtained by the analysis unit. The learning is performed by a method such as supervised learning, unsupervised learning, or reinforcement learning. Step 4: The judgment unit makes a judgment based on the results learned by the learning unit. The judgment is made using methods such as rule-based judgment or probabilistic judgment, and can make a judgment in real time based on the learning results.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information; an analysis unit that analyzes the information collected by the collection unit; a learning unit that performs learning based on the data obtained by the analysis unit; a determination unit that makes a determination based on the results of learning by the learning unit; Equipped with A system characterized by:

2. The collecting unit Collecting audio, video, motion, and location information 2. The system of claim 1.

3. The analysis unit Analyze the collected data and extract patterns 2. The system of claim 1.

4. The learning unit Learning based on analyzed data 2. The system of claim 1.

5. The determination unit Make real-time decisions based on learning results 2. The system of claim 1.

6. The determination unit In smart homes, analyzing residents' behavioral patterns and automatically adjusting lighting and air conditioning according to when residents return home 2. The system of claim 1.

7. The determination unit In the medical field, detecting abnormalities based on a patient's vital signs and notifying doctors promptly 2. The system of claim 1.

8. The collecting unit Inferring user sentiment and adjusting the type of data collected based on the estimated user sentiment 2. The system of claim 1.

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