system

Abstract

The system according to the embodiment aims to collect data in real time and retrain the model based on the analysis results. According to an embodiment, the system includes a collection unit, an analysis unit, and a re-learning unit. The collection unit collects data in real time. The analysis unit analyzes the data collected by the collection unit. The re-learning unit re-learns a model based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the process of collecting data in real time and re-training models based on the analysis results is not efficient enough.

[0005] The system according to the embodiment aims to collect data in real time and retrain the model based on the analysis results. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, and a re-learning unit. The collection unit collects data in real time. The analysis unit analyzes the data collected by the collection unit. The re-learning unit re-learns a model based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect data in real time and retrain the model based on the analysis results. [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 data accumulation system according to an embodiment of the present invention is a system capable of accumulating data in real time based on training data. In this data accumulation system, AI builds a model using initial training data, collects new data in real time, analyzes the collected data, adds it to the existing training data, and performs re-training to improve the accuracy of the model. This process is continuous, allowing the system to always provide optimal results based on the latest data. For example, on an e-commerce site, a user's purchase history and browsing history can be collected in real time and analyzed by AI to recommend optimal products to the user. In the medical field, a patient's medical records and test results can be collected in real time and analyzed by AI to recommend optimal treatments. Thus, the present invention can be applied in various fields and can always provide optimal results based on the latest data. First, user behavioral data and health data are collected in real time. Next, AI analyzes the collected data and re-trains the model based on the analysis results. This allows the system to always provide optimal results based on the latest data. For example, on an e-commerce site, a user's purchase history and browsing history can be collected in real time and analyzed by AI to recommend optimal products to the user. In the medical field, AI can collect patient medical records and test results in real time and analyze them to suggest optimal treatments. This allows data storage systems to always provide optimal results based on the most up-to-date data.

[0029] A data accumulation system according to an embodiment includes a collection unit, an analysis unit, and a re-learning unit. The collection unit collects data in real time. For example, the collection unit can collect user behavioral data and health data in real time. The collection unit can also collect user purchase histories and browsing histories in real time on e-commerce sites. The collection unit can also collect patient medical records and test results in real time in the medical field. The analysis unit analyzes the data collected by the collection unit using AI. For example, the analysis unit can improve the accuracy and efficiency of the data by analyzing the collected data using AI. The analysis unit can also analyze the collected purchase histories and browsing histories using AI. The analysis unit can also analyze the collected medical records and test results using AI. The re-learning unit re-learns a model based on the analysis results obtained by the analysis unit. For example, the re-learning unit can improve the accuracy of the system by re-learning the model based on the analysis results. The re-learning unit can recommend optimal products to users based on the analysis results. The re-learning unit can also suggest optimal treatments based on the analysis results. As a result, the data accumulation system according to the embodiment can always provide optimal results based on the latest data.

[0030] The collection unit can collect user behavioral data or health data in real time. Examples of behavioral data include, but are not limited to, location information and click history. For example, the collection unit can collect user location information in real time using GPS. The collection unit can also collect user click history from website logs. Examples of health data include, but are not limited to, heart rate and blood pressure. For example, the collection unit can collect the user's heart rate in real time using a wearable device. The collection unit can also collect the user's blood pressure using a smartphone app. In this way, by collecting the user's behavioral data and health data in real time, analysis and relearning can always be performed based on the latest information.

[0031] The analysis unit can analyze the collected data using AI. Examples of AI include, but are not limited to, machine learning and deep learning. The analysis unit can analyze the collected data using, for example, a machine learning algorithm. The analysis unit can also analyze the data using a deep learning model. Examples of machine learning algorithms include linear regression, logistic regression, and support vector machines. Examples of deep learning models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). As a result, analyzing data using AI improves the accuracy and efficiency of the analysis.

[0032] The re-learning unit can re-learn a model based on the analysis results. Examples of models include, but are not limited to, a prediction model and a classification model. For example, by re-learning a prediction model, the re-learning unit can more accurately predict future data. The re-learning unit can also improve the accuracy of data classification by re-learning a classification model. Examples of prediction models include a time series prediction model and a regression model. Examples of classification models include a decision tree, a random forest, a neural network, etc. In this way, by re-learning a model based on the analysis results, the accuracy of the system is improved.

[0033] The collection unit can collect a user's purchase history or browsing history on an e-commerce site in real time. The purchase history includes, for example, but is not limited to, purchase dates and times, purchased items, etc. The collection unit can, for example, collect a user's purchase dates and times from a database of the e-commerce site in real time. The collection unit can also collect a user's purchased items from a log of the e-commerce site. The browsing history includes, for example, but is not limited to, viewed pages, browsing time, etc. The collection unit can, for example, collect a user's viewed pages from a website log in real time. The collection unit can also collect a user's browsing time from the website log. In this way, by collecting a user's purchase history or browsing history on an e-commerce site in real time, it is possible to recommend optimal products to the user.

[0034] The analysis unit can analyze the collected purchase history and browsing history using AI. The purchase history includes, for example, purchase date and time, purchased items, etc., but is not limited to these examples. For example, the analysis unit can identify purchasing patterns by analyzing the user's purchase date and time using AI. The analysis unit can also identify popular items by analyzing the items purchased by the user using AI. The browsing history includes, for example, viewed pages, viewing time, etc., but is not limited to these examples. For example, the analysis unit can identify categories of interest by analyzing the pages viewed by the user using AI. The analysis unit can also identify items of high interest by analyzing the user's viewing time using AI. In this way, by analyzing the collected purchase history and browsing history using AI, data can be obtained for recommending optimal products to the user.

[0035] The relearning unit can recommend appropriate products to the user based on the analysis results. Appropriate products include, but are not limited to, the user's past purchase history, browsing history, etc. For example, the relearning unit can recommend related products based on the user's past purchase history. The relearning unit can also recommend products of interest to the user based on the user's browsing history. The purchase history includes, for example, the purchase date and time, purchased products, etc. The browsing history includes, for example, viewed pages, browsing time, etc. This improves user satisfaction by recommending optimal products to the user based on the analysis results.

[0036] The collection unit can collect patient medical records or test results in real time in the medical field. Medical records include, for example, but are not limited to, the date and time of medical treatment and the details of medical treatment. For example, the collection unit can collect patient medical dates and times in real time from an electronic medical record system. The collection unit can also collect patient medical treatment details from the electronic medical record system. Test results include, for example, test items and test values, but are not limited to, for example. For example, the collection unit can collect patient test items in real time from a testing system. The collection unit can also collect patient test values ​​from a testing system. In this way, by collecting patient medical records and test results in real time in the medical field, data can be obtained for proposing optimal treatment methods.

[0037] The analysis unit can analyze the collected medical records and test results using AI. Medical records include, for example, but are not limited to, the date and time of medical treatment and the details of medical treatment. For example, the analysis unit can identify medical treatment patterns by analyzing the date and time of a patient's medical treatment using AI. The analysis unit can also evaluate the effectiveness of treatment by analyzing the details of the patient's medical treatment using AI. Test results include, for example, but are not limited to, test items and test values. For example, the analysis unit can identify abnormal values ​​by analyzing the patient's test items using AI. The analysis unit can also evaluate the patient's health condition by analyzing the patient's test values ​​using AI. In this way, by analyzing the collected medical records and test results using AI, data can be obtained to propose the optimal treatment.

[0038] The re-learning unit can propose an appropriate treatment based on the analysis results. Appropriate treatments include, but are not limited to, the patient's medical history, test results, etc. The re-learning unit can propose a relevant treatment based on, for example, the patient's medical history. The re-learning unit can also propose an optimal treatment based on the patient's test results. The medical history includes, for example, past medical records, medical history, etc. Test results include, for example, blood test results, diagnostic imaging results, etc. This improves the therapeutic effect of the patient by proposing an optimal treatment based on the analysis results.

[0039] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit can suggest the optimal data collection method based on, for example, the user's frequent behavior in the past. The collection unit can also analyze the user's past behavioral patterns and set an efficient data collection schedule. Furthermore, the collection unit can select a method for collecting data for a specific time period from the user's past behavioral data. The optimal collection method includes, for example, the type of data and the frequency of collection. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data.

[0040] When collecting data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, if the user is exercising, the collection unit can prioritize collecting health data. Furthermore, if the user is at work, the collection unit can prioritize collecting work-related data. Furthermore, if the user is immersed in a hobby, the collection unit can prioritize collecting data related to the hobby. Activity status includes, for example, the amount of exercise, work content, etc. Areas of interest include, for example, hobbies, topics of interest, etc. In this way, by filtering data based on the user's current activity status and areas of interest, more relevant data can be collected.

[0041] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. Geographical location information includes, for example, GPS data, address information, etc. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0042] When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's interests on social media and collect data related to those interests. Furthermore, the collection unit can collect related data based on information about accounts the user follows on social media. Social media activity includes, for example, the content of posts, the number of likes, etc. This allows related data to be collected efficiently by analyzing the user's social media activity.

[0043] 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 perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. Importance includes, for example, the impact and urgency of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply a behavior analysis algorithm to behavior data. Furthermore, the analysis unit can apply a purchasing behavior analysis algorithm to purchasing data. Categories include, for example, the type of data, purpose, etc. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of the analysis is improved.

[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, prioritize analyzing the most recent data. The analysis unit can also prioritize analyzing the most recent data while referring to past data. Furthermore, the analysis unit can also prioritize analyzing data collected during a specific period. The collection time includes, for example, the freshness of the data and the frequency of collection. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis.

[0046] During analysis, the analysis unit can 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. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. Relevance includes, for example, correlation of data, frequency of co-occurrence, etc. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data.

[0047] During relearning, the relearning unit can select the optimal relearning method by analyzing the user's past behavioral data. The relearning unit can, for example, suggest the optimal relearning method based on the user's past behavioral data. The relearning unit can also analyze the user's past behavioral patterns and set an efficient relearning schedule. Furthermore, the relearning unit can select a method for relearning in a specific time period based on the user's past behavioral data. The optimal relearning method includes, for example, the algorithm to be used, the type of data, etc. In this way, the optimal relearning method can be selected by analyzing the user's past behavioral data.

[0048] The relearning unit can customize the relearning means based on the user's current activity status during relearning. For example, if the user is exercising, the relearning unit can perform effective relearning in a short amount of time. Furthermore, if the user is at work, the relearning unit can perform work-related relearning. Furthermore, if the user is immersed in a hobby, the relearning unit can perform relearning related to that hobby. Activity status includes, for example, the amount of exercise, the type of work, etc. This allows for more effective relearning by customizing the relearning means based on the user's current activity status.

[0049] When relearning, the relearning unit can select the optimal relearning method by taking into account the user's geographical location information. For example, if the user is in a specific area, the relearning unit can select a relearning method related to that area. Furthermore, if the user is traveling, the relearning unit can select a relearning method related to the travel destination. Furthermore, if the user is at home, the relearning unit can select a relearning method based on information about the area around the user's home. Geographical location information includes, for example, GPS data, address information, etc. This allows the optimal relearning method to be selected by taking into account the user's geographical location information.

[0050] During relearning, the relearning unit can analyze the user's social media activity and suggest relearning methods. For example, the relearning unit can suggest related relearning methods based on information shared by the user on social media. The relearning unit can also analyze the user's interests on social media and suggest relearning methods related to those interests. Furthermore, the relearning unit can suggest related relearning methods based on information about accounts the user follows on social media. Social media activity includes, for example, the content of posts, the number of likes, etc. In this way, related relearning methods can be suggested by analyzing the user's social media activity.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the collection unit can suggest the optimal data collection method based on the user's frequent behavior in the past. The collection unit can also analyze the user's past behavioral patterns and set an efficient data collection schedule. Furthermore, the collection unit can select a method for collecting data during a specific time period based on the user's past behavioral data. The optimal collection method includes the type of data and collection frequency. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data.

[0053] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, a detailed analysis can be performed on data of high importance. A simplified analysis can also be performed on data of low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data of medium importance. Importance includes the impact and urgency of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0054] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a health-related analysis algorithm can be applied to health data. A behavioral analysis algorithm can also be applied to behavioral data. Furthermore, a purchasing behavior analysis algorithm can be applied to purchasing data. Categories include the type and purpose of data. This allows different analysis algorithms to be applied depending on the data category, thereby improving the accuracy of the analysis.

[0055] The relearning unit can select the optimal relearning method by analyzing the user's past behavioral data during relearning. For example, the optimal relearning method can be proposed based on the user's past behavioral data. The relearning unit can also analyze the user's past behavioral patterns and set an efficient relearning schedule. Furthermore, the relearning unit can select a method for relearning at a specific time period based on the user's past behavioral data. The optimal relearning method includes the algorithm to be used, the type of data, and so on. In this way, the optimal relearning method can be selected by analyzing the user's past behavioral data.

[0056] The relearning unit can customize the relearning method based on the user's current activity status during relearning. For example, if the user is exercising, the relearning unit can perform effective relearning in a short amount of time. If the user is at work, the relearning unit can perform work-related relearning. Furthermore, if the user is immersed in a hobby, the relearning unit can perform relearning related to that hobby. Activity status includes the amount of exercise, the type of work, and so on. This allows for more effective relearning by customizing the relearning method based on the user's current activity status.

[0057] The relearning unit can select the optimal relearning method by taking into account the user's geographical location information during relearning. For example, if the user is in a specific area, the relearning unit can select a relearning method related to that area. Also, if the user is traveling, the relearning unit can select a relearning method related to the travel destination. Furthermore, if the user is at home, the relearning unit can select a relearning method based on information about the area around the user's home. Geographical location information includes GPS data, address information, and the like. This allows the optimal relearning method to be selected by taking into account the user's geographical location information.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects data in real time. For example, the collection unit can collect user behavioral data, health data, user purchase history, browsing history on an e-commerce site, and patient medical records and test results in the medical field in real time. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. For example, the analysis unit can improve the accuracy and efficiency of the data by analyzing the collected data with AI. Collected purchase history, browsing history, medical records, and test results can also be analyzed with AI. Step 3: The re-learning unit re-learns the model based on the analysis results obtained by the analysis unit. For example, the re-learning unit can improve the accuracy of the system by re-learning the model based on the analysis results. It can also recommend optimal products to users or suggest optimal treatments based on the analysis results.

[0060] (Example 2) A data accumulation system according to an embodiment of the present invention is a system capable of accumulating data in real time based on training data. In this data accumulation system, AI builds a model using initial training data, collects new data in real time, analyzes the collected data, adds it to the existing training data, and performs re-training to improve the accuracy of the model. This process is continuous, allowing the system to always provide optimal results based on the latest data. For example, on an e-commerce site, a user's purchase history and browsing history can be collected in real time and analyzed by AI to recommend optimal products to the user. In the medical field, a patient's medical records and test results can be collected in real time and analyzed by AI to recommend optimal treatments. Thus, the present invention can be applied in various fields and can always provide optimal results based on the latest data. First, user behavioral data and health data are collected in real time. Next, AI analyzes the collected data and re-trains the model based on the analysis results. This allows the system to always provide optimal results based on the latest data. For example, on an e-commerce site, a user's purchase history and browsing history can be collected in real time and analyzed by AI to recommend optimal products to the user. In the medical field, AI can collect patient medical records and test results in real time and analyze them to suggest optimal treatments. This allows data storage systems to always provide optimal results based on the most up-to-date data.

[0061] A data accumulation system according to an embodiment includes a collection unit, an analysis unit, and a re-learning unit. The collection unit collects data in real time. For example, the collection unit can collect user behavioral data and health data in real time. The collection unit can also collect user purchase histories and browsing histories in real time on e-commerce sites. The collection unit can also collect patient medical records and test results in real time in the medical field. The analysis unit analyzes the data collected by the collection unit using AI. For example, the analysis unit can improve the accuracy and efficiency of the data by analyzing the collected data using AI. The analysis unit can also analyze the collected purchase histories and browsing histories using AI. The analysis unit can also analyze the collected medical records and test results using AI. The re-learning unit re-learns a model based on the analysis results obtained by the analysis unit. For example, the re-learning unit can improve the accuracy of the system by re-learning the model based on the analysis results. The re-learning unit can recommend optimal products to users based on the analysis results. The re-learning unit can also suggest optimal treatments based on the analysis results. As a result, the data accumulation system according to the embodiment can always provide optimal results based on the latest data.

[0062] The collection unit can collect user behavioral data or health data in real time. Examples of behavioral data include, but are not limited to, location information and click history. For example, the collection unit can collect user location information in real time using GPS. The collection unit can also collect user click history from website logs. Examples of health data include, but are not limited to, heart rate and blood pressure. For example, the collection unit can collect the user's heart rate in real time using a wearable device. The collection unit can also collect the user's blood pressure using a smartphone app. In this way, by collecting the user's behavioral data and health data in real time, analysis and relearning can always be performed based on the latest information.

[0063] The analysis unit can analyze the collected data using AI. Examples of AI include, but are not limited to, machine learning and deep learning. The analysis unit can analyze the collected data using, for example, a machine learning algorithm. The analysis unit can also analyze the data using a deep learning model. Examples of machine learning algorithms include linear regression, logistic regression, and support vector machines. Examples of deep learning models include convolutional neural networks (CNNs) and recurrent neural networks (RNNs). As a result, analyzing data using AI improves the accuracy and efficiency of the analysis.

[0064] The re-learning unit can re-learn a model based on the analysis results. Examples of models include, but are not limited to, a prediction model and a classification model. For example, by re-learning a prediction model, the re-learning unit can more accurately predict future data. The re-learning unit can also improve the accuracy of data classification by re-learning a classification model. Examples of prediction models include a time series prediction model and a regression model. Examples of classification models include a decision tree, a random forest, a neural network, etc. In this way, by re-learning a model based on the analysis results, the accuracy of the system is improved.

[0065] The collection unit can collect a user's purchase history or browsing history on an e-commerce site in real time. The purchase history includes, for example, but is not limited to, purchase dates and times, purchased items, etc. The collection unit can, for example, collect a user's purchase dates and times from a database of the e-commerce site in real time. The collection unit can also collect a user's purchased items from a log of the e-commerce site. The browsing history includes, for example, but is not limited to, viewed pages, browsing time, etc. The collection unit can, for example, collect a user's viewed pages from a website log in real time. The collection unit can also collect a user's browsing time from the website log. In this way, by collecting a user's purchase history or browsing history on an e-commerce site in real time, it is possible to recommend optimal products to the user.

[0066] The analysis unit can analyze the collected purchase history and browsing history using AI. The purchase history includes, for example, purchase date and time, purchased items, etc., but is not limited to these examples. For example, the analysis unit can identify purchasing patterns by analyzing the user's purchase date and time using AI. The analysis unit can also identify popular items by analyzing the items purchased by the user using AI. The browsing history includes, for example, viewed pages, viewing time, etc., but is not limited to these examples. For example, the analysis unit can identify categories of interest by analyzing the pages viewed by the user using AI. The analysis unit can also identify items of high interest by analyzing the user's viewing time using AI. In this way, by analyzing the collected purchase history and browsing history using AI, data can be obtained for recommending optimal products to the user.

[0067] The relearning unit can recommend appropriate products to the user based on the analysis results. Appropriate products include, but are not limited to, the user's past purchase history, browsing history, etc. For example, the relearning unit can recommend related products based on the user's past purchase history. The relearning unit can also recommend products of interest to the user based on the user's browsing history. The purchase history includes, for example, the purchase date and time, purchased products, etc. The browsing history includes, for example, viewed pages, browsing time, etc. This improves user satisfaction by recommending optimal products to the user based on the analysis results.

[0068] The collection unit can collect patient medical records or test results in real time in the medical field. Medical records include, for example, but are not limited to, the date and time of medical treatment and the details of medical treatment. For example, the collection unit can collect patient medical dates and times in real time from an electronic medical record system. The collection unit can also collect patient medical treatment details from the electronic medical record system. Test results include, for example, test items and test values, but are not limited to, for example. For example, the collection unit can collect patient test items in real time from a testing system. The collection unit can also collect patient test values ​​from a testing system. In this way, by collecting patient medical records and test results in real time in the medical field, data can be obtained for proposing optimal treatment methods.

[0069] The analysis unit can analyze the collected medical records and test results using AI. Medical records include, for example, but are not limited to, the date and time of medical treatment and the details of medical treatment. For example, the analysis unit can identify medical treatment patterns by analyzing the date and time of a patient's medical treatment using AI. The analysis unit can also evaluate the effectiveness of treatment by analyzing the details of the patient's medical treatment using AI. Test results include, for example, but are not limited to, test items and test values. For example, the analysis unit can identify abnormal values ​​by analyzing the patient's test items using AI. The analysis unit can also evaluate the patient's health condition by analyzing the patient's test values ​​using AI. In this way, by analyzing the collected medical records and test results using AI, data can be obtained to propose the optimal treatment.

[0070] The re-learning unit can propose an appropriate treatment based on the analysis results. Appropriate treatments include, but are not limited to, the patient's medical history, test results, etc. The re-learning unit can propose a relevant treatment based on, for example, the patient's medical history. The re-learning unit can also propose an optimal treatment based on the patient's test results. The medical history includes, for example, past medical records, medical history, etc. Test results include, for example, blood test results, diagnostic imaging results, etc. This improves the therapeutic effect of the patient by proposing an optimal treatment based on the analysis results.

[0071] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. As a result, by adjusting the timing of data collection according to the user's emotions, the burden on the user can be reduced and more appropriate data can be collected.

[0072] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit can suggest the optimal data collection method based on, for example, the user's frequent behavior in the past. The collection unit can also analyze the user's past behavioral patterns and set an efficient data collection schedule. Furthermore, the collection unit can select a method for collecting data for a specific time period from the user's past behavioral data. The optimal collection method includes, for example, the type of data and the frequency of collection. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data.

[0073] When collecting data, the collection unit can filter the data based on the user's current activity status and areas of interest. For example, if the user is exercising, the collection unit can prioritize collecting health data. Furthermore, if the user is at work, the collection unit can prioritize collecting work-related data. Furthermore, if the user is immersed in a hobby, the collection unit can prioritize collecting data related to the hobby. Activity status includes, for example, the amount of exercise, work content, etc. Areas of interest include, for example, hobbies, topics of interest, etc. In this way, by filtering data based on the user's current activity status and areas of interest, more relevant data can be collected.

[0074] The collection unit can estimate the user's emotions and determine the priority 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 collecting data related to stress reduction. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting data for maintaining a relaxed state. Furthermore, if the user is excited, the collection unit can also prioritize collecting data related to the cause of the excitement. Emotions are estimated using technologies such as facial expression recognition and voice analysis. In this way, more appropriate data can be collected by determining the priority of data to be collected according to the user's emotions.

[0075] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. Geographical location information includes, for example, GPS data, address information, etc. In this way, highly relevant data can be prioritized by taking into account the user's geographical location information.

[0076] When collecting data, the collection unit can analyze the user's social media activity and collect related data. For example, the collection unit can collect related data based on information shared by the user on social media. The collection unit can also analyze the user's interests on social media and collect data related to those interests. Furthermore, the collection unit can collect related data based on information about accounts the user follows on social media. Social media activity includes, for example, the content of posts, the number of likes, etc. This allows related data to be collected efficiently by analyzing the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. Emotions are estimated using technologies such as facial expression recognition and voice analysis. As a result, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0078] 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 perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on data of medium importance. Importance includes, for example, the impact and urgency of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0079] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit can apply a health-related analysis algorithm to health data. The analysis unit can also apply a behavior analysis algorithm to behavior data. Furthermore, the analysis unit can apply a purchasing behavior analysis algorithm to purchasing data. Categories include, for example, the type of data, purpose, etc. In this way, by applying different analysis algorithms depending on the category of data, the accuracy of the analysis is improved.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. As a result, the length of the analysis can be adjusted according to the user's emotions, thereby providing the optimal analysis result for the user.

[0081] 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, prioritize analyzing the most recent data. The analysis unit can also prioritize analyzing the most recent data while referring to past data. Furthermore, the analysis unit can also prioritize analyzing data collected during a specific period. The collection time includes, for example, the freshness of the data and the frequency of collection. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis.

[0082] During analysis, the analysis unit can 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. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. Relevance includes, for example, correlation of data, frequency of co-occurrence, etc. This allows for efficient analysis by adjusting the order of analysis based on the relevance of data.

[0083] The re-learning unit can estimate the user's emotions and adjust the re-learning method based on the estimated user's emotions. For example, the re-learning unit can perform detailed re-learning when the user is relaxed. Furthermore, the re-learning unit can perform simplified re-learning when the user is in a hurry. Furthermore, the re-learning unit can perform visually stimulating re-learning when the user is excited. Emotions are estimated using techniques such as facial expression recognition and voice analysis. This allows for more effective re-learning by adjusting the re-learning method according to the user's emotions.

[0084] During relearning, the relearning unit can select the optimal relearning method by analyzing the user's past behavioral data. The relearning unit can, for example, suggest the optimal relearning method based on the user's past behavioral data. The relearning unit can also analyze the user's past behavioral patterns and set an efficient relearning schedule. Furthermore, the relearning unit can select a method for relearning in a specific time period based on the user's past behavioral data. The optimal relearning method includes, for example, the algorithm to be used, the type of data, etc. In this way, the optimal relearning method can be selected by analyzing the user's past behavioral data.

[0085] The relearning unit can customize the relearning means based on the user's current activity status during relearning. For example, if the user is exercising, the relearning unit can perform effective relearning in a short amount of time. Furthermore, if the user is at work, the relearning unit can perform work-related relearning. Furthermore, if the user is immersed in a hobby, the relearning unit can perform relearning related to that hobby. Activity status includes, for example, the amount of exercise, the type of work, etc. This allows for more effective relearning by customizing the relearning means based on the user's current activity status.

[0086] The re-learning unit can estimate the user's emotions and determine the priority of relearning based on the estimated user's emotions. For example, if the user is feeling stressed, the re-learning unit can prioritize relearning related to stress reduction. Furthermore, if the user is relaxed, the re-learning unit can prioritize relearning to maintain a relaxed state. Furthermore, if the user is excited, the re-learning unit can prioritize relearning related to the cause of the excitement. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows for more effective relearning by determining the priority of relearning according to the user's emotions.

[0087] When relearning, the relearning unit can select the optimal relearning method by taking into account the user's geographical location information. For example, if the user is in a specific area, the relearning unit can select a relearning method related to that area. Furthermore, if the user is traveling, the relearning unit can select a relearning method related to the travel destination. Furthermore, if the user is at home, the relearning unit can select a relearning method based on information about the area around the user's home. Geographical location information includes, for example, GPS data, address information, etc. This allows the optimal relearning method to be selected by taking into account the user's geographical location information.

[0088] During relearning, the relearning unit can analyze the user's social media activity and suggest relearning methods. For example, the relearning unit can suggest related relearning methods based on information shared by the user on social media. The relearning unit can also analyze the user's interests on social media and suggest relearning methods related to those interests. Furthermore, the relearning unit can suggest related relearning methods based on information about accounts the user follows on social media. Social media activity includes, for example, the content of posts, the number of likes, etc. In this way, related relearning methods can be suggested by analyzing the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and re-learning 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 user behavior data and health data in real time using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using AI. The re-learning unit re-learns a model based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, and re-learning 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 user behavior data and health data in real time using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 using AI. The re-learning unit re-learns a model based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and re-learning 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 user behavior data and health data in real time using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit uses AI to analyze the data collected by the specific processing unit 290 of the data processing device 12. The re-learning unit re-learns a model based on the analysis results by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and re-learning 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 user behavior data and health data in real time using the camera 42 and microphone 238 of the robot 414. The analysis unit uses AI to analyze the data collected by the specific processing unit 290 of the data processing device 12. The re-learning unit re-learns a model based on the analysis results by the specific processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting only important data. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the data collection method to be adjusted according to the user's emotions, reducing the burden on the user and collecting more appropriate data.

[0091] The collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the collection unit can suggest the optimal data collection method based on the user's frequent behavior in the past. The collection unit can also analyze the user's past behavioral patterns and set an efficient data collection schedule. Furthermore, the collection unit can select a method for collecting data during a specific time period based on the user's past behavioral data. The optimal collection method includes the type of data and collection frequency. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data.

[0092] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are concise. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the way the analysis is presented to be adjusted according to the user's emotions, making it possible to provide analysis results that are easy for the user to understand.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, a detailed analysis can be performed on data of high importance. A simplified analysis can also be performed on data of low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data of medium importance. Importance includes the impact and urgency of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0094] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a health-related analysis algorithm can be applied to health data. A behavioral analysis algorithm can also be applied to behavioral data. Furthermore, a purchasing behavior analysis algorithm can be applied to purchasing data. Categories include the type and purpose of data. This allows different analysis algorithms to be applied depending on the data category, thereby improving the accuracy of the analysis.

[0095] The re-learning unit can estimate the user's emotions and adjust the re-learning method based on the estimated emotions. For example, if the user is relaxed, the re-learning unit can perform detailed re-learning. If the user is in a hurry, the re-learning unit can perform simplified re-learning. Furthermore, if the user is excited, the re-learning unit can perform visually stimulating re-learning. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows the re-learning method to be adjusted according to the user's emotions, enabling more effective re-learning.

[0096] The relearning unit can select the optimal relearning method by analyzing the user's past behavioral data during relearning. For example, the optimal relearning method can be proposed based on the user's past behavioral data. The relearning unit can also analyze the user's past behavioral patterns and set an efficient relearning schedule. Furthermore, the relearning unit can select a method for relearning at a specific time period based on the user's past behavioral data. The optimal relearning method includes the algorithm to be used, the type of data, and so on. In this way, the optimal relearning method can be selected by analyzing the user's past behavioral data.

[0097] The relearning unit can customize the relearning method based on the user's current activity status during relearning. For example, if the user is exercising, the relearning unit can perform effective relearning in a short amount of time. If the user is at work, the relearning unit can perform work-related relearning. Furthermore, if the user is immersed in a hobby, the relearning unit can perform relearning related to that hobby. Activity status includes the amount of exercise, the type of work, and so on. This allows for more effective relearning by customizing the relearning method based on the user's current activity status.

[0098] The re-learning unit can estimate the user's emotions and determine the priority of re-learning based on the estimated emotions. For example, if the user is feeling stressed, the re-learning unit can prioritize re-learning related to stress reduction. Also, if the user is relaxed, the re-learning unit can prioritize re-learning to maintain a relaxed state. Furthermore, if the user is excited, the re-learning unit can prioritize re-learning related to the cause of the excitement. Emotions are estimated using technologies such as facial expression recognition and voice analysis. This allows for more effective re-learning by determining the priority of re-learning based on the user's emotions.

[0099] The relearning unit can select the optimal relearning method by taking into account the user's geographical location information during relearning. For example, if the user is in a specific area, the relearning unit can select a relearning method related to that area. Also, if the user is traveling, the relearning unit can select a relearning method related to the travel destination. Furthermore, if the user is at home, the relearning unit can select a relearning method based on information about the area around the user's home. Geographical location information includes GPS data, address information, and the like. This allows the optimal relearning method to be selected by taking into account the user's geographical location information.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The collection unit collects data in real time. For example, the collection unit can collect user behavioral data, health data, user purchase history, browsing history on an e-commerce site, and patient medical records and test results in the medical field in real time. Step 2: The analysis unit uses AI to analyze the data collected by the collection unit. For example, the analysis unit can improve the accuracy and efficiency of the data by analyzing the collected data with AI. Collected purchase history, browsing history, medical records, and test results can also be analyzed with AI. Step 3: The re-learning unit re-learns the model based on the analysis results obtained by the analysis unit. For example, the re-learning unit can improve the accuracy of the system by re-learning the model based on the analysis results. It can also recommend optimal products to users or suggest optimal treatments based on the analysis results.

[0102] 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.

[0103] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

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

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The 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.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] 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.

[0146] 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.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the 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.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

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

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 data in real time; an analysis unit that analyzes the data collected by the collection unit; a re-learning unit that re-learns a model based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collecting user behavioral or health data in real time 2. The system of claim 1.

3. The analysis unit Analyze the collected data using AI 2. The system of claim 1.

4. The relearning unit Retraining the model based on the analysis results 2. The system of claim 1.

5. The collecting unit Collecting user purchase or browsing history in real time on e-commerce sites 2. The system of claim 1.

6. The analysis unit Analyze collected purchase and browsing history using AI 2. The system of claim 1.

7. The relearning unit Recommend appropriate products to users based on analysis results 2. The system of claim 1.

8. The collecting unit In the medical field, collecting patient medical records or test results in real time 2. The system of claim 1.

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